WO2024097906A1 - Système de flux - Google Patents

Système de flux Download PDF

Info

Publication number
WO2024097906A1
WO2024097906A1 PCT/US2023/078558 US2023078558W WO2024097906A1 WO 2024097906 A1 WO2024097906 A1 WO 2024097906A1 US 2023078558 W US2023078558 W US 2023078558W WO 2024097906 A1 WO2024097906 A1 WO 2024097906A1
Authority
WO
WIPO (PCT)
Prior art keywords
semantic
endpoint
augmentation
activity interest
capability
Prior art date
Application number
PCT/US2023/078558
Other languages
English (en)
Inventor
Lucian Cristache
Original Assignee
Lucomm Technologies, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US17/980,913 external-priority patent/US20230079238A1/en
Priority claimed from US17/982,922 external-priority patent/US11731273B2/en
Application filed by Lucomm Technologies, Inc. filed Critical Lucomm Technologies, Inc.
Publication of WO2024097906A1 publication Critical patent/WO2024097906A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/166—Editing, e.g. inserting or deleting
    • G06F40/186—Templates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/40—Robotics, robotics mapping to robotics vision
    • G05B2219/40298—Manipulator on vehicle, wheels, mobile
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition

Definitions

  • FLUX SENSING SYSTEM I NVENTOR Lucian Cristache F IELD OF THE I NVENTION This invention relates generally to flux sensing systems, including such systems for publishing capabilities and associated activities of stored semantic fluxes and matching them with inferred activities of interest.
  • BACKGROUND OF THE INVENTION There are many cases in which physical devices are used in a variety of settings involving groups of people and/or objects, such as in the formation of posts and lines to demark crowd control areas or permitted pathways for movement. These provide regions which may be fluid, and tend to require manpower to continually reconfigure them.
  • the posts themselves provide opportunities for gathering/inferring/presenting/rendering/conveying information which may be optical, visual, or otherwise.
  • a preferred robotic semantic system may include one or more smart posts each having a base (which may optionally include a plurality of wheels or casters in the case of a - 1 - LUCM-1-1055Spec mobile smart post), a power section, a trunk section, a structure fixation and manipulation portion, a control section, a clipping area, a portion supporting one or more antennas, and an optical sensor portion.
  • Other modules may be incorporated with such smart posts including a copter module (e.g. for aerial transportation) and a display module (e.g. for providing semantic augmentation).
  • the smart post includes all or a subset of the components listed above in a manner in which they are integrated into a generally unified structure, such as a single pole or post having a hollow center and in which the listed components are attached or inserted into the post.
  • the components described above are generally assembled separately, such that they are produced as modules which are joined together to form the post.
  • each of the above sections or regions or portions may be separately formed modules which are joined together, or may be separate portions of a unitary post or similar structure.
  • a module is a portion or section of the smart post, and not necessarily a discrete module.
  • the post may use any number of modules of any type.
  • a post may comprise multiple power modules and/or multiple antenna elements modules and/or multiple cameras modules.
  • One example of the invention includes a semantic robotic system comprising a plurality of communicatively coupled devices which use a plurality of semantic routes and rules and variable semantic coherent inferences based on such routes and rules to allow the devices to perform semantic augmentation.
  • the devices comprise semantic posts.
  • the devices comprise autonomous robotic carriers.
  • the devices comprise semantic composable modules.
  • the devices comprise semantic units.
  • the semantic system includes a semantic gate.
  • the semantic system comprises a semantic cyber unit.
  • the semantic posts implement crowd control.
  • the semantic posts implement guiding lanes.
  • the semantic units perform signal conditioning. - 2 - LUCM-1-1055Spec [0015]
  • the signal conditioning is based on semantic wave conditioning, preferably based on semantic gating.
  • the system performs video processing. [0017] In some examples of the invention, the system performs semantic augmentation on video artifacts. [0018] In preferred versions, the system may form semantic groups of posts and physically connect them through physical movement of the semantic posts motor components. [0019] Preferably, the system uses concern factors in order to determine coherent inferences. [0020] In some examples, the system forms a semantic group based on semantic resonance. [0021] Preferably, the system invalidates a semantic group based on semantic decoherence. [0022] In some examples, the system performs semantic learning based on the inference of semantic resonance. [0023] In some versions, the system performs semantic learning based on the inference of semantic decoherence.
  • the system learns semantic rules based on semantic resonance. [0025] In preferred versions, the system learns damping factor rules. Preferably, the system learns semantic gating rules. [0026] In some examples, the system learns a hysteresis factor based on semantic analysis. [0027] In preferred versions, the system performs semantic augmentation using a variety of augmentation modalities. [0028] In some examples, the system performs semantic augmentation comprising semantic displaying. Preferably, the system performs semantic augmentation on particular devices based on ad-hoc semantic coupling. [0029] In some examples, the system performs semantic augmentation based on challenges and/or inputs. [0030] In some examples, the system performs semantic encryption.
  • the system performs semantic gating based on semantic inferences related to at least one video frame.
  • the system uses semantic groups to form composite carriers. - 3 - LUCM-1-1055Spec
  • the devices comprise semantic meshes.
  • the devices comprise biological sensors.
  • the biological sensors comprise at least one medical imaging sensor. BRIEF DESCRIPTION OF THE DRAWINGS [0035] Preferred and alternative examples of the present invention are described in detail below with reference to the following drawings: [0036] Fig.1 is a front perspective view of a preferred smart post.
  • Fig.2A is a front perspective view of a preferred optical module with dome for a preferred smart post.
  • Fig.2B is a front perspective view of an alternate optical module for a preferred smart post.
  • Figure 3 is a front perspective view of a preferred module with multi-array antenna elements for a preferred smart post.
  • Fig.4 is a front perspective view of a preferred clipping module for a preferred smart post.
  • Fig. 5A is a front perspective view of an alternate clipping module for a preferred smart post.
  • Fig. 5B is a front perspective view of another alternate clipping module for a preferred smart post.
  • Fig. 5A is a front perspective view of an alternate clipping module for a preferred smart post.
  • FIG. 5C is a front perspective view of another alternate clipping module for a preferred smart post.
  • Fig.6A is a bottom plan view of a preferred standing and moving base.
  • Fig. 6B is a bottom plan view of an alternate preferred standing and moving base.
  • Fig. 6C is a bottom plan view of another alternate preferred standing and moving base.
  • Fig.7 is a front perspective view of a preferred module having a central post.
  • Fig.8A shows a representative view of a plurality of posts arranged in a guiding configuration, shown in a retracted position.
  • Fig. 8B shows a representative view of the posts of Fig. 8A, shown partially extended to form a guiding arrangement.
  • Fig. 8C shows a representative view of the posts of Fig. 8A, shown fully extended in one of many possible guiding arrangements. - 4 - LUCM-1-1055Spec
  • Fig.9 shows a plurality of posts in a perimeter delimitation configuration.
  • Fig. 10A illustrates a plurality of posts in communication wirelessly with a remote control infrastructure.
  • Fig. 10B illustrates a plurality of posts in wireless communication with one another.
  • Fig. 11 illustrates an example of a configuration of a plurality of smart posts forming a configuration of smart carriers.
  • Fig.12 illustrates an alternate example of a configuration of a plurality of smart posts forming a configuration of smart carriers.
  • Fig.13 illustrates a plurality of smart posts, such as those in Figs.11 or 12, but in which the telescopic capabilities of the posts define enclosed areas within a pair of composed post structures.
  • Fig. 14 shows nine posts arranged in a 3x3 configuration forming a combined sensing and/or processing capability.
  • Fig.15 is a representative view illustrating a combination of modules A through n which may combine to form a smart post.
  • Fig.16 illustrates pluralities of smart posts or similar elements shown connected via semantic fluxes.
  • Fig. 17 illustrates a representative map of locations and intersections of the trajectories of actual and semantic movement between nodes.
  • Fig.18 illustrates an alternate representative map of locations and intersections of the trajectories of actual and semantic movement between nodes.
  • Fig. 19A illustrates a preferred circuit diagram for conditioning a received signal based on a modulated semantic wave signal.
  • Fig.19B illustrates a preferred circuit diagram for conditioning a received signal based on a modulated semantic wave signal.
  • Fig.19C illustrates a preferred circuit diagram for conditioning a received signal based on a modulated semantic wave signal.
  • Fig.20 illustrates a block diagram of a plurality of elements (e.g. semantic units) coupled through a plurality of links/semantic fluxes.
  • Fig.21 illustrates a block diagram of a plurality of semantic units joined through a multiplexer as a semantic group.
  • Fig.22 illustrates a block diagram of a plurality of semantic cells joined through a multiplexer as a semantic group of semantic cells. - 5 - LUCM-1-1055Spec
  • Fig.23 illustrates a multi-stage block diagram for processing of a collection of semantic cells.
  • Fig. 24A illustrates a block diagram of a preferred system for implementing a mathematical (co)processor to process the mathematical functions embedded in the formulas defining semantic rules.
  • Fig. 24A illustrates a block diagram of a preferred system for implementing a mathematical (co)processor to process the mathematical functions embedded in the formulas defining semantic rules.
  • FIG. 24B illustrates an alternate block diagram of a preferred system for implementing a mathematical (co)processor to process the mathematical functions embedded in the formulas defining semantic rules.
  • Fig. 24C illustrates an alternate block diagram of a preferred system for implementing a mathematical (co)processor to process the mathematical functions embedded in the formulas defining semantic rules.
  • Fig. 24D illustrates an alternate block diagram of a preferred system for implementing a mathematical (co)processor to process the mathematical functions embedded in the formulas defining semantic rules.
  • Fig.25 is a block diagram of a semantic system including a plurality of robotic devices and an insurance provider.
  • Fig.25 is a block diagram of a semantic system including a plurality of robotic devices and an insurance provider.
  • FIG. 26A is an illustration of an observer directing attention to a first endpoint within a semantic field of view.
  • Fig.26B is an illustration of an observer directing attention to a second endpoint within a semantic field of view.
  • Fig.27 is an illustration of a field of view mapped to a display surface.
  • Fig.28 is an illustration of a field of view mapped to an alternate display surface.
  • Fig.29 is an illustration of a field of view mapped to an alternate display surface.
  • Fig.30 is an illustration of a field of view mapped to an alternate display surface.
  • Fig.31 is a representative view of a plurality of fairings.
  • Fig.32 is a perspective view of a preferred robotic pallet.
  • Fig.33 is a perspective view of an alternate robotic pallet.
  • Fig.34 is a perspective view of a robotic pallet including arms in an unloading or loading process.
  • Fig.35 is a perspective view of an alternate robotic pallet including arms in an unloading or loading process.
  • Fig. 36 is a side elevational view of a robotic pallet in a loading or unloading process.
  • Fig.37A an elevational view of a preferred robotic pallet.
  • - 6 - LUCM-1-1055Spec [0087]
  • Fig.37B an elevational view of a preferred robotic pallet.
  • Fig.37A an elevational view of a preferred robotic pallet.
  • Fig. 38A is an alternate view of a pair of semantic posts for a robotic post system.
  • Fig. 38B is an alternate view of a pair of semantic posts for a robotic post system.
  • Fig. 38C is an alternate view of a pair of semantic posts for a robotic post system.
  • Fig.39A is a close-up view of an upper portion of a semantic post.
  • Fig. 39B is a close-up view of an alternate upper portion of a semantic post, incorporating a hook.
  • Fig. 39C is an exemplary view of a first semantic post and a second semantic post in the process of connecting a hook of a lockable band.
  • Fig.39D is a block diagram of a preferred semantic post.
  • Fig.40A is a front elevational view of a preferred robotic shopping cart.
  • Fig.40B is a front elevational view of an alternate robotic shopping cart.
  • Fig.40C is a front elevational view of another alternate robotic shopping cart
  • Fig.41A is an exemplary close-up view of an upper portion of a semantic post in position to connect with a piece of luggage.
  • Fig. 41B is an exemplary view of a semantic post with an arm connected to a piece of luggage.
  • Fig.42 is a representative view of a plurality of posts forming a composable gate.
  • Fig.43 is a close-up view of a preferred lockable hook.
  • Fig. 44A is a preferred representation of a robotic gate and panel implementation.
  • Fig.44B is an alternate preferred representation of a robotic gate and panel implementation.
  • Fig.45A is a sequencing and connectivity diagram between a mobile device and a holder/cart.
  • Fig.45B is a further sequencing and connectivity diagram between a mobile device and a holder/cart, including a provider.
  • Fig.45C is a block diagram of a preferred system including a mobile device, provider, and holder/cart.
  • Fig.46A is a block diagram of a preferred account access control system.
  • Fig. 46B is a block diagram of a preferred cloud computing system for use with the preferred account access control system.
  • Fig.47A is a front elevational view of a pair of posts with lockable bands.
  • Fig. 47B is a close-up view of an upper portion of a post with a lockable band.
  • Fig.47C is an illustration of a preferred band holder for a post with lockable band.
  • Fig.47D illustrates a preferred spinner mechanism for a band holder.
  • Fig.47E illustrates a spinner mechanism including a spring.
  • Fig.47F illustrates a spinner mechanism including a plurality of blades.
  • Fig.47G illustrates a preferred lock for a lockable band.
  • Fig.47H illustrates an alternate preferred lock for a lockable band.
  • Fig.47I is an illustration of an alternate preferred band holder for a post with lockable band.
  • Fig. 48 is a representative illustration of a wireless module embedded in a door lock to harvest and/or provide energy to actuate electromagnets or identify/authenticate a user.
  • Fig. 49A is a preferred example of a door cylinder having a spinner/lock attached or linked to a bolt.
  • Fig. 49A is a preferred example of a door cylinder having a spinner/lock attached or linked to a bolt.
  • FIG. 49B is an alternate example of a door cylinder having a spinner/lock attached or linked to a bolt.
  • Fig. 49C is another alternate example of a door cylinder having a spinner/lock attached or linked to a bolt.
  • Fig. 49D is another alternate example of a door cylinder having a spinner/lock attached or linked to a bolt.
  • Fig. 49E is another alternate example of a door cylinder having a spinner/lock attached or linked to a bolt.
  • Fig. 50 is a representative illustration of an enclosure having a spinner attached to a knob and bolt, with another spinner attached to a handle and bolt.
  • Fig. 50 is a representative illustration of an enclosure having a spinner attached to a knob and bolt, with another spinner attached to a handle and bolt.
  • Fig. 51A is a perspective view of a linearly moveable bolt in a retracted position.
  • Fig.51B is a perspective view of a pivoting or swinging bolt in an extended position.
  • - 8 - LUCM-1-1055Spec [00127]
  • Fig. 51C is representative illustration of an axle/spinner supported by an exterior shell of a lock and/or faceplates.
  • Fig.52 is a plan view of a preferred stopper.
  • Fig.53A is a view of a preferred pin-lockable actuator.
  • Fig.53B is a view of an alternate pin-lockable actuator.
  • Fig.54A is a front elevational view of a preferred door having a lock and a camera.
  • Fig.54B is a front elevational view of a preferred door having wheels.
  • Fig. 54C is a front elevational view of a preferred door being secured by a lock security module attached to a post.
  • DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT [00134] The present invention relates to versatile smart sensing robotic posts, appliances and systems. Such systems can be used in various environments including airports, hospitals, transportation, infrastructure works, automotive, sport venues, intelligent homes and any other circumstances.
  • the posts serve as stanchions and include clips or connectors for belts or ropes which may optionally be retractable within one or more of the posts.
  • the smart posts may be used as barricades or crowd control in areas where it is desired to restrict or organize access to certain areas by a population.
  • the smart posts may be used as appliances and smart infrastructure for applications such as robotics, wireless communications, security, transportation systems, scouting, patrolling etc.
  • the system may perform semantic augmentation, wherein the system uses semantic analysis for inferring/presenting/rendering/conveying/gathering information in optimal ways and/or using particular modalities based on circumstances, challenges, users and/or profiles.
  • the smart posts are used for semantic augmentation via incorporated displays, speakers, actuation and other I/O mechanisms.
  • a display is mounted on the post and/or top of the post.
  • the smart posts may comprise smart pop-up signs which allow traffic control (e.g. REDUCED SPEED, CONTROLLED SPEED etc.).
  • the posts may comprise other semantic augmentation capabilities and/or outputs. It is to be understood that the signs/posts may register their capability semantics on the semantic system and the system controls them based on semantic augmentation and/or - 9 - LUCM-1-1055Spec analysis including semantic time management (e.g.
  • the preferred smart posts may move independently or may be installed on moving vehicles and any other moving structures; alternatively, or in addition they may be installed on fixed structures such as walls, floors, and so on for sensing and control purposes.
  • a preferred post has sensing elements including at least a vision element such as a camera, and an array of antenna elements receiving and/or radiating electromagnetic radiation.
  • the electromagnetic radiation may use various frequency spectrums including but not limited to low frequency, ultra-high frequency, microwave, terahertz, optical and so on.
  • the camera and/or vision element may operate in visual, infrared and any other optical spectrum. It is to be understood that sensing elements may provide time of flight (TOF) capabilities.
  • the smart robotic posts may include other sensing modalities (e.g. microphones) and/or any other analog and/or digital sensors and transducers used for other environmental measurements and detections (e.g. pressure, sound, temperature, motion, acceleration, orientation, velocity etc.). It is to be understood that such elements may be disposed in an arrangement about the smart post to enable detection of environmental conditions or parameters in geographic areas or zones about the post.
  • the system may use environment profiling and learning based on corroborating radiofrequency energy returns with optical (e.g.
  • the system determines artifacts through camera frame sensing and/or inference operating in optical spectrum and groups them with artifacts sensed and/or inferred through antennas operating in the microwave spectrum.
  • the system may be very particular on conditions and inferences that resemble learning groups and patterns.
  • a preferred smart post 101 comprises a base 1 (which may optionally include a plurality of wheels or casters 10 in the case of a mobile smart post), a power section 2, a trunk section 3, a structure fixation and manipulation portion 4, a control section 5, a clipping area 6, a portion supporting one or more antennas 7, and an optical sensor portion 8. While the illustrated embodiment shows a hexagonal design (as viewed in a horizontal cross section taken through a vertical axis, in which the vertical axis extends - 10 - LUCM-1-1055Spec centrally from the base to the optical sensor portion) it is to be understood that it can be shaped differently (squared, pentagonal, octagonal, circular etc. in other versions.
  • the smart post includes all or a subset of the components listed above and illustrated in Figure 1 in a manner in which they are integrated into a generally unified structure, such as a single pole or post having a hollow center and in which the listed components are attached or inserted into the post.
  • the components described above are generally assembled separately, such that they are produced as modules which are joined together to form the post.
  • each of the above sections or regions or portions may be separately formed modules which are joined together, or may be separate portions of a unitary post or similar structure.
  • the post may use any number of modules of any type.
  • a post may comprise multiple power modules and/or multiple antenna elements modules and/or multiple cameras modules.
  • the base 1 may comprise wheels 10 and its movement be controlled via motors, actuators and other control components or interfaces by a computer (or the equivalent, such as a processor having a memory and programming instructions) embedded in the robotic post.
  • the standing base may comprise suspension (e.g.
  • Figs.6A-C illustrate bottom plan views of the standing and moving base 1 in various embodiments comprising attaching mechanisms 20 and/or driving wheels 21.
  • the (driving) wheel or wheels may mount on attaching mechanisms and/or be retractable, tension- able and/or spring-able (e.g. for using, holding and releasing energy for achieving particular compressions, extensions and/or motions); in an example, the post may use any three wheels, each on any non-adjoining edge/segment of the hexagonal shaped base while the other wheels may be inactivated and/or retracted.
  • the driving wheels may function on similar principles (e.g. activate particular ones based on (semantic) circumstances and/or semantic groups).
  • the mounts (wheel mounts, ball type mounts, module connecting mounts, band connecting mounts etc.) may be controlled (e.g. by compression, extension etc.) by - 11 - LUCM-1-1055Spec semantic actuation based on observed circumstances.
  • some mounts’ compression is stiffened and others loosened when the system uses, observes and/or infers a trajectory which would determine an 80 HARD LEFT LEAN semantic; further, the 80 HARD LEFT LEAN may use further routes such as WHEEL MOUNT GROUP LEFT 75 COMPRESSION, WHEEL MOUNT GROUP RIGHT 25 COMPRESSION.
  • at least two post rectangular bases comprise each four wheels in a rectangular pattern one for each edge; when joined on one of the lateral edge faces the base allows a combined support and thus the center of gravity moves towards the joining edge face.
  • the combined post may use any inferred particular group from the combined base (e.g.
  • Each module may comprise a computer or controller, memory or other computing units. While illustrated as separate modules, in other versions one or more physical modules and/or their functionality may fuse or be distributed among fused modules.
  • the standing base and moving module 1 may be fitted with a power supply such as one or more Li-Ion batteries, and therefore may serve as a single consolidated base and power supply module rather than two separate modules.
  • the power, control and antenna elements are combined in a single module rather than separate modules joined together.
  • the trunk and antenna panels extend to the whole surface of the post.
  • the power module may comprise batteries (e.g.
  • the electrical storage components may be charged via physical plug-in, wireless or any other charging technique.
  • multiple modules whether physical or logical may fuse into a larger trunk module.
  • such fused trunk module is telescopic and extensible, facilitating dynamic reconfiguration settings.
  • the standing base module and the trunk module are telescopic thus allowing height adjustment.
  • the telescopic movement may be controlled through electric motors powered through the power module and controlled by the control module.
  • the modules may be carried on a supporting post or frame, which may be configured as a central post defining a central vertical axis for the smart post.
  • the modules may be attached to the post 9, as shown in Fig. 7, through a variety of mechanism with the preferred version being that the post comprises a frame on which modules - 12 - LUCM-1-1055Spec slide, attach and lock/unlock (e.g. fig 7 middle column 9).
  • the supporting post or frame comprises backplanes, connectors and/or communication buses; when slide into place the modules connect (e.g. via connectors) to the backplane, connection and/or communication bus, thus allowing flexible module interconnects (e.g. Fig 15, showing a plurality of modules which includes Module A, Module B, and continuing through Module n).
  • the modules comprise interlocking and interconnect features such as tongues and grooves, pegs and cavities, tabs and slots and/or other interconnect systems that allow the modules to lock to each other while being stacked.
  • Interconnect mechanisms allow the modules to be in signal communication via a composable bus formed by interconnecting buses of each module. It is to be understood that the buses may comprise electrical and/or optical components.
  • a collection of any types of modules may also communicate wirelessly via transmit/receive components, antennas and/or panels embedded in each module. In some embodiments the communication between modules take place in the same post and/or other posts.
  • the modules may be in signal communication and communicably coupled for various purposes including for transmit/receives command signals via buses, providing status information (e.g. battery charging status), semantic augmentation (e.g. airline name, flight information, routing information etc.) and so forth. Post to post communication may also occur in such situations and further when the system infers, groups and/or deploy posts and units in particular configurations and/or missions.
  • the control module provides commands to actuators incorporated in the base module for guiding the posts through environment.
  • control module may infer semantic routes such as GO TO LOCATION A and further TURN LEFT UNTIL ON THE DIRECTION OF LOCATION A and further when detecting a curb MODERATELY ACCELERATE TO CURB AND JUMP.
  • the system may further infer from JUMP and HIGH CURB to LOAD SPRING 1 HIGH (e.g. commanding driveline suspension spring 1 to load high tension via electrical motor actuation) and RELEASE SPRING 10 (e.g. high energy release) once HIGH CURB CLOSE.
  • the control units command actuation based on such commands (e.g. commands electrical motors of the base module driveline, controls voltages, currents and/or electromagnetic fluxes/properties in time of such components etc.).
  • post units and/or groups may require inter post communication and command whether master-master and/or master-slave.
  • the carriers command semantic groups of posts and/or modules in order to achieve particular movements.
  • a composite 3X3 carrier may need to climb a stair and as such it may command rows of posts independently at particular times for achieving the goals.
  • the system elevates at least the first row of posts from the ground once in proximity of a stair and further moves forward and elevates further rows in order to climb the stairs while always maintaining the load initial posture (e.g. horizontal agnostic).
  • the robotic system may be considered as formed from a number of rows and columns rows and columns and groups thereof.
  • the front upper row of modules moves upward (e.g. via telescopic means) and slide forward and rests at a first time on at least the second stair up from the current position.
  • the lower level horizontal rows move in position forward on the subsequent stairs under the upper row position’s stairs and generate telescopic lift for the upper level horizontal rows that will detach from the upper stair/s, slide up and forward to attach to higher upper stairs and generate support for the ensemble allowing the lower level rows to detach from the supporting position and slide up and forward to upper stairs.
  • stairs ascent is based on row movement such as slide up and forward
  • the movement is telescopic and/or retractable to elevate the horizontal rows.
  • stair descent is based on moving the vertical columns in a slide forward and down movement while the horizontal rows use a telescopic and/or retractable movement to slide forward the vertical columns.
  • the carrier may turn over on one side (e.g. such a vertical row become horizontal and vice-versa) and/or reconfigure its layout for the particular mission (e.g. ASCENT, DESCENT etc.).
  • rows and/or columns may be used interchangeably with “semantic group of rows” and/or “semantic group of columns” and further, in a hierarchical manner, of semantic groups.
  • the selection of rows and/or columns of sliding, telescoping, retracting and/or lifting elements may be based on semantic group inferencing which may also take in consideration the lift weight and height (e.g. weight of carrier and load, height of load, height of telescoping areas, height of stairs etc.).Other factors such as surface traction grip, environment conditions and other factors may also come into effect.
  • the semantic posts may use group leverage to achieve goals such as changing positions, lifting, jumping, getting straight and/or out of the ground.
  • at least one post is sideways on the ground (maybe because it was pushed to the ground by external factors) and other posts are used to lift the fallen post and move it back to vertical position.
  • at least two posts have fallen, and they leverage each other to lift to vertical position based on side by side maneuvering, latching, hooking, lifting, pushing and/or pulling.
  • the post deployments based on semantic routes may be based on the semantics associated with various locations and/or other information.
  • the system detects that the area of GATE A having a scheduled DREAMLINE AIRLINE flight is DELAYED or boards later and hence smart posts at the gate may be re-deployed to other locations and areas based for example on a reward-based system.
  • the posts are deployed to locations associated with semantics having high rewards and incentives while pondering the total rewards (e.g. via opposite sign weights and/or rewards) with the accessibility, deployment and routing semantics in the semantic network model.
  • the system infers a goal of redeploying the posts to a HAZARDOUS area (e.g.
  • semantic inference allows goals, rewards and/or semantic routes to be adjusted and/or selected based on further semantic routes, goals and/or rewards (e.g. MINIMIZE COST AND RISK, MOVE FAST, MAXIMIZE POWER CHARGING etc.).
  • semantic routes and/or goals may be hierarchical and compositional with higher-level abstraction semantic routes and/or goals comprising lower-level abstraction semantic routes and/or goals in a hierarchical and/or compositional fashion.
  • Such hierarchy may be determined and/or mapped to hierarchies and topologies in hierarchical semantic network models thus allowing the semantic inference to pursue selectively (e.g. based on higher level endpoints comprising a lower level sub-model comprising a selection of endpoints and/or links) and hierarchically from lower to higher and higher to lower abstraction (e.g. endpoint) levels.
  • the system may perform semantic factorization wherein a quantifiable (semantic) factor/indicator associated with a semantic artifact is adjusted based on semantic inference/analysis. It is understood that when referring to “factorization” in this disclosure it may refer to “semantic factorization”. Semantic factorization techniques may be used such as explained in this application (e.g. based on semantic time management, decaying, indexing, resonance, (entanglement) entropy, divergence, damping etc.). [00165] Semantic factorization may entail semantic decaying.
  • Semantic decaying occurs when a quantifiable factor/indicator associated with a semantic artifact decays or varies in time, most of the time tending to 0; as such, if the parameter is negative decaying is associated with increases in the semantic factor value and if the factor is positive decaying is associated with decreases in factor’s value.
  • the semantic may be inactivated, invalidated or disposed and not considered for being assigned to an artifact, semantic route, goal, semantic rule, semantic model and/or inference; further, based on the same principles the semantic is used in semantic group inference and membership.
  • Semantic factors may be associated with values of control voltages and currents in analog and/or digital components and blocks. Analogously, other material and further emission, dispersive, diffusive and/or quantum properties may be controlled (e.g. electromagnetic flux, conductivity, photon/photoelectron emission, polarization, etc.). [00168] Decaying and semantic factors may be inferred and learned with semantic analysis. In some examples the system learns decaying and semantic factors for semantic rules and/or semantic routes. [00169]
  • the clipping module 6 (see Fig. 4) comprises bands and clips that can be used to hook up or pair two posts, such as by the attachment of opposite ends of a band, rope or belt to two separate posts. Each clip module has at least one band (see Fig.
  • band 4 showing one end of a band having a clip 25 attached, in which the band is retracted within the module) such that the attached clip or hook that can be used to clip together at least two posts by joining to a band clip insert or attachment point 26 on another post.
  • the bands can therefore be extended to form a perimeter by moving and guiding the posts to the desired location. Once coupled or hooked the posts may move, thus extending the clipped bands and creating various configurations, potentially delimitating semantic zones (e.g. traveler or automotive guiding lanes, hazards emergency lanes, parking areas/lanes/space, work zones etc.).
  • the posts system may be performing the access control and/or zoning function via physical movement and/or sensing means (e.g. laser, vision, radiofrequency and/or other modalities).
  • physical movement and/or sensing means e.g. laser, vision, radiofrequency and/or other modalities.
  • the posts may move towards each other in order to detach the band clips at a closer distance in order to avoid band dangling.
  • the posts detach while at farther distances and the band rolls attenuate the retraction movement through amortization or controlled retraction (e.g.
  • the semantic posts may perform clipping/unclipping, unfolding/folding of the bands, barricades and/or nets once they are commanded to allow/deny/control access.
  • the posts may not move to each other in order to perform clipping but rather perform the shooting of drive threads, ropes and/or cables towards each other that may hook once colliding in the air (e.g. male-female type of hooking, where one thread is a male connector and the other thread is a female connector). Once disconnecting such threads, ropes and/or cables may have mechanisms to manipulate the end hooks and latches.
  • Figs.5A-C show further exemplary preferred embodiments for coupling mechanisms to affix belts or bands from one post to another post.
  • the coupling mechanism between two clips or hooks may comprise a sliding mechanism 31, insertion lock mechanism 32, hook lock mechanism 33, turning mechanism, plug and lock mechanism, latching an any other techniques.
  • the sliding mechanism comprises hooks, clips or grooves that slide into each other via horizontal or vertical movement.
  • the plug and lock mechanism may comprise plugs that lock into each other once connected.
  • the latching mechanism latches the hooks once connected. It is to be understood that any of these techniques use mechanical and/or electrical means for such clippings and latches and can be combined in any configuration.
  • the semantic posts may comprise a (foldable) barrier mechanisms and/or modules.
  • the barrier mechanism/module may comprise/control multiple barrier segments (e.g. from plastic, metal, fabric and/or any other material) which can be folded and/or extended thus forming shorter or longer barriers used to adapt to (semantic) access control needs (e.g. entry points, controlled areas/endpoints etc.).
  • Such barriers may comprise segments with grooves which slide, extend and/or retract within each other with the sliding movement being controlled via (electro)magnets, toothed rails, strings and/or cables.
  • the barrier - 17 - LUCM-1-1055Spec mechanism/module allows the barrier to lift/raise or drop based on semantic access control.
  • the barrier segments may be folded and/or stowed thus shortening the barrier to a particular/minimum size. Further, the barrier may be stowed along the vertical length of the posts; further, the (compacted) barrier may slide down along the vertical side of the post and thus adjusting the height of the post to an optimal/minimum height.
  • the barriers from at least two semantic posts may join and/or lock together using joining and/or locking mechanisms; such mechanisms may comprise mechanical and/or magnetic components.
  • the tips of the barriers comprise magnets which when in vicinity attract and lock together. Magnetism in the components may be controlled by semantic units (e.g. via a voltage, current, inductance, magnetic flux etc.) and thus controlling the timing (e.g.
  • Two joining posts may use joining capability for communication, networking and/or energy transfer.
  • the bands, clips, barriers and their latches/connections/tips incorporate feed cables and connections.
  • the posts comprise capabilities such as joining and/or delimiting bands, barriers, pop-up signs and so forth in other examples they may lack such capabilities.
  • the semantic zoning and access control may be implemented by physical moving and positioning of the posts (e.g. as blocking posts, delimiting posts, guiding posts, semantic zoning posts etc.). In some examples the posts may or may not comprise joining and/or delimiting elements.
  • the semantic zoning and/or access control can be based on the augmentation provided via pop-up signs (e.g. capabilities, rise/fall commands etc.), displays (modules) attached to the semantic posts and/or other semantic fluxes.
  • the semantic posts may be controlled via a centralized and/or distributed computer system where the functionality is distributed among pluralities of control modules and/or other external computers, computer banks or clouds. In some examples the distributed computer system is organized in a hierarchical manner.
  • the power module may comprise a power hooking mechanism that is used to plug-in and recharge the power module. It is to be understood that the plug-in may be automatic based on sensing and robotic capabilities.
  • the charge socket is localized via sensing and the system guides a post’s rechargeable plug via orientation and/or routing in a semantic network model where at least one endpoint is mapped to the location of - 18 - LUCM-1-1055Spec the charge socket; further, at lower endpoint levels other location based features and/or shapes of the socket are mapped and used with orientation and routing.
  • the location of the charge socket may be mapped and detected via any available sensing technique or a combination of those.
  • shapes, sockets and/or its features are detected via camera sensing (e.g. frame processing based on deep learning, semantic segmentation, semantic analysis etc.).
  • the power module can be attached or detached by sliding and/or lifting the assembly (e.g.
  • the structure fixation and manipulation module 4 is used to attach the smart post to various fixed and mobile structures including walls and bases in any orientation.
  • the base is a structure of a car, drone, aircraft or any other mobile structures.
  • the fixation module it may incorporate various latching, hooking and clipping mechanisms for attachment that may be present sideways and/or underneath. Further, the latching and locking mechanism may allow the movement and orientation of posts in various angles.
  • the clipping module and/or the structure fixation and manipulation module are used to compose larger formations and/or structures of smart posts.
  • those formations are based on semantic inference and semantic groups of posts.
  • a group of smart semantic posts are joined together to form a larger structure (e.g. a larger transportation system, trailer unit, bed truck, vehicle, drone etc.).
  • the composable structure can comprise a variety of configurations of the smart posts; for example there may be posts in the structure comprising sensing units such as optical module and/or antenna elements module while other posts in the structure (e.g. used to compose a flat transportation bed) may not have such capabilities (e.g. comprise a combination of the moving base module, power module, clipping and fixation module, control module and/or trunk module including any telescopic capabilities).
  • FIG. 11 and 12 present example of such configurations where smart posts (for example, posts 101a through 101e; for simplicity, not all posts shown in Figs. 11 or 12 are labeled) are used in conjunction to form various configurations of smart carriers.
  • the system composes the sensing able posts with reduced posts (lacking some sensing capabilities) in order to form smart flat carrier beds.
  • Such composable configurations may be based on goals, missions and rewards thus, the system selecting the optimal configuration.
  • mission - 19 - LUCM-1-1055Spec collaboration may occur where goals and/or sub-goals are split, challenged and/or distributed between modules, posts and/or semantic fluxes by semantic leadership.
  • a group of posts are used to hook up and carry a net (e.g. for drone neutralization goals and purposes).
  • a group of posts hook up and carry drone neutralization measures (e.g. arrow launchers, high powered lasers, mini-drones etc.).
  • the system deems an area as needed to be cleaned up of drones and based on the goal the system launches ANTI DRONE and DRONE DESTROY missions and routes. Such missions may be inferred for example based on user or flux feedback and/or input (e.g.
  • an area-based endpoint EC encompasses area-based locations EA and EB.
  • semantics and missions from a higher-level authorization is marked and/or established for such areas they will take leadership over lower authorization levels; the system pursues goal based inference on such missions with leadership associated to higher level authorization semantics, missions and groups; in the case of increased superposition (e.g.
  • the system may perform superposition reduction by asking for additional feedback (e.g. from a user, identity or semantic group based on authorization level, flux etc.) and/or assigning additional bias based on profiles and/or preferences. If no feedback or profile is available, the system may perform the missions based on higher levels policies and/or hard route semantic artifacts. It is to be understood that the authorization levels may be inferred for various semantic identities, semantic groups and/or semantic profiles based on semantic analysis and leadership. Thus, in a first context (e.g.
  • a semantic group A might be assigned a higher authorization level than semantic group B while in a second context the group A might be assigned a lower authorization level.
  • the authorization levels are assigned based on inferred semantic artifacts (e.g. semantic routes, semantic profiles etc.) and the system uses the semantic artifacts and further projections for further inference and validation of authenticity.
  • a confusion semantic factor may be inferred based on the incoherent and/or coherent superposition factors, indicators, rate and/or budgets wherein the confusion factor is high if the incoherent superposition is high and/or coherent superposition is low.
  • the confusion factor is low when the incoherent superposition is low and/or coherent superposition is high.
  • the system may prefer coherent semantic artifacts during analysis when the confusion factors are high and may use more incoherent semantic artifacts when the confusion factors are low.
  • Allowed confusion factors thresholds, intervals and/or budgets may be inferred, ingested, adjusted and/or predefined by inputs from users, semantic fluxes and semantic analysis.
  • Confusion factor semantic intervals may be associated with semantic artifacts (e.g. semantic routes and/or rules) thus allowing the system to apply such artifacts when the system exhibit a particular confusion range.
  • the higher the confusion factor, the higher priority based on leadership and/or factorization have the rules that are associated with such intervals (hard routes and rules may have explicitly or implicitly the highest priority).
  • the system may exhibit an undetermined (time) interval of confusion and thus the system may use further semantic rules (e.g. access control, time management rules) to restrict and/or bound the confusion interval.
  • the system may adjust factors, budgets and or quanta in order to control the inference towards goals and/or keep (goal) semantic inference within a semantic interval.
  • the system may infer DO NOT semantic artifacts (e.g.
  • the system may use the semantic areas depth axis (e.g. Z axis) attribute for hierarchy determination and for establishing the leadership semantics.
  • the system may provide more leadership bias towards semantic artifacts associated with higher placement on the Z axis, in this case EB.
  • biases may be configurable or provided as part of semantic profiles (e.g. associated with users, identities, semantic groups, semantic artifacts etc.).
  • semantic profiles e.g. associated with users, identities, semantic groups, semantic artifacts etc.
  • - 21 - LUCM-1-1055Spec [00193] It is understood that the authorization rights and levels may be based or assigned on hierarchy levels and/or artifacts in the semantic model. For example, the right for DRONE SHUTDOWN related artifacts may be assigned to particular semantic groups (e.g. of users, semantic posts, endpoints etc.).
  • the semantic network model inference may be guided by semantic superposition factors and/or biases provided in the context of semantic profiles and/or authorization at various hierarchy levels.
  • two endpoints may be associated with two zones which overlap (e.g. by coordinates, geographically, semantically etc.; two property/facility areas overlapping on a no man’s land zone between two properties mapped to endpoints).
  • the system may infer the intersection endpoint (a third endpoint) as an area associated with an inferred agreement (e.g.
  • At least one endpoint associated and/or comprising the first and the second (and potentially the third) endpoints and based on the reunion of those zones may be associated with the semantics, agreements, fluxes and/or narratives of/at the two endpoints plus additional semantics, agreements, fluxes and/or narratives resulting from semantic analysis on such composable artifacts.
  • the system infers and maintain hierarchical structures of semantic artifacts which help assign the law of the land and/or agreements to various mappings.
  • law of the land and/or agreements may be composed and comprise various semantic artifacts associated and/or particularized with semantic groups, semantic identities and so forth; further semantic analysis of the composable laws of the land may be based on semantic groups and/or semantic identities (e.g. TRUCK OPERATORS, NURSE/S HOLDING A NEWSPAPER, JOHN’S DE LOREAN etc.). It is to be observed that the semantic identities (e.g. NURSE/S HOLDING A NEWSPAPER, JOHN’S DELOREAN etc.) may be developed in time based on semantic inference and may be related with semantic groups; further they can be inferred by semantic grouping.
  • semantic identities e.g. NURSE/S HOLDING A NEWSPAPER, JOHN’S DELOREAN etc.
  • semantic identity of NURSE HANDS and of a NEWSPAPER are formed as a semantic dependent group.
  • a semantic trail/route of NURSE, (HANDS, HOLD), NEWSPAPER may be used.
  • the system may be more specific about the semantic identifiers (e.g. “THE” NURSE HOLDING A NEWSPAPER, NURSE JANE, HEALTH AFFAIRS etc.).
  • the system may associate, - 22 - LUCM-1-1055Spec group and/or learn semantic routes and/or rules (e.g.
  • Such inferred and learned artifacts may comprise time management (e.g. WEDNESDAY AFTER LUNCH); further, based on the semantic route and the identification of JANE it may create behavioral routes for the semantic identity comprising leadership semantics (e.g. NURSE and/or more precisely for NURSE JANE and/or JANE).
  • the law of the land at an endpoint may comprise particular rules and/or agreements published by an endpoint supervisor.
  • the endpoint supervisor has the rights to publish/unpublish the laws of the land.
  • the laws of the land may be composed, augmented, resolved and/or validated hierarchically (for coherence/confusion); alternatively, or in addition, this may happen when confusion is detected and/or before publishing.
  • users, operators and/or supervisors may be notified and/or challenged in a (diffusive) hierarchical manner.
  • specific level laws, publishing and/or supervisors may be validated and/or approved with supervisor levels.
  • the system may augment supervisors and/or not publish and/or unpublish artifacts which are being non- affirmatively factorized as per supervisors’ goals in a potential hierarchical supervising manner.
  • the system detects semantic shapes which move and/or are linked together and thus infers semantic grouping and/or identities.
  • semantic group semantic
  • semantic identity are/is associated with indicators and/or factors comprising higher confusion, low trust and/or risk (e.g. because they are unnatural, not learned, not believable etc.); further, the (semantic) leadership and/or factorization of one shape over the other may determine the semantic identity.
  • the system detects a wheel and a mobile phone spinning around the wheel (e.g. in an un/controlled manner); while the factorization of the parts allow potentially very believable inferences, the factorization of the composite reflects it’s hard believability as does not resemble any known route and/or is hardly/not diffused by semantic rules. Nevertheless, the system may infer a semantic route, group, shape and/or rule which have and/or are associated with decayed believability, elevated confusion and/or high-risk indicators and/or factors. Further, based on the factorization of particular circumstances and/or profiles the composite semantic inferences (e.g.
  • the believability factors may be associated with particular semantic groups and/or leaders.
  • the system may provide leadership of the (composite) semantic artifacts which are more believable (e.g. SPINNING WHEEL vs SPINNING PHONE etc.).
  • the system may use semantic shaping and/or overlaying of (known/saved) semantic network models in order to infer such believability factors and/or artifacts.
  • the inferences may be guided by privacy rules which may allow, deny and/or control inference and/or collapsing and thus inferring only the allowed level of granularity for semantic identities and/or semantic groups.
  • privacy rules may deny inferring, projecting and/or using semantic identities associated with a particular threshold or lesser number of objects and/or artifacts. It is understood that the level of inference granularity may be based on hierarchical and/or projected inference.
  • the system may infer/assign leadership on particular locations, endpoints and/or semantic groups thereof to particular semantic identities and/or semantic groups thereof. Such leadership inference/assignment may be based for example semantic analysis including semantic time management.
  • the (semantic) leadership may be inferred/assigned based on particular goals and/or factor intervals.
  • two entities E1 and E2 e.g. governments, companies etc.
  • E1 and E2 share a common FISHING area and are bounded by a goal/sub-goal of DEVELOP FISHING, KEEP THE WATER CLEAN or DEVELOP FISHING BUT KEEP THE RISK OF CONTAMINAING THE WATER LOW.
  • the system may change ratings of the entity E1 in rapport with the goals/sub-goals and potentially update and/or index the time management rules asserting the leadership of the other entity (e.g.
  • a new leadership (E2) is inferred and exerted (e.g. based on semantic profiles of E2) once the conditions are breached while potentially bounding the breaching entity (E1) with goals (e.g. creating semantic artifacts including semantic routes, time management rules etc.) to (help) bring/recover the conditions to an agreed semantic artifacts baseline, anchor and/or goals.
  • goals e.g. creating semantic artifacts including semantic routes, time management rules etc.
  • such inferences, ratings and/or leaderships may be related with more complex environments with multiple entities, semantic fluxes and/or semantic groups contributing to collaborative contractual inferences such as explained throughout the application.
  • Semantic leadership is inferred and/or adjusted based on semantic analysis including semantic factorization.
  • the system uses semantic gating at endpoints in order to preserve confidentiality in relation with semantic inference associated with inferences related to objects and/or semantic identities passing through the endpoints.
  • the antenna module may be positioned on top of the optical module; further, in other embodiments the optical module may not be present at all with the optical detection capabilities being performed by the antenna module. While this are specific examples, the generality and applicability of flexible module compositions extend to any configuration. In other examples as depicted in Fig.
  • the telescopic capabilities of the posts may allow the realization of enclosed areas within a composed post structure.
  • posts 61 are all “high raised” posts forming a perimeter about posts 62 which are relatively lower.
  • the “high raised posts” are using telescopic capabilities to form an enclosed area on the lower posts.
  • Such areas may be used for example to store or conceal tools, articles and any other artifacts.
  • the enclosed posts area by the high raised posts may be based on a semantic group inferred based on a sensed pressure exercised by a load on the enclosed posts.
  • the system elevates the post (e.g.
  • a wagon carrier driveline may be composed from a plurality of detached carriers and/or beds (e.g. a driveline comprises four carrier beds, one for each corner of a wagon) which may be represented and/or inferred as semantic groups.
  • the system elevates posts for guiding, locking and/or connecting other artifacts or components into the enclosed areas; in an example the system encloses a higher capacity battery of a larger size wherein the system uses goal-based inference to determine the battery type and infer the enclosed area where to be placed.
  • the smart posts can join and/or clip for improved sensing and processing.
  • Fig.14 shows nine posts 101a-i in a configuration of 3x3 forming a combined sensing and/or processing capability.
  • the composability of such elements and groupings is based on specific goals that may be specified by a user and/or inferred by the system.
  • such goals may comprise of CARRY 7 BIG LUGGAGES or CARRY 7 6 BY 6 LUGGAGES and the system estimates the size of a flatbed and the number of required posts to form the flatbed based on mapping endpoints to areas to be covered by posts, luggage, and/or by using its own estimation of size, weight and/or indexing of the semantic BIG.
  • the goal may comprise further restrictions such as USING A MAXIMUM 4’ CARRIER WIDTH; such restrictions may be based for example on estimating an optimal route of travel (e.g.
  • restrictions e.g. a location comprising a door of 4’ width.
  • restrictions may be based for example on inferred location-based semantics (e.g. using a camera or vision sensors for detecting the door width).
  • the system composes various post configurations based on their sizes to determine the optimal join topology which may be based on mapping a semantic network (e.g. endpoint) model to areas to be covered by particular posts.
  • the previous example may incorporate wheeled smart posts, alternatively, or in addition, it may incorporate drone type semantic posts comprising a copter module for lifting; it is to be understood that the smart post modules including the copter module may comprise motors/engines, propellers, servomotors, electronic speed controller, analog blocks, digital blocks and actuators.
  • the system activates the wheeled module and/or copter module of the smart posts based on routing and semantic inference on the semantic model.
  • the semantic network model may be mapped to land-based locations and/or aerial based locations.
  • the system may create a composite formation of posts/units (e.g.
  • the system infers low count, low trust rating, unreliable and/or conflicting semantics by posts at a location. Further, the system may infer that the coverage of location and/or a mapped semantic network model in the field of sensing is not adequate. Thus, the system composes the smart posts to improve coverage and/or reliability of semantic inference. In further examples, the system combines smart posts in a formation based on their capabilities; in addition, it may use a goal or mission- based inference to form the composite based formation. [00210]
  • the antenna elements module 7 see also Fig.
  • the antenna elements and panels may incorporate RF and optical frontends, transmit/receive modules, ADC, DAC, power amplifiers, DSPs, semantic units and other analog and/or digital blocks and components. Other post modules might incorporate similar elements in some embodiments.
  • the vision, or optical, module 8 may incorporate arrays of camera and/or vision sensors 23 disposed in a circular pattern about the perimeter of an optical module such as in the example illustrated in Fig.2B, or may be arranged within an upper dome in an array pattern, or may incorporate dome cameras or others, such as illustrated in Fig.2A (showing the outer dome, with the optical elements or cameras not visible within the dome).
  • the cameras and/or vision sensors may be of time of flight type comprising laser and/or photonic elements for emitting and receiving (e.g.
  • the control module 5 is used to process the information of the robotic unit and for communication via the sensing and wireless modules (e.g. antenna modules).
  • the posts may communicate with each other (such as depicted in Fig.10B, showing three separate smart posts labeled posts 1, 2, and 3) or with the distributed computing infrastructure (as illustrated in Fig. 10A, also showing three posts, numbered 1, 2, and 3) using any wireless protocols.
  • the posts may communicate through wiring and/or cabling embedded in the connecting bands and/or clips while the latching and clipping mechanisms comprise cabling connectors (e.g. specialized connectors, RJ45, Ethernet, serial interface etc.).
  • cabling connectors e.g. specialized connectors, RJ45, Ethernet, serial interface etc.
  • the control module functionality may be distributed amongst other modules, posts, computers and computer banks.
  • the clipping and fixation mechanisms allow the posts to reconfigure in various setups, topologies, zones and settings.
  • the robotic distributed infrastructure allows such reconfigurations based on semantic inference including localization, hierarchical network models and zoning. While various clipping and attaching modules and mechanisms have been presented and depicted it is to be understood that such clipping and attaching mechanism may be standardized in some applications.
  • Semantic IOT composable cloud and real time semantic technologies provide adaptive real time and just in time operational intelligence and control while aggregating disparate sources of information.
  • They function based on semantic engines which interpret semantic models and semantic rules and thus are highly adaptable to the operational or simulated context. They are highly suitable for integrating multi-domain knowledge including capabilities, interdependencies, interactions, actions and what-ifs scenarios.
  • Real-time semantic technologies understand the meaning of data from various sources and take appropriate actions; they provide real time situational awareness and automation.
  • a semantic engine performs semantic knowledge discovery by using a set of adaptive artifacts including a semantic model which may be defined by a user, ingested or learned by the system.
  • the semantic model comprises the representation and mapping of informational flows and groupings to meanings (e.g. linguistic based terms related to objects, states, control actuation, groups, relationships, routes etc.); the semantic system guides the inference in the semantic model based on semantic rules and routes which specify how the system should behave.
  • the capacity of a semantic system inference capabilities increases as the semantic model evolves through modeling and learning.
  • the semantic model is defined as linguistic based operational rules and routes. Further, the semantic model may be associated with hierarchical semantic network models for further management of paths, fluxes/flows, routes and semantic inference.
  • semantics are assigned to artifacts in an oriented graph and the system adjusts the semantic network model based on ingested data and semantic inference.
  • the semantic network graph comprises endpoints and oriented links in a potential hierarchical structure with graph components representing another semantic network graph.
  • the semantic engine is able to perform inferences in real time, providing semantic intelligence, adjusting the semantic model and potentially executing actions.
  • Semantics and/or semantic attributes are language or symbol terms and structures that have a meaning. The meaning in particular contexts and circumstances is established by semantic models including semantic groups and semantic routes; when associated with a semantic network model they may be associated with artifacts in a semantic graph representation of the system.
  • a semantic group represents a grouping of artifacts based on at least one semantic relationship.
  • Semantic routes comprise a collection of semantic artifacts (e.g. semantics, semantic groups, semantic routes, semantic network model artifacts etc.) and potential synchronization times; the semantic routes may be represented as a semantic and/or as a semantic group of semantic artifacts. They may be also associated with semantic rules (e.g. time management, access control, factoring, weighting, rating etc.).
  • Semantic routes may be represented, associated and/or identified with semantic artifacts (e.g. semantic and/or semantic group) and as such they benefit from general semantic modeling and analysis.
  • Semantic routes may be organized in a hierarchical manner with semantic routes comprising other semantic routes. Such hierarchical structure may be recursive. [00221] The semantic routes may be grouped in semantic groups and participate in semantic inference. [00222] Semantic routes associated with a semantic network model may be used for artifact (e.g. traveler, smart post) routing within modeled environments. [00223] In this disclosure we will refer as semantic rules to all rules that allow semantic inference comprising composition and management plans including time management, access control, weighting, ratings, rewards and other factors (e.g. risk). [00224] Semantic routes may be used as and/or to implement operational rules and guidelines.
  • the system is provided with allowable, desired, non-allowable and/or non-desired routes.
  • a route specifies that HOT CROWDED SPACES ARE NOT PLEASANT and also that CLOSE TO SHOPPING IS NICE and thus semantic post units and/or groups provisioned with such routes when inferring a HOT CROWDED SPACE semantic (e.g. via semantic composition) for an area would select the previous rules and determine a further route comprising COOLING and/or DIVIDE crowds to areas encompassing (or closest) to SHOPPING locations.
  • areas may be mapped to endpoints in a network model representation of a physical space and the system would execute the commands in the routes based on the existing or deployable capabilities at mapped endpoints (e.g. areas).
  • the DIVIDE semantic may be achieved via further semantic inference comprising smart post routing/guidance topologies, semantic shaping, semantic orientation and/or semantic augmentation.
  • the COOLING semantic may be achieved if the areas comprise cooling capabilities and/or semantics (e.g. via a fixed air conditioning fan module which may be potentially attached to a smart post unit).
  • semantic inference techniques are explained in a family of patent applications such as - 29 - LUCM-1-1055Spec US20140375431, the content of which is incorporated by reference.
  • semantic artifacts e.g. HEAT related, etc.
  • the system may pursue the COOLING leadership and/or capabilities.
  • the inference at an endpoint may be based on semantic profiles of the (semantic) identities at the area/endpoint and thus the high shift and/or entropy semantics may be based and/or related with at least one (semantic) identity and/or (composite) profile. If the area and/or endpoint semantics are inferred based on multiple identities (during at least on a projected hysteresis, diffusion and/or semantic time interval) then the system may pursue COOLING capabilities (e.g. until the entropy, drift and/or factors adjust to sensible (composite profiling) (hysteresis) levels, health risk of HEAT decreases etc.).
  • the system determines goals and further optimized semantic shapes of groups of posts (or cars) to be realized within particular semantic budgets (e.g. based on energy consumption/quanta, fuel related quanta, entropy etc.).
  • Such shapes and/or zones may be based on semantic groups and/or presence at particular areas and/or endpoints.
  • such shapes may be associated with areas, endpoints, trajectories and/or sub-models.
  • the shaping may take in consideration the fitting of the posts within an area or endpoint based on semantic inference on dimensions, mappings, semantics and/or further semantic analysis; further, the shaping may be based on semantic orientation and drift analysis between the goal group shape and the current group shape.
  • semantic shaping is used to optimize traffic flows where the system determines the best shapes, zones and endpoints for groups of vehicles at particular times or particular areas.
  • semantic shaping and semantic analysis may be used to optimize container and/or artifact storage in particular areas and/or volumes (e.g. mapped to semantic models).
  • Semantic inference uses semantic analysis comprising semantic composition, semantic fusion, semantic routing, semantic resonance, semantic indexing, semantic grouping, semantic time and/or other language based semantic techniques including semantic shift, entailment, synonymy, antonymy, hypernymy, hyponymy, meronymy, homonymy. - 30 - LUCM-1-1055Spec [00229]
  • a semantic group containing all the synonyms for “great” is stored and used in semantic inference.
  • the group comprises semantic factors assigned to semantic components to express the similarity within a group or with the semantic attributes defining the group.
  • the system stores a semantic group for the same semantic (e.g.
  • the system stores separate identities and/or groups for “cat” and/or “c.a.t.” as they are associated with different semantics; further, during semantic inference the system infers leadership to “c.a.t.” over “cat” or vice-versa based on exact semantic identification (e.g. match the exact semantic form and/or identity) and/or semantic view.
  • the system may have inferred from ingested data that artifacts (e.g. “cat” and “c.a.t.”) have and/or are associated with different semantics (e.g.
  • semantic identities and thus the system is able to identify and/or create such semantic identities and/or semantic groups.
  • the system may infer that the ingested artifacts are associated with the same semantic (e.g. (“running”, “runnin’ ” and thus the system may create a semantic identity and/or group to reflect the association and for further optimization.
  • the leadership may be determined by coupling of semantic analysis and/or circumstances (e.g. location/localization, language, semantic profiles, roaming etc.).
  • the semantic analysis comprises semantic techniques such as synonymy, semantic reduction, semantic expansion, antonymy, polysemy and others.
  • the user specifies lists of synonyms, antonyms and other lists that are semantically related.
  • the elements in a list are by themselves related through semantic groups via semantic attributes or semantics (e.g. SYNONIM, ANTONIM).
  • Real time semantic technologies optimize processes and resources by considering the meaning of data at every level of semantic AI inference. Real time semantic technologies are well suited for providing situational awareness in ports of entries while further providing a framework for adaptive integration.
  • Semantic IOT infrastructure based on smart posts/robots and real time semantic technologies can provide precise counting, times and routing at the port of entries.
  • the ports of entry layout may be modeled through hierarchical semantic network models wherein the endpoints are associated with smart post sensing and locations in the layout; further, oriented links between endpoints represent the flows, transitions and the semantics of traffic at the modeled/instrumented points.
  • the area, location and sensing based - 31 - LUCM-1-1055Spec semantic network model is recursive and thus can be used to achieve the desired level of granularity in the mapped environments.
  • Semantics may be associated with sensing/data flows, checkpoint attributes, traveler attributes and further, the semantic model comprises semantic routes and how semantics compose. Flows/fluxes semantics and interdependencies may be modeled and learned via semantic modeling and inference.
  • the counting of people in monitored queues, areas or endpoints may be based on the traveler-based semantics inferred based on transitioning of links in the semantic layout/sensing model. Further, the system guides the semantic inference for traveler waiting times using semantic time and semantic intervals.
  • the semantic time and semantic intervals allow time inference based on semantics.
  • a semantic time is indexed based on the context of operation. Thus, semantic time and semantic intervals ensure that the time inference takes places in the most accurate context of operation.
  • a semantic system achieves predictive semantics.
  • a checkpoint for foreign nationals is timed based on the transitions in the semantic network model.
  • a semantic system also groups artifacts based on semantic inference and use those groups in further semantic inference.
  • the system may detect object types or complex semantics based on such semantic groups (e.g. group sensors, settings and detections and infer meanings, infer travelers by detecting flows of grouping of detections, features, clothing items and belongings; infer that a person is carrying a red bag etc.).
  • the Semantic IOT is a distributed composable cloud and as such it distributes, groups, compose and fusion various modalities detections in an optimized manner; as mentioned, the modalities may comprise a diverse spectrum of electromagnetic sensing.
  • the modalities may comprise a diverse spectrum of electromagnetic sensing.
  • the counting may be based on the transitions in the semantic network model; thus, when a link in the semantic network model is transitioned as detected by the smart posts and their modalities, the system infers a particular semantic (e.g. TRAVELER ENTER CHECKPOINT 1 or TRAVELER EXITS CHECKPOINT 1).
  • Semantic composition and fusion of such semantics allow the coupling of detected semantics in and with time (e.g. counting the number of semantics/travelers at checkpoints, estimating waiting times or other general or personalized semantics) in the most flexible, efficient and optimized manner and utilizing a minimum amount of resources thus decreasing system costs.
  • Other systems may not employ such flexibility, optimization, fusion and modeling techniques and hence they are not able to provide the same capabilities, coherence, accuracy and cost effectiveness.
  • the system will use adjustable inferable model semantics for mapping the type of service (e.g. CITIZENS AND PERMANENT RESIDENTS mapped to transition links from the checkpoint inbound to checkpoint outbound), for counting (e.g.
  • Semantic automation and augmentation ensure actions in various domains; in an example, the coupling of the command and control model to semantic automation and augmentation may implement automatic or semi-automatic guiding, routing and access control in port of entry environments.
  • the technology may be used to automate various tasks and provide semantic intelligence in various forms including display, sound, actuation, electric, electromagnetic, etc.
  • Solutions for port of entries includes developing semantic network models to be deployed on the distributed semantic cloud and mapped to a semantic sensing infrastructure.
  • the semantic sensing infrastructure may include smart semantic posts/appliances comprising sensors, batteries and semantic sensing units which can be deployed throughout the port of entry.
  • the assumption in this example is that there are no available sensors at the monitored locations and as such the system uses semantic sensing for feeding the semantic network model.
  • Semantic systems provide semantic fusion and as such, the system may integrate various data sources and/or additional sensing infrastructure for contextual accuracy - 33 - LUCM-1-1055Spec and more precise inference.
  • the smart posts comprise one or more of radiofrequency, camera/optical/infrared sensors.
  • camera/optical/infrared sensors can be selected from cost effective solutions such as low-cost ones designed for mobile devices.
  • the radiofrequency devices/sensors may function in microwave frequencies range (e.g.2.4Ghz to 80Ghz) or higher.
  • Such sensors be easily deployable and reconfigurable in various environments and as such they may be one or more of the following: mobile post deployed sensors and fixed posts deployed sensors. While the smart semantic posts/appliances may be mobile in some environments, they can deploy as fixed on walls or other structures.
  • the smart posts may comprise Li-Ion batteries which may provide extended functioning time for the attached sensors and semantic units.
  • the battery posts provide real time awareness of their charging status which allow easy maintenance whether manual or automatic for charging and/or battery replacement. Alternatively, they may be plugged in at any time at a permanent or temporary supply and/or charging line. For easier maintenance of the battery powered devices, they may be deployed in a mutual charging and/or external charging topology comprising RF and/or robotic charging components.
  • the microwave devices/sensors may comprise multiple sensing elements (e.g. 4 to 256) which allow the sensors to detect steer and optimize the beam, frequency, detection and communication patterns. More antennas may be present thus providing more scene interpretation capabilities and data that can be fused for knowledge discovery (e.g. adapting and changing radiation patterns, adapting frequencies and polarizations).
  • post sensors are disposed to capture transition patterns in at least one semantic network model which may be stored at each post comprising control module logic.
  • the system detects and counts semantics of objects depending on the determined semantic of travel (e.g. PERSON IN CHEKPOINT GATE 2, PERSON OUT CHECKPOINT etc.).
  • semantics of objects e.g. PERSON IN CHEKPOINT GATE 2, PERSON OUT CHECKPOINT etc.
  • the system uses one or two posts for lane ingestion and one or two posts for departure detection.
  • the location based semantic network models comprise fewer artifacts than in non-lane-controlled areas, thus minimizing the processing and - 34 - LUCM-1-1055Spec optimizing power consumption.
  • the relevant detection happens in near field for both optical and microwave and as such the data interpretation would be straightforward.
  • semantic system capability of changing and adapting the sensing patterns allows the reduction in the number of collection points and the number of sensors and thus maximum flexibility in deployments.
  • the system may employ a more complex near to far field semantic model of locations which are mapped to semantic sensing detection techniques.
  • the semantic engine fuses the information in the semantic network model.
  • the system uses radio frequency polarization diversity to improve detection in multipath environments.
  • the smart semantic sensors may employ diversity antennas and/or use coupling of antenna elements to adjust electromagnetic radiation, polarizations, optimize frequencies and so forth.
  • the system may reposition the smart posts in the environment and coordinate them to clip to each other in order to delimitate and realize the semantic zones and topologies required for traffic flow control.
  • posts are disposed in a guiding lane configuration.
  • a first series of posts labeled a-f are on a left side of an entry point 40 and a second series of posts g-n are on a right side of the entry point.
  • the entry point may be a location of passport control, boarding a craft, check-in, or any other point at which persons are processed or allowed to pass.
  • the posts are arranged closely adjacent one another, and preferably with their associated ropes or belts attaching adjacent posts to one another but with the belts either retracted within the respective post or hanging in a slack fashion.
  • some of the posts have moved and been extended to increase the length of the traffic lane between the posts.
  • posts d, e, and f have moved, as has post n, as indicated by the arrows and the visibility of the belts that have been extended.
  • the posts have extended to the fullest extent, forming the longest line possible for the assembled collection of posts.
  • one or more of the sensors (cameras, antennas, analog and/or digital blocks/devices etc.) of one or more of the posts scans the region between the posts, indicated as region 41. Upon the detection of persons standing in the region, the system determines that an extension is required.
  • the particular logic may vary and be determined as above, but for example may require a plurality of posts a-f and/or g-n to detect static persons in the area, waiting but not moving quickly.
  • - 35 - LUCM-1-1055Spec [00255]
  • one or more of the posts continues to scan the area, including region 42 occupying the terminal end of the lane 50 defined by the opposite pairs of posts.
  • Most preferably, at least the end posts f and n provide input indicating the presence of persons standing in that region.
  • all of the posts, or at least a larger subset also provide such an input which is used by the controller to determine whether to extend the posts yet again and thereby form a larger line.
  • the posts have exhausted their reach.
  • the controller is programmed with a map of the area surrounding the entry point, and also tracks the location of each of the posts, in order to direct the individual posts whether to move in a direction linearly away from a prior post (for example, with reference to Fig. 8C, in a direction from post I to post k), or to move at an angle with respect to at least a pair of prior posts (for example, in a direction from post k to post l, or from m to n).
  • Fig.9 we show a perimeter delimitation configuration.
  • the perimeter in the illustrated example is defined by posts a-d, though a different number of posts may be used.
  • the posts combine to define a perimeter 51 having an internal area 52.
  • the system infers and/or a user specifies an area and/or a semantic associated with it.
  • the area may be delimited based on anchor points and/or the edges.
  • Fig 10 we show various deployment options in which the posts communicate wirelessly and/or process information in a distributed cloud infrastructure. While in embodiment A they may use an external distributed cloud infrastructure, in embodiment B they use their own internal processing capabilities in a distributed cloud mesh topology; it is to be understood that the system may use any capabilities, whether internal and/or external to infer and configure composable cloud topologies. Also, their movement, positioning and coupling may be based on semantic network models whether at sensor, post, semantic group, infrastructure or any other level.
  • any one or more of the posts may travel independently about a region, such as generally indicated with reference to posts 1, 2, and 3 shown in in Figs.10A and 10B, without being tethered to one another.
  • the posts collect the optical, audio, or other information from sensors, cameras, antennas, analog and/or digital blocks and/or devices, front-ends etc., which may then be passed along directly to other posts as indicated in Fig.10B, and/or to a central or distributed control infrastructure 100 as shown in Fig.10A.
  • the control infrastructure 100 may be a central computer communicatively coupled - 36 - LUCM-1-1055Spec with the plurality of distributed devices. It should be appreciated that any of the features described in this disclosure as being performed by “the system” may be performed by the control infrastructure in a centralized fashion, or may alternatively be performed in a distributed fashion by a distributed system including a plurality of control structures and/or computer components on the posts or robotic devices.
  • the posts may comprise master-slave configurations. In such configurations the master posts controls at least one slave post.
  • the slave posts may comprise less functionality and/or be less capable than the master post (e.g. lacking full suite of sensors and/or actuators, smaller batteries, lacking displays etc.).
  • the master post may control the movement and/or deployment of slave posts.
  • the master post detects and control the positioning of slave posts.
  • an airport may use units of groupings of master and slave posts (e.g. groupings of at least one master and at least five slaves). Such units may be deployed and yield composable topologies and formations.
  • the robotic posts formations and/or components thereof may be based on semantic groups which may comprise leadership semantic artifacts.
  • Master-slave configurations may be represented as semantic groups with the master units attaining leadership in particular configurations and/or environments.
  • the smart posts may comprise billboards, displays, actuators, speakers and other forms of semantic augmentation allowing them to convey information.
  • the smart posts may be deployed in key areas and provide guidance via semantic augmentation.
  • the semantic augmentation may comprise advertising.
  • the smart posts and/or groups may be designed as for general use, however, when they receive a mission and a target they may adapt to the mission and target.
  • a unit of posts may receive the mission to provide guidance and/or lane formation to a particular airline.
  • the posts may deploy to the targeted airline airport area and provide the semantic augmentation related to the airline; such information may comprise airline name, flight information, airline specific advertising and so on.
  • the specific information may be received and/or downloaded from a specialized advertising service and/or cloud (e.g. airline cloud).
  • the deployment of the post to the airline area may be based on the previous knowledge on the location of the airline, sensing and guidance.
  • the posts may deploy in areas that are inferred as of high risk and/or congested.
  • the distributed cloud infers such conditions it automatically initiates the deployment of units and/or topology reconfiguration; the - 37 - LUCM-1-1055Spec initialization of operations may take place based on semantics inferred at any inference capable post.
  • the posts may be deployed for achieving a topology that reduces the overall risk (e.g. guiding the travelers through lower risk areas and/or routes, dividing the crowds based on boarding zones, traveler/visa status, risk etc.).
  • the posts are deployed in location and/or areas for which the system infers particular semantics.
  • the system may infer a semantic of HAZARDOUS or SHOPPING TOO CROWDED and thus the system may dispose posts and/or units to contain those zones and/or guide travelers to other routes that do not contain such areas.
  • posts deployed for such purpose may indicate via semantic augmentation (e.g. display and/or audio, wireless beaconing) the zone semantics and directions to follow by travelers in proximity; it is to be understood that proximal semantic augmentation may be triggered when travelers are detected in proximity.
  • the travelers may include people, vehicles and any other moving artifacts considered by the system.
  • inference While we refer to inference, it is to be understood that it may be based on inference at a single post/unit, a group of posts/units, distributed cloud and any combination of the former.
  • the semantic system functions as a distributed architecture in various configurations comprising but not limited to semantic group computing, edge computing, cloud computing, master-master, master-slave etc.
  • the system issues missions and/or commands to posts that are in particular locations, areas and/or endpoints and have inferred specific semantics. For example, the system issues commands to the posts that have been deployed to HAZARDOUS semantic areas and have associated semantics of MASTER POST, BATTERY HIGH and/or STAND POST UNIT DISPLAY TIME 1 HOUR.
  • such commands may be used to display flight information, routing information (e.g. for guiding out of hazardous area), advertisements and any other type of augmentative information.
  • the selection of posts may be associated with a semantic group defined by composite semantics determined by a semantic route (e.g. STAND POST UNIT DISPLAY TIME). It is to be understood that the system may select and/or command a semantic group of posts based on compositional semantics (e.g. STAND POST UNIT) and other sematic group hierarchies formed based on semantic composition.
  • a HAZARDOUS semantic - 38 - LUCM-1-1055Spec inference may be based and/or reinforced (e.g. higher weights) using synonyms and/or related semantic groups (e.g. UNSAFE).
  • the HAZARDOUS semantic may be coupled and/or reinforced (e.g.
  • Semantic analysis comprises semantic composition, semantic fusion, semantic routing, semantic orientation, semantic gating, semantic inference and/or other language based semantic techniques including semantic shift, entailment, synonymy, antonymy, hypernymy, hyponymy, meronymy, holonomy.
  • semantic rules to all rules that allow semantic inference comprising composition and management plans including time management, access control, weighting, ratings, rewards and other factors.
  • Semantic artifacts include semantics, semantic groups, rules, semantic routes, semantic views, semantic view frames, semantic models and any other artifact used in semantic analysis.
  • Semantic technologies allow the interpretation of inputs and data streams into operational semantic knowledge which may comprise intelligent related outputs, user interfaces, control and automation.
  • the inputs, data streams and operational semantic knowledge may be related to sensing, signals, images, frames, multimedia, text, documents, files, databases, email, messages, postings, web sites, media sites, social sites, news sites, live feeds, emergency services, web services, mobile services, renderings, user interface artifacts and other electronic data storage and/or providers.
  • ingested artifacts and/or semantic groups thereof may be linked and/or associated with semantic model artifacts.
  • paragraphs/sections/headers from email, markup formatted data/objects/files, chat or posting messages and/or web pages may be represented. Further, semantic identification of such paragraphs (e.g.
  • semantic artifacts associated with the semantic identification and semantic profiles may be further factorized based on the semantic analysis of encountered tags, markups and/or their values (e.g. certain artifacts are associated and/or factorized based on an underlined and/or particular font, header etc. as detected based on tags and/or markups); further, such inferred factorized - 39 - LUCM-1-1055Spec semantic artifacts may be used to modify and/or mask the associated tags and/or markup values in documents.
  • the summary content in some documents is masked, not showed and/or not rendered in preview mode in particular circumstances (e.g., when user not present or not looking at semantic device).
  • An integral part of the semantic knowledge discovery is a semantic model which represents a set of rules, patterns and templates used by a semantic system for semantic inference.
  • the capacity of a semantic system’s inference capabilities may increase as the semantic model evolves through semantic inference, modeling and learning.
  • a semantic field represents the potential of semantic knowledge discovery for a semantic system through information processing and inference.
  • a system achieves a particular semantic coverage which represents the actual system capabilities for semantic knowledge generation.
  • the semantic coverage can be expanded by adding new streams or inference artifacts to the operational semantic capabilities of the system.
  • the semantic coverage is related to the semantic network model coverage capabilities (e.g. the area covered, the resolution covered at the lowest or highest endpoint hierarchy, the number of hierarchical levels etc.). Further, the semantic coverage may be related to sensing and inference modalities available for given semantic network model artifacts (e.g. a semantic coverage is extended if a system comprises two sensing modalities as comparable to only one modality of similar capabilities).
  • the semantics may be assigned to artifacts in the semantic network model (graph) including endpoints and links.
  • Dependencies between semantics and/or artifacts may be captured and/or determined by oriented links between the endpoints, hierarchy and/or path composition.
  • a group dependent semantic group may be represented as an oriented graph/subgraph with the causality relationships specified as oriented links (e.g. from cause/causator to effect/affected and/or vice-versa).
  • the elements in the model may be hierarchical and associated with any semantic artifacts.
  • the system may comprise symptoms – cause – effect semantic artifacts (e.g. semantic routes).
  • the system determines symptoms such as P0016 ENGINE TIMING WHEN COLD and 80% DIRTY OIL and as such infers a potential cause of 80% TIMING SOLENOID ISSUE and further projected risk (e.g. IMMEDIATE, WHEN VERY COLD etc.) of ENGINE BREAKDOWN. - 40 - LUCM-1-1055Spec
  • Semantic collaboration means that disparate systems can work together in achieving larger operational capabilities while enhancing the semantic coverage of one’s system semantic field.
  • a semantic flux is defined as a channel of semantic knowledge exchange, propagation and/or diffusion between at least a source and at least a destination.
  • a semantic flux connection architecture may be point to point, point to multipoint, or any combination of the former between a source and destination.
  • Semantic fluxes may be modeled as a semantic network model whether hierarchical or not.
  • Semantic fluxes can be dynamic in the sense that they may interconnect based on semantic inference, semantic groups and other factors. In an example, a semantic flux A is connected with a semantic flux B at first and later it switches to a point to point configuration with semantic flux C.
  • a composite semantic flux comprises one or more semantic groups of semantic fluxes, potentially in a hierarchical and/or compositional manner; further all the information from the composite flux is distributed based on the composite flux interconnection, semantic routing and analysis.
  • Dynamic flux configurations may be based on semantic groups and hierarchies. For example, flux A and B are semantically grouped at first and flux A and C are semantically grouped later. In further examples semantic groups interconnect with other semantic groups and/or fluxes, potentially in hierarchical and compositional manner.
  • Semantic fluxes may transfer information between semantic engines and/or semantic units comprising or embedded in access points, gateways, firewalls, private cloud, public cloud, sensors, control units, hardware components, wearable components and any combination of those.
  • the semantic engine may run on any of those components in a centralized manner, distributed manner or any combination of those.
  • the semantic engine may be modeled in specific ways for each semantic unit with specific semantic artifacts (e.g. semantics, semantic groups etc.) being enabled, disabled, marked, factorized, rewarded and/or rated in a specific way.
  • Semantic fluxes may use any interconnect technologies comprising protocols, on-chip/board and off-chip/board interconnects (e.g.
  • semantic - 41 - LUCM-1-1055Spec fluxes connect via semantic sensing units comprising semantic controlled components, including those previously enumerated and others enumerated within this application.
  • Semantic fluxes and/or streams may also connect other objects or artifacts such as semantic display units, display controls, user interface controls (e.g. forms, labels, windows, text controls, image fields), media players and so on; semantic fluxes may be associated and/or linked to/with display controls in some examples.
  • Such objects may benefit from the semantic infrastructure by publishing, gating, connecting, routing, distributing and analyzing information in a semantic manner.
  • Such objects may use I/O sensing, authentication and rendering units, processes, components and artifacts for further semantic analysis, gating, routing and security.
  • the semantic gating routes the information based on authentication and semantic profiles.
  • display control or user interface components and/or groups thereof are displayed/rendered/labeled, enabled, access controlled or gated based on semantic analysis, semantic profiles, semantic flux and gating publishing.
  • the system identifies the context of operation (e.g. comprising the user, factors, indicators, profiles and so on) and displays coherent artifacts based on coherent inference.
  • Various types of controls and/or dashboards can be displayed based on semantic routes and/or semantic profiles (e.g. groups specific, semantic identity specific, user specific etc.).
  • the system flows the information between semantic fluxes and gates based on semantic routing and semantic profiles.
  • the system monitors the change of data (e.g. via analyzing a rendering, bitmap, user interface control/artifact, window, memory buffer analysis, programming interface, semantic inference etc.) in the user interface and perform semantic analysis based on the new data and the mapping of the changed data.
  • the system infers and identifies display semantics artifacts (e.g.
  • mapping may be hierarchical, relative to the activated artifacts in a composable manner. Alternatively, or in addition the mapping may be absolute to the display surface whether composed or not (e.g. comprising multiple display artifacts and/or sub-models).
  • the “time” may be represented sometimes as a semantic time or interval where the time boundaries, limits and/or thresholds include semantic - 42 - LUCM-1-1055Spec artifacts; additionally, the time boundaries may include a time quanta and/or value; sometime the value specifies the units of time quanta and the time quanta or measure is derived from other semantic; the value and/or time quanta may be potentially determined through semantic indexing factors.
  • the semantic indexing factors may be time (including semantic time), space (including location semantics) and/or drift (including semantic distance/drift) wherein such indexing factors may be derived from one another (e.g.
  • semantic indexing may be used to index risk factors, cost factors, budgets and so on.
  • Semantic indexing represents changes in the semantic continuum based on semantics and/or semantic factors with some examples being presented throughout the application.
  • the system determines a first semantic at a first endpoint/link and a second semantic for an endpoint/link; further, the system determines a location for a new endpoint on an oriented link and/or endpoint determined by the first and/or second endpoint/link based on an indexing factor associated with a composite semantic which is a combination of the first semantic and the second semantic.
  • the composite semantic is a combination between a semantic associated with a source model artifact (e.g. endpoint or link) and a destination model artifact and the indexing factor associates a new model artifact on the path/link between the source model artifact and the destination model artifact.
  • the indexing factor may be associated with a semantic factor calculated/composed/associated with a semantic artifact; an indexing factor may be used to index semantic factors.
  • an indexing factor may be used to index semantic factors.
  • the system may update the semantic model and add endpoints on all semantic endpoints and/or links associated with the semantic via semantic relations or semantic groups. Further the system may redistribute the existing or newly inferred semantics on the new determined endpoints and establish new oriented links and rules. [00299] In an example the system determines an object/feature boundary based on indexing wherein the system indexes and/or merges/splits the on and/or off boundary artifacts until it achieves a goal of inferring high-quality object semantics.
  • the system may map hierarchical semantic models to artifacts in the semantic field and infer semantics at various hierarchical levels, wherein higher hierarchical levels provide a higher semantic level of understanding of feature and identification semantics (e.g. nails, legs, hands, human, man, woman, John Doe, classmates etc.).
  • feature and identification semantics e.g. nails, legs, hands, human, man, woman, John Doe, classmates etc.
  • the system maps semantic network models to objects artifacts and so on and performs further inference in the semantic field. In some examples the mapping is based on boundary conditions and detection.
  • the indexing is used in what-if and projected analysis, mapping and/or rendering the semantic model based on goals and forward/backward hierarchical semantic inference.
  • the system may invalidate and/or delete related artifacts post indexation (e.g. first and/or second endpoints/links).
  • the indexing factors may be related with indexing values related with actuation and or commands (e.g. electric voltages, currents, chemical and biological sensors/transducers etc.).
  • the indexing factors may have positive or negative values.
  • Semantic factors and indexing factors may be used to activate and control analog or digital interfaces and entities based on proportional command and signal values.
  • the system may use indexed and/or factorized analog and digital signals to control such electronic blocks, interfaces, other entities, electric voltages, currents, chemical and biological sensors and transducers etc.
  • the system may use variable coherent inferences based on at least one (variable) coherence/incoherence indicators and/or factors.
  • the semantic analysis of circumstances associated with the coherence/incoherence factors deem the variable coherent inference as coherent and/or incoherent based on the (semantic) factorization of the coherence/incoherence indicators and/or factors.
  • the semantic composition infers, determines and guides the context of operation.
  • Semantic analysis may determine semantic superposition in which a semantic view frame and/or view comprises multiple meanings (potentially contradictory, high spread, high entanglement entropy, incoherent, non-composable -due to lack of composability, budgets and/or block/not allowable rules, routes and/or levels) of the context.
  • the inference in semantic views may yield incoherent inferences which determine incoherent superposition artifacts (e.g. semantic factors, groups, routes etc.).
  • the inference in semantic views yield coherent inferences which determine coherent superposition artifacts (e.g. semantic factors, groups, routes etc.).
  • the semantic expiration may control the level of superposition - 44 - LUCM-1-1055Spec (e.g. the factor of conflictual meanings or a sentiment thereof).
  • the superposition is developed through semantic analysis including semantic fusion in which a combined artifact represents the composition and/or superposition of two or more semantic artifacts.
  • semantic expiration may be inferred based on semantic fusion and superposition.
  • the system performs fusion (e.g. potentially via multiple routes) and infers that some previous inferred semantics are not needed and therefore learns a newly inferred semantic time management rule which expires, invalidates and/or delete them and the semantic model is updated to reflect the learned rules and artifacts.
  • the system may use projections to associate and/or group ingested and/or inferred signals and/or artifacts with projected semantic artifacts; it is to be understood that such learned semantic groups, rules and further (associated) semantic artifacts may expire once the system perform further analysis (e.g. collapses them, deems them as nonsensical, decays them etc.).
  • the system learns artifacts via multiple semantic routes. Further, the semantic routes are factorized by the multiplicity of associated semantic artifacts. In an example the system factorizes a semantic route based on an association with an inferred semantic; further, the inferred semantic is factorized based on the associated semantic routes.
  • Coherent semantic groups may be inferred based on coherent and/or safe inferences (with less need of evaluating blocking routes and/or rules on leadership and/or group semantics) comprising the members of the group.
  • the coherency and/or entanglement of semantic groups may increase with the increased semantic gate publishing, factorizations, budgets and/or challenges within the group. Further, increases in coherency and/or entanglement may be based on high factorized collaborative inferences including inference and/or learning of sensitive artifacts (e.g. based on a sensitivity and/or privacy factor, risk of publishing (to other groups), bad publicity, gating, weights and/or access control rules).
  • Factors and/or indicators may influence the coherency and/or entanglement of semantic groups.
  • the increased affirmative coherency and/or resonance of (affirmative) semantic groups may increase likeability/preference/satisfaction/trust factors and/or further affirmative factors.
  • the decreased affirmative coherency and/or resonance of semantic groups may decrease likeability/preference/satisfaction/trust factors and/or further affirmative factors.
  • the system may prefer non-affirmative coherency and/or resonance of (non-affirmative) semantic groups in order to increase the semantic spread.
  • the affirmative factors may comprise affirmative-positive and/or affirmative-negative factors.
  • Affirmative-positive factors are associated with confidence, optimistic, enthusiastic indicators and/or behaviors.
  • affirmative-negative factors are associated with non-confidence, pessimistic, doubtful, unenthusiastic indicators and/or behaviors.
  • Affirmative-positive and/or affirmative-negative may be used to model positive and/or negative sentiments. Further, they may be used to asses, index and/or project (realizations) of goals, budget, risks and/or further indicators.
  • Semantic indexing may be used to implement hysteresis and/or diffusion. Semantic indexing may be inferred based on diffusion (e.g. atomic, electronic, chemical, molecular, photon, plasma, surface etc.) and/or hysteresis analysis. Further, the system may use semantic diffusion to implement semantic hysteresis and vice-versa. Semantic superposition may be computed on quantum computers based on the superposition of the quantum states. Alternatively, other computing platforms as explained in this application are used for semantic superposition.
  • the system may budget and project superposition factors.
  • a user may specify the maximum level and/or threshold interval of superposition for inferences, views, routes, goals and other inference and viewing based artifacts; further, it may specify superposition budgets, factors and goals.
  • the semantic field comprises a number of semantic scenes.
  • the system may process the semantic field based on semantic scenes and eventually the factors/weights associated to each semantic scene; the semantic scenes may be used to understand the current environment and future semantic scene and semantic field developments.
  • a semantic scene can be represented as a semantic artifact.
  • the semantic scenes comprise localized semantic groups of semantic artifacts; thus, the semantic scenes may be represented as localized (e.g.
  • a semantic group represents a grouping of artifacts based on at least one semantic relationship.
  • a semantic group may have associated and be represented at one or more times through one or more leaders of artifacts from the group.
  • a leader may be selected based on semantic analysis and thus might change based on context.
  • the leaders are selected based on semantic factors and indicators.
  • a semantic group may have associated particular semantic factors (e.g. in semantic views, trails, routes etc.).
  • a semantic view frame is a grouping of current, projected and/or speculative inferred semantics.
  • a semantic field view frame comprises the current inferred semantics in the semantic field;
  • a semantic scene view frame may be kept for a scene and the semantic field view frame is updated based on a semantic scene view frame.
  • a peripheral semantic scene may be assigned lower semantic factors/weights; as such there may be less inference time assigned to it.
  • the semantic group of sensors may be less focused on a low weight semantic scene.
  • a semantic scene comprising a person riding a bicycle may become peripheral once the bicycle passed the road in front of the car just because the autonomous semantic system focuses on the main road.
  • a semantic view frame may be represented as a semantic group and the system continuously adjusts the semantic factors of semantics, groups, objects and scenes.
  • Semantic view frames may be mapped or comprised in semantic memory including caches and hierarchical models.
  • the semantic system retains the semantics associated with that scene (e.g. semantic scene view frame) longer since the status of the scene is not refreshed often, or the resolution is limited. In some examples the refreshment of the scenes is based on semantic analysis (e.g. including time management) and/or semantic waves and signals.
  • a predictive approach may be used for the semantic scene with the semantic system using certain semantic routes for semantic inference; semantic routes may be selected based on the semantics associated with the semantic scene and semantics associated with at least one semantic route.
  • the peripheral scene doesn’t comply with projections, inferred predicted semantics or semantic routes the semantic system may change the weight or the semantic factor of that semantic scene and process it accordingly.
  • the system may refocus the processing from that scene; if there is something unexpected with that semantic scene (group) (e.g. a loud sound comes from that scene, in which case the system may infer a “LOUD SOUND” semantic based on the sound sensors) the system may refocus processing to that scene.
  • the system blocks/gates some sounds and/or factorizes others based on the perceived peripherality and/or importance (e.g. based on - 47 - LUCM-1-1055Spec location, zone, semantic identity, semantic etc.). Further, the system may infer leadership semantic artifacts associated with the non-peripheral and/or peripheral scenes and use them to enhance the non-peripheral scenes and/or gate peripheral scenes. [00327] Analogously with peripheral scene analysis the system may implement procedural tasks (e.g. moving, climbing stairs, riding a bicycle etc.) which employ a high level of certainty (e.g. low risk factor, high confidence factor etc.).
  • procedural tasks e.g. moving, climbing stairs, riding a bicycle etc.
  • a high level of certainty e.g. low risk factor, high confidence factor etc.
  • the procedural semantic analysis and semantic view frames may comprise only the procedural goal at hand (e.g. RIDING THE BYCICLE, FOLLOW THE ROAD etc.) and may stay peripheral if there are no associated uncertainties (e.g. increasing risk factor, decreasing confidence/weight factor etc.) involved in which case semantic artifacts may be gated to/from higher semantic levels.
  • the system uses semantic analysis, factors and time management to determine the reassessment of the scenes/frames and/or the semantic gating for each scene/frame (and/or semantic groups thereof).
  • the semantic view frames which are peripheral, predictive and/or have highly factorized cues (e.g.
  • semantic time quanta and/or budgets may appear to decay slower as they may require less semantic time and/or entanglement entropy budgets.
  • Semantic inference based on semantic composition and/or fusion allow for generalization and abstraction. Generalization is associated with composing semantic/s and/or concepts and applying/assigning them across artifacts and themes in various domains. Since the semantics are organized in a composite way, the system may use the compositional ladder and semantic routing to infer semantic multi domain artifacts.
  • Generalization rules may be learned for example during semantic analysis and collapsing artifacts composed from multiple semantic fluxes and/or gated semantics.
  • generalization rules learning comprises the inference and association of higher concepts and/or semantic artifacts (e.g. rules, routes, model artifacts etc.) in rapport with fluxes, signals, waveforms and/or semantic waves.
  • semantic artifacts e.g. rules, routes, model artifacts etc.
  • particular semantics may be available, associated and/or inferred only within particular hierarchical levels, endpoints, semantic groups (e.g. of endpoints, components etc.) and/or stages.
  • those semantics may be decoded and/or inferred only in those particular contexts.
  • a semantic group may comprise artifacts which change position from one another.
  • the semantic engine identifies the shapes and/or trajectories of one artifact in relation with another and infers semantics based on relative shape movement and/or on semantic shape.
  • the trajectory and shapes may be split and/or calculated in further semantic shapes, routes and/or links where the system composes the semantics in shapes or links to achieve goals or factors.
  • the semantic engine may determine semantic drift and/or distance between artifacts based on endpoints, links, semantics assigned to artifacts (including semantic factors), indexing factors and/or further semantic analysis.
  • the system may infer sentiments for the distance and motion semantics based on the context.
  • the system may infer a REASONABLE RISK for takeover while further using a semantic trail of FURTHER APPROACH THE FRONT CAR, PRESERVE VISIBILITY; as hence, the risk is reassessed based on the semantic trail, view inferences and further semantic routes (e.g.
  • the system may adjust the factor for the drive semantics (e.g. 25 % TAKEOVER FRONT CAR) based on further inferences and risk assessment (e.g.
  • the delay and/or expiration may be based on semantic indexing (e.g. time, space) and/or time management wherein the system uses existing and/or learned artifacts.
  • the system infers a CAR CRASH associated with a semantic group identity in a semantic view and as hence it adjusts the routes, rules and/or model to reflect the risk factors associated with the particular semantic group (e.g. in the semantic view context).
  • the system may use semantic (view) shaping to infer and/or retain particular semantic artifacts reflecting contexts captured in (hierarchical) semantic views potentially in a hierarchical manner.
  • the semantic system also groups artifacts based on semantic inference and use those groups in further semantic inference.
  • the system may detect object types or complex semantics based on such semantic groups (e.g. group sensors, settings and detections and infer meanings, infer travelers by detecting flows of grouping of detections, features, clothing items and belongings; infer that a person is carrying a red bag etc.).
  • semantic system is a hybrid composable distributed cloud and as such it distributes, groups, compose and fusion various modalities detections in an optimized manner.
  • the modalities may comprise a diverse spectrum of electromagnetic sensing.
  • a semantic stream is related with a stream of non-semantical and semantic information.
  • a semantic stream may transmit/receive data that is non-semantical in nature coupled with semantics.
  • the first artifact may interpret the data based on its own semantic model and then transfer the semantic annotated data stream to another entity that may use the semantic annotated data stream for its own semantic inference based on semantic analysis.
  • the second system may interpret the scene on its own way and fusion or compose its inferred semantics with the first system provided semantics.
  • the annotation semantics can be used to trigger specific semantic drives and/or routes for inference on the second semantic system.
  • a semantic stream may be comprised from semantic flux channel and stream channel; such separation may be used to save bandwidth or for data security/privacy.
  • the semantic flux is used as a control channel while the stream channel is modulated, encoded, controlled and/or routed based on the semantics in the semantic flux channel. While the channels may be corrupted during transmission, the semantic flux channel may be used to validate the integrity of both the stream channel and semantic flux channel based on semantic analysis on the received data and potentially correct, reconstruct or interpret the data without a need for retransmission.
  • the semantic stream may comprise semantic wave and/or wavelet compressed and/or encrypted artifacts.
  • the semantic flux channel distributes information to peers and the stream channel is used on demand only based on the information and semantic inference from flux.
  • the system may use authorization to retrieve data from the flux and/or stream channel; in an example, the authorization is based on an identification data/block, chain block and/or the authorization is pursued in a semantic group distributed ledger. - 50 - LUCM-1-1055Spec [00342]
  • the system may associate semantic groups to entities of distributed ledgers.
  • the distributed ledger semantic group may be associated with multiple entities and/or users; alternatively, or in addition, it may be associated with identities of an entity, for example, wherein the distributed ledger comprises various user devices. Sometime the distributed ledger is in a blockchain type network.
  • Virtual reconstruction of remote environments, remote operation and diagnosis are possible based on semantic models and real time semantic technologies. The objects from the scenes, their semantic attributes and inter-relationships are established by the semantic model and potentially kept up to date. While such reconstruction may be based on transfer models, in addition or alternatively, they may be based on virtual models (e.g. based on reconstruction of or using semantic orientation and shaping).
  • the ingesting system assigns a semantic factor (e.g.
  • Themes are semantic artifacts (e.g. semantic, semantic group) that are associated with higher level concepts, categories and/or subjects.
  • the semantic routes may be classified as hard semantic routes and soft semantic routes.
  • the hard-semantic routes are the semantic routes that do not change. At times (e.g. startup or on request), the system may need to ensure the authenticity of the hard- semantic routes in order to ensure the safety of the system.
  • the hard semantic routes may be authenticated via certificates, keys, vaults, challenge response and so on; these mechanisms may be applicable to areas of memory that store the hard semantic routes and/or to a protocol that ensure the authentication of those routes.
  • the hard semantic routes are stored in read only memories, flashes and so on.
  • Semantic routes may be used for predictive and adaptive analysis; in general, the semantic routes comprise a collection of semantic artifacts and potential synchronization times; the semantic routes may be represented as a semantic group of semantic artifacts including semantics, groups, rules etc.; they may be identified based on at least one semantic. They may be also associated with semantic rules (e.g. time management, access control, factoring, weighting, rating etc.).
  • semantic routes are used for semantic validation and/or inference they may be triggered and/or preferred over other semantic routes based on context (e.g. semantic view, semantic view frame).
  • context e.g. semantic view, semantic view frame.
  • Semantic routes may be represented, associated and/or identified with semantic artifacts (e.g. semantic and/or semantic group) and as such they benefit from general semantic modeling and analysis. Semantic routes may comprise or be associated with semantic artifacts, semantic budgets, rewards, ratings, costs, risks or any other semantic factor.
  • semantic routes representation comprises semantic groups and/or semantic rules.
  • Semantic routes may be organized in a hierarchical manner with semantic routes comprising other semantic routes.
  • the semantic rules may be grouped in semantic groups and participate in semantic inference.
  • the semantic routes and rules may encompass ethics principles.
  • Ethics principles of semantic profiles and/or semantic groups may model “positive” behavior (e.g. DO, FOLLOW artifacts etc.) and/or “negative” behavior (DON’T DO, DON’T FOLLOW artifacts etc.) and their associated factors; as specified the “positive” and “negative” behavior may be relative to semantic profiles and/or semantic groups.
  • Ethics principles may be based and/or relative to semantic profiles comprising ethics semantic routes and rules; in some examples, the ethics principles are comprised in hard semantic and/or highly factorized trails, routes and/or rules. Semantic analysis may use ethics principles for semantic factorization.
  • positive behavior artifacts within or as related with semantic profiles and/or semantic groups and associated circumstances would be preferred to negative behavior based on a reward to risk ratio interval thresholding.
  • the reward may be based on publicity (e.g. gating) of behavior based inference; further the risk may entail bad publicity (e.g. gating of semantics which would cause “negative” behavior inference (relative to the particular semantic identities, semantic profiles) in collaborative semantic fluxes and/or semantic groups.
  • Projections of publicity may be inferred through propagation and/or diffusion of gated semantics through various leadership artifacts and/or semantic fluxes.
  • particular fluxes may act as leaders, it is important to project the propagation and/or diffusion based on goals.
  • the system may diffuse semantics which will first reach a “positive influence” leader as opposed to a “negative influence” leader.
  • the system may - 52 - LUCM-1-1055Spec perform semantic orientation, routing and/or gating in order to achieve the publicity and/or influencing goals.
  • a “positive influencer” leader is relative to the goals of publisher and not necessarily towards the goal of the influencer (e.g. the influencer may have a negative behavior towards (NURSE) (JANE) artifacts but because the influencer’s negative factors/ratings on (NURSE) (JANE) artifacts propagate and/or diffuse in groups which have low ratings, high risk and/or are “negatively” factorized of routes comprising the influencer then the overall goal of generating positive ratings on those groups may be achieved.
  • the representation of semantic groups may include semantic factors assigned to each group member. In some examples semantic factors determine the leaders in a group in particular contexts generated by semantic analysis.
  • membership expiration times may be assigned to members of the group so, when the membership expires the members inactivated and/or eliminated from the group.
  • Expiration may be linked to semantic rules including time management rules; further factor plans with semantic factors and semantic decaying may determine invalidation or inactivation of particular members.
  • the semantic routes may be organized as a semantic model and/or as a hierarchical structure in the same way as the semantics and semantic groups are organized and following similar semantic inference rules.
  • the system may infer semantics by performing semantic inference on the semantic groups.
  • the system may compose and fuse two semantic groups and assign to the new group the composite semantics associated with the composition of the first group semantics and the second groups semantics.
  • Group leader semantics may be composed as well besides the member semantics.
  • the system performs semantic augmentation while inferring and/or identifying a person (JOHN) performing an activity (BASEBALL); using semantic analysis based on multiple semantic trails and routes it infers that JOHN’s skills factors are high and pursues a goal to EXPRESS OPINION TO BILL of the inference based on a semantic route of IMPRESSED SO EXPRESS OPINION TO PAL.
  • JOHN inferring and/or identifying a person
  • BASEBALL semantic analysis based on multiple semantic trails and routes it infers that JOHN’s skills factors are high and pursues a goal to EXPRESS OPINION TO BILL of the inference based on a semantic route of IMPRESSED SO EXPRESS OPINION TO PAL.
  • the inference may establish that a leadership semantic is 3 RD PERSON; as such, when being routed within the semantic network it may select artifacts that comply with such leadership semantic in semantic groups and further routes.
  • the system may have semantic groups such as PRONOUN - 53 - LUCM-1-1055Spec ( (1 ST PERSON, ALL GENDERS, “I”), (2 ND PERSON, ALL GENDERS, “YOU”), (3 RD PERSON, MALE, “HE”), (3 RD PERSON, FEMALE, “SHE”)); and further IS (3 RD PERSON, ALL GENDERS); and further GOOD (ALL PEOPLE (1 ST PERSON, 2 ND PERSON, 3 RD PERSON), ALL GENDERS (MALE, FEMALE)) and thus the system may determine a semantic augmentation of JOHN IS GOOD based on a leadership semantic of 3 RD PERSON and other semantic analysis as appropriate.
  • semantic groups such as PRONOUN - 53 - LUCM-1-1055Spec ( (1 ST PERSON, ALL GENDERS, “I”), (2 ND PERSON, ALL GENDERS, “YOU”), (3 RD PERSON, MALE, “HE”), (3 RD PERSON
  • the system may infer from BILL’s voice signals that JOHN IS GOOD and because has semantic groups that associate IS with VERB and GOOD with ADEJCTIVE it may infer a semantic route, template and/or semantic group of PRONOUN VERB ADJECTIVE; and further, similar and/or other semantic artifacts and/or relationships whether factorized or not. Further factorization may occur on such learned artifacts based on further semantic analysis.
  • Semantic decaying occurs when a quantifiable parameter/factor associated with a semantic artifact decays or varies in time, most of the time tending to a reference value (e.g.
  • the semantic may be inactivated, invalidated or disposed and not considered for being assigned to an artifact, semantic route, semantic rule, semantic model and/or inference; further, based on the same principles the semantic is used in semantic group inference and membership.
  • the system asks for feedback on group leadership, semantic factors and/or group membership.
  • the feedback may be for example from users, collaborators, devices, semantic gates and other sources.
  • the reference decaying value is associated with applied, activation/deactivation, produced or other voltages and currents of analog or digital components and/or blocks. In further examples such values are associated with chemical or biological components and mixing elements.
  • Quantifiable parameters such as semantic factors may be assigned or associated with semantics. The semantic factors may be related to indicators such as weights, ratings, costs, rewards, time quanta or other indicators and factors. In some cases, the semantic factors are used to proportionate control parameters, hardware, I/O, analog and digital interfaces, control blocks, voltages, currents, chemical and biological agents and/or any other components and/or interfaces. Those quantifiable parameters may be adjusted through semantic inference.
  • the semantic factors may be associated to a semantic (e.g. semantic identity) implicitly (directly) or explicitly via a semantic indicator in which a semantic specifies the type of indicator (e.g. risk, rating, cost, duration etc.) and the semantic factors are associated with the semantic via semantic indicators.
  • the semantic factors may be associated to a semantic via semantic groups which may comprise the semantic, the semantic indicators and/or the semantic factors in any combinative representation of a semantic group. As such, the semantic factors participate in semantic inference and analysis.
  • a semantic factor When a semantic factor is assigned directly to a semantic the system may associate and interpret the indicator associated with the factor implicitly based on context.
  • the factor is assigned to various indicators based on context.
  • the factors are associated with degrees, percentages of significance of semantic artifacts in contextual semantic analysis.
  • Implicit or explicit semantic indicators may be defined, determined and/or inferred based on a context. In an example an indicator is inferred based on goals. In other examples multiple indicators are determined for a particular goal inference. In some cases, the system may substitute an indicator over the other, may infer or invalidate indicators based on semantic inference.
  • the system may comprise indicator rules that specify the interdependencies between semantic indicators based on time management, semantic time, weights, ratings, semantics, semantic groups, semantic routes, semantic shapes and other semantic artifacts.
  • Semantic indicator rules and any other semantic rules may be associated with semantic artifacts, semantic factors and indicators. As such the system may perform recursive inference which is controlled by factor rules, decaying and other semantic techniques. Further, the semantic rules are inferred, invalidated, learned and prioritized based on such factor techniques; in general, the semantic techniques which apply to semantic artifacts apply to semantic rules.
  • Semantic factors may be associated with symbols, waveforms and patterns (e.g. pulsed, clocked, analog etc.). The association may be direct through semantics or semantic model. Further the semantic factors may be used in hierarchical threshold calculations (HTC) algorithms to determine a mapping to an endpoint.
  • HTC hierarchical threshold calculations
  • Decaying and semantic factors may be inferred and learned with semantic analysis.
  • the system learns decaying and factor semantic rules and semantic routes.
  • the semantic learning may include inferring, linking and/or grouping a multitude of trails and routes based on variation of circumstances (e.g. location, anchor, orientation, profile, environment, sensor, modality, semantic flux, route etc.).
  • the system optimizes the inference by factorizing and/or learning relationships in the network semantic model.
  • the system uses the semantic analysis (e.g. based on action/reaction, action/reward etc.) to reinforce routes and paths (e.g. based on rewards, goals etc.).
  • the system when the system infers artifacts that are not against the DO NOT guidelines (e.g. blocked semantics, rules, routes), it may collapse the semantic artifacts, link and/or factorize them. In further examples, the system may cache such routes and/or map them at lower or higher level depending on factorization and/or theme. Further, when the system infers semantic artifacts which are against DO NOT (BLOCK) rules and/or guidelines it may associate and/or collapse them with semantic artifacts based on DO semantics, artifacts and/or rules. It is to be understood that the DO and DO NOT semantic artifacts may be associated with time management rules (e.g.
  • a semantic time budget may comprise a time interval or time quanta required to perform an inference; in some examples the semantic time budget is based on semantic time.
  • Semantic cost budgets comprise an allowed cost factor for the semantic inference.
  • Semantic budgets may comprise and/or be associated with other factors and indicators (e.g. risk, reward etc.).
  • Semantic budgets may be based on predictions/projections based on a variety of factors and may be associated with semantic composition, time management rules, access control rules and/or semantic routes. Also, they may be correlated with the hardware and software components characteristics, deployment and status in order to generate a more accurate budget inference.
  • Semantic budgets may include inferences about the factors to be incurred until a semantic goal or projection is achieved; also, this may comprise assessing the semantic expiration, semantic budget lapse and/or semantic factor decaying. Such assessment of factors may be interdependent in some examples.
  • semantic thresholds and/or decaying are based on a bias where the bias is associated with particular semantics, factors and/or budgets.
  • semantic budgets may be specified by semantic time intervals. Further, semantic budgets may be specified based on decaying, factor and indexing rules.
  • the semantic budgets may comprise and/or be associated with prices (e.g. utilizing 10 quanta budgets in a computing and/or energy grid environment comprises 0.4W power consumption and/or 0.05$ charge etc.). It is to be understood that the inferences may be based on any budget including time, price, risk, reward and/or other factors and indicators.
  • the system may comprise time management rules specifying that the utilization of 10 quanta budgets in particular circumstances (e.g. time management) may entail additional bonus budgets made available (potentially also having an expiration time management) to the user and/or flux and thus the system may associate and/or index budgets with particular components, units, fluxes, routes and further factorize them (e.g. factorize a PREFERRED indicator for the bonus provider flux in rapport with particular inferences).
  • Semantic (time) budgets enable crediting and/or rewarding providers for their capabilities (at a semantic time and/or used during a (published) semantic time).
  • a user/consumer of the capability (at a semantic time) incurs a charge and/or debited for the respective capability budget while the provider is credited with the budget for the respective capability.
  • a capability may be valued, debited and/or credited based on a particular semantic identity, profile, resonances and/or further circumstances.
  • SOUP AT LUNCH WHEN JOHN PRESENT OR PROJECTED TO ARRIVE (+/- 10 MINS)
  • AND/OR 30 MINS may (be indexed to) resonate more and/or bear more credit and/or goodwill than SOUP AT DINNER AND/OR SOUP AT LUNCH IN/FOR 45 MINS and/or SOUP AT LUNCH AFTER JOHN LEAVES (e.g. for presence of resonant artifacts with John and/or for a particular resonant semantic group indicative (e.g. via factors, factorized indicators, resonance etc.) that soup is preferred at lunch vs dinner).
  • a semantic profile may encompass preferred capabilities and/or budget intervals at semantic times.
  • device and/or vehicle/post may communicatively couple and/or transfer the profile and/or preferences (e.g. selected based - 57 - LUCM-1-1055Spec on inferred semantic (times)) and the system may assigns capabilities based on (further) matching (endpoint) capabilities with preferences and/or profiles.
  • the debiting and crediting happen at the same (semantic) time while in other examples happen at different (semantic) times (potentially comprised both within another semantic time in a hierarchical manner).
  • a broker may keep associations between crediting, debiting and/or associated semantic times. The crediting and/or debiting may be based on bargaining by the broker. [00385] The bargaining (by the broker) and/or the other brokerage activities and/or capabilities may be based on crediting and/or debiting.
  • Brokers may be (flux) coupled, organized, assigned and/or associated with endpoints and/or related artifacts/inferences in a hierarchical manner (e.g.
  • a broker may act as an intermediary between associated endpoints(and related artifacts/inferences and/or further crediting/debiting/bargaining) and further (higher level) broker(s)/brokerage(s).
  • the credits may be added and/or stored to a (credit) block and/or blockchain.
  • the debits may be subtracted, marked (e.g. as debit, subtracted etc.) and/or added to a (debit) block and/or blockchain.
  • a trade system may be implemented wherein a user/consumer (e.g.
  • Capabilities may be matched based on semantic drift inference and/or semantic grouping. Further, the capabilities may be composed and/or published based on - 58 - LUCM-1-1055Spec semantic identities, semantic groups. endpoints, supervisors and/or associated hierarchies thereof.
  • Capabilities may be published by operators and/or supervisors of semantic fluxes, endpoints and/or associated devices, modules, posts and/or carriers. Alternatively, or in addition, capabilities may be enabled, activated and/or published by users of devices, modules, posts and/or carriers. Publishing and/or availability (for matching) of capabilities may be indicated, configured and/or allowed/blocked/enabled/disabled/activated/inactivated pre-discovery (e.g. before being inferred) and/or post discovery (e.g. after being inferred). [00391] The publishing may comprise and/or entail access control (e.g.
  • the publishing may be associated with an oriented link and/or flux and thus, controlling the publishing from a first endpoint and/or flux to a second endpoint and/or flux.
  • the access control may entail applying an activation and/or enablement configuration to control the availability (within and/or outside an endpoint and/or link).
  • an endpoint supervisor may configure (or indicate) the system to block/disable (projected) CT scan capabilities/interests at a first endpoint while allowing/enabling it at a second endpoint; thus, any (discovered, localized and/or inferred) CT scan capabilities or interests may not be discovered, published and/or matched at the first endpoint while at the second endpoint can.
  • the block/disable (or similar) and/or allow/enable (or similar) may be based on an endpoint and/or further hierarchies (e.g. associated with supervisors, access control, compositional/composite (factorized) semantics etc.).
  • Jane is factorized as a higher supervisor than John at a first endpoint and thus, the enablement by Jane of a tea pot capability “brew tea in 30 secs for 50 cents” may take precedence over John’s disablement of the same capability at the endpoint (and/or encompassing endpoints). However, if John is factorized as a higher supervisor than Jane at a second endpoint encompassing the first endpoint, then the capability of “brew tea in/for 30 secs (for 50c/50W(h))” is disabled within the second endpoint (but not within the first endpoint) as per John’s (and Jane’s) configuration.
  • Jane is factorized as a higher supervisor than John at a first endpoint and thus, the publishing by Jane of a tea pot capability “brew tea in 30 secs” may take precedence over John’s (publishing) blocking of the same capability at the endpoint (and/or encompassing endpoints).
  • John is factorized as a higher supervisor than Jane at a second endpoint encompassing the first endpoint, then the capability - 59 - LUCM-1-1055Spec of “brew tea in 30 secs” may be invisible/unavailable (as published) within the second endpoint as per John’s disable/blocking configuration.
  • John if John doesn’t disable/block the capability at the second endpoint, then the published capability may be visible/available within the second endpoint (and/or further outside the second endpoint if John publishes it further and/or Jane is delegated by John with the rights to publish).
  • John delegates Jane to supervise all the publishing/access control/enablement regarding “tea” (or tea pot, brewing etc.) and thus, Jane’s publishing/access control/enablement at the first point may be further published at the second endpoint (by Jane).
  • Jane is delegated as a supervisor and/or owner for tea pots (brewing) (capabilities/interests) within particular endpoints and/or all endpoints.
  • the access control rules may comprise and/or be combined to with item ownership and/or supervision.
  • the enablement and/or access control may be based on encompassing semantics and/or further more localized associated semantics (e.g. “tea” encompasses more localized “tea brewing” etc.).
  • the enablement/disablement and/or allowed/blocked may be (hierarchically) intrinsic.
  • Capabilities and/or interests may be access controlled (e.g. to control matching); thus, only particular semantics and/or semantic identities may have access to capabilities and/or interests.
  • Jane publishes “brew tea in 30 secs for/at 50c/50W(h)” to be accessible and/or available to a "person possessing and/or carrying Health Affairs”.
  • John may control and/or override within his endpoint the accessibility, publishing and/or diffusion to/of the capability; the control and/or override may entail enable/disable/allow/deny and/or specifying more localized access control, diffusion and/or publishing encompassing more localized semantic identities(e.g. “a nurse carrying Health Affairs”, “a nurse reading Health Affairs” etc.).
  • an interest associated with such a capability may index a goodwill and/or budget based on (projected) endpoint semantics and/or (semantic) time; as such, the 50c/50W(h) budget may be indexed based on (semantic) time (e.g.30 sec, MEETING JANE + 30secs etc.) - 60 - LUCM-1-1055Spec [00398] Semantic times may be specified, organized and/or published in a hierarchical manner. In some examples, the (semantics associated/identifying with) encompassed semantic times are associated with a more specific localized and/or lower drift semantics (e.g.
  • semantic identities associated with semantic identities, objects, artifacts, assets, agents, themes etc.
  • semantic identities associated with semantic identities, objects, artifacts, assets, agents, themes etc.
  • they may be published, accessed and/or inferred based on the semantic hierarchy of semantic groups and/or supervisory/ownership hierarchies.
  • Goal based inferences allow the system to determine semantic routes, trails and/or budgets.
  • Semantic routes are used for guiding the inference in a particular way. In an example, a user specifies its own beliefs via language/symbology and the system represents those in the semantic model (e.g. using semantic routes, semantic groups etc.).
  • the semantic inference based on semantic routes may be predictable and/or speculative in nature.
  • the predictability may occur when the semantic routes follow closely the semantic trails (portions of the history of semantics inferred by the system). Alternatively, the system may choose to be more pioneering to inferences as they occur and follow semantic trails less closely. In an example, a car may follow a predictive semantic route when inferring “ENGINE FAILURE” while may follow a more adaptive semantic route when inferring “ROLLING DANGER”.
  • the predictability and/or adaptivity may be influenced by particular semantic budgets and/or factors. [00402] Such budgets and/or factors may determine time management and/or indexing rules.
  • the system infers/learns a semantic time rule and/or indexing factor based on low inferred predictability factor wherein the inference on a semantic artifact is delayed until the predictability increases.
  • the system identifies threats comprising high risk artifacts in rapport to a goal.
  • the system may increase speculation and/or superposition in order to perform inference on goals such as reducing threats, inconsistencies, confusion and/or their risk thereof; in case that the goals are not achieved (e.g. factors not in range) and/or confusion is increasing the system may increase dissatisfaction, concern and/or stress factors.
  • the system may factorize dissatisfaction, stress and/or concern factors based on the rewards factors associated with the goal and the threat/ inconsistency risk factors. It is to be understood that such factors and/or rules may be particular to semantic profiles and/or semantic views. In some examples the threats and/or inconsistencies are inferred based on (risk) semantic factors (e.g. risk of being rejected, risk of not finding an article (at a location) etc.). - 61 - LUCM-1-1055Spec [00404] When the system follows more predictable routes and the projections do not match evidential inference the system may infer and/or factorize dissatisfaction, concern and/or stress factors based on semantic shifts and/or drifts.
  • Dissatisfaction, concern and/or stress factors may be used to infer semantic biases and/or semantic spread (indexing) factors and, further, the system may infer semantic (modality) augmentation in order to reduce such dissatisfaction, concern and/or stress factors.
  • the augmentation may be provided and/or be related with any device based on circumstantial inference and/or semantic profiles.
  • a detected sound e.g.
  • tactile actuators may be inferred to be used to alter and/or divert the inference on the sound receptor trails to tactile trails and to further increase the semantic spread and thus potentially reducing the concern and/or stress factors. It is to be understood that the system may monitor the dissatisfaction, concern and/or stress factors correlated with the augmentation artifacts applied to reduce them and further perform semantic learning based on correlation.
  • the system may infer, adjust and/or factorize likeability, preference, satisfaction, trust, leisure and/or affirmative factors based on high (entanglement) entropy inference in rapport with (higher) dissatisfaction, concern and/or stress artifacts and vice-versa.
  • Confusion may decrease as more semantic routes/trails and/or rules are available and/or are used by the system.
  • Confusion thresholds may shape semantic learning. Thus, lower confusion thresholds may determine higher factorizations for a smaller number of routes/trails and/or rules associated to (past and/or future) (projected) inferences.
  • Higher confusion thresholds may determine lower factorizations for a larger number of routes/trails and/or rules associated to (past and/or future) (projected) inferences.
  • the superposition may increase as the evidence inference comprises more semantic spread.
  • the assessment of evidence e.g. truth artifacts (provided) in the semantic field and/or flux) may be more difficult as the existing highly factorized artifacts are fewer and they may shape fewer highly factorized inferences with less semantic spread and decreased superposition.
  • Dissatisfaction, concern and/or stress factors may increase if higher factorized semantic artifacts in the inferred (projected) circumstances do not match evidence and/or evidence inference leads to confusion.
  • Dissatisfaction, concern and/or stress factors may be used to index and/or alter factorizations of the semantic artifacts used in evidence inference, in order to decrease such factors in future inferences, based on evidence inference and/or challenges (e.g. flux, user etc.).
  • the system may infer goals such as maintaining and/or gaining leadership which might signify involvement and/or importance in (group) decision making and further factorizations of dissatisfaction, concern and/or stress factors.
  • Increase in dissatisfaction, concern and/or stress factors may signify that the (group) pursued goals where not optimal. Further, such inferences may determine adjustments of routes, rules and/or further artifacts including factorizations of leadership, groups and/or semantic fluxes.
  • Predictability and/or speculative factors inferences may be associated with factors related to dissatisfaction, concern and/or stress factors (e.g. they may alter semantic spread).
  • the semantic route may be represented as a semantic artifact (e.g. semantic, semantic group) and participate in semantic analysis and semantic modeling.
  • Semantic route collapse occurs when during an inference the semantic engine determines (through generalization and/or composition for example) that a semantic route can be represented in a particular or general context through a far more limited number of semantics that the route contains. With the collapse, the system may create a new semantic route, it may update the initial semantic route, it may associate a single semantic associated with the original semantic route.
  • the system may inactivate and/or dispose of the collapsed semantic route if the system infers that are no further use of the semantic route (e.g. through semantic time management and/or expiration).
  • the semantics that may result from a route collapse may be compositional in nature.
  • the semantic engine may update the semantic rules including the semantic factors and as such it loosens (e.g. decaying) up some relationships and strengthen (e.g. factorizing) others.
  • the system creates and/or updates semantic groups based on semantic route collapse. Further, the system may collapse the semantic model artifacts (e.g.
  • Semantic route collapse may determine semantic wave collapse (e.g. low modulated semantic wave) and vice-versa.
  • Semantic wave collapse may depend on the frequency of electromagnetic radiation received by semantic systems, components, endpoints and/or objects. In an example, composition and collapse doesn’t happen unless the electromagnetic radiation frequency reaches a threshold which further allows (the semantic unit, object’s semantic wave) the gating/outputting of semantics. In some examples the threshold frequency is associated with the minimum electromagnetic frequency generating photoelectrons emissions (e.g. by photoelectric effect).
  • the system builds up the semantic routes while learning either implicitly or explicitly from an external system (e.g. a user, a semantic flux/stream).
  • the build-up may comprise inferring and determining semantic factors.
  • the semantic routes may be used by the semantic system to estimate semantic budgets and/or semantic factors. The estimate may be also based on semantics and be associated with weights, ratings, rewards and other semantic factors.
  • the semantics that are part of the semantic route may have semantic factors associated with it; sometimes the semantic factors are established when the semantic route is retrieved in a semantic view frame; as such, the factors are adjusted based on the context (e.g. semantic view frame factor). While the system follows one or more semantic routes it computes semantic factors for the drive and/or inferred semantics. If the factors are not meeting a certain criterion (e.g. threshold/interval) then the system may infer new semantics, adjusts the semantic route, semantic factors, semantic rules and any other semantic artifacts. [00423] Sometimes the system brings the semantic route in a semantic view frame and uses semantic inference to compare the semantic field view and the semantic view frame.
  • a certain criterion e.g. threshold/interval
  • the system may use semantic route view frames to perform what if inferences, pioneer, speculate, project and optimize inferences in the semantic view.
  • a plurality of routes can be used to perform semantic inference and the system may compose inferences of the plurality of routes, based on semantic analysis, factors, budgets and so on.
  • the analysis may comprise semantic fusion from several semantic route view frames.
  • the - 64 - LUCM-1-1055Spec semantic route does not resemble the expected, goal or trail semantics and as such the system updates the semantic routes and trails, potentially collapsing them, and/or associate them with new inferred semantics; additionally, the system may update the semantic factors, update semantic groups of applicable semantic routes and any other combinations of these factors and/or other semantic techniques.
  • the system learning takes in consideration the factorization of semantic rules and/or routes; thus, the learned semantic artifacts may be associated with such rules and factors (e.g. “DRIVE IN A TREE” has a high risk and/or fear factor etc.).
  • semantic artifacts are compared and/or associated with the hard semantic routes and/or artifacts; the inferred semantic artifacts may be discarded instead of learned if they make little sense (e.g. prove to be incoherent and/or highly factorized in relation with particular stable, factorized, high factorized semantic trails/routes, semantic drift too high etc.).
  • the system receives and/or infers a composite semantic comprising a potential semantic goal and an associated entangled (consequence) semantics (e.g. having high/low undesirability/desirability factors) for pursuing/not-pursuing and/or meeting/non-meeting the goal (e.g. JUMP THE FENCE OR GO BUST, JUMP THE FENCE AND GO TO EDEN, JUMP THE FENCE AND GO TO EDEN OR GO BUST); further, the entangled semantic artifact may determine adjustment of the goals factors (e.g. risk, weight, desirability etc.) and further projections.
  • entangled semantic artifact may determine adjustment of the goals factors (e.g. risk, weight, desirability etc.) and further projections.
  • the entanglement entropy is high due to consequences having a high relative semantic entropy (in rapport with the goal and/or in rapport to each other, they are being quite different even opposite or antonyms).
  • the entangled consequence can be similar and/or identical with the goal (e.g. GO BUST OR GO BUST) and as such the entanglement entropy is low.
  • the entanglement entropy may be associated with the semantic factors inference (e.g. when the entanglement entropy is high the factors and/or indexing may be higher).
  • EDEN may activate different leaderships based on semantic analysis and/or semantic profiles.
  • the previous inferences and/or profiles may have been related solely with EDEN a town in New York state and hence the semantic route associated with EDEN, TOWN, New York may have a higher semantic leadership than EDEN, GARDEN, GODS.
  • the EDEN, GODS may bear a higher semantic leadership than EDEN, TOWN.
  • the confused system may challenge the user - 65 - LUCM-1-1055Spec and/or other fluxes (e.g.
  • the leadership semantics may be based on inferences and/or semantics associated with endpoints, links, locations, semantic groups and/or further semantic artifacts associated with the subject (e.g. challenger, challenged, collaborator, user, operator, driver etc.).
  • Semantic drift shift and/or orientation may be assessed based on semantic entropy and/or entanglement entropy.
  • semantic entropy and/or entanglement entropy may be based on semantic drift, shift and/or orientation.
  • the system may assess whether the collapsible semantic is disposable possible based on semantic factors and decaying; if it is, the system just disposes of it. In the case of semantic wave collapse it may reject, filter or gate noisy and/or unmodulated wave signal.
  • Sometimes the disposal is deferred based on semantic time management.
  • the system continuously adjusts the semantic factors and based on the factors adjusts the routes, the semantic rules, semantic view frames and so on. If the factors decay (e.g. completely or through a threshold, interval and/or reference value) the system may inactivate, invalidate and/or dispose of those artifacts.
  • new semantic artifacts may be associated with highly factorized routes based on the activity associated with the route and thus the new semantic artifact may be also highly factorized and/or retained longer (e.g. in semantic memory).
  • a highly factorized semantic artifact when associated with a semantic route determines the higher factorization and/or longer retainment of the semantic group.
  • Semantics are linguistic terms and expression descriptive and indicative of meanings of activities on subjects, artifacts, group relationships, inputs, outputs and sensing.
  • the representation of the semantics in the computer system is based on the language of meaning representation (e.g. English) which can be traced to semantics, semantic relationships, and semantic rules.
  • the relationship between the languages is represented through semantic artifacts wherein the second language components are linked (e.g. via a first language component into a semantic group) with the first language; sometimes, the system choses to have duplicated - 66 - LUCM-1-1055Spec artifacts for each language for optimization (e.g.
  • the system has a semantic group of associated to CAR comprising GERMAN AUTO, SPANISH COCHE, FRENCH VOITURE.
  • GERMAN AUTO When performing translation from the language of the meaning representation to GERMAN the system uses the GERMAN as a leadership semantic and thus the system performs German language narrative while inferencing mostly in the language of meaning representation (e.g. English).
  • the system may optimize the GERMAN narrative and inference by having, learning and reorganizing the particular language (e.g.
  • GERMAN GERMAN semantic waves, semantic artifacts, models and/or rules as well so that it can inference mostly in German as another language of meaning representation (e.g. besides English). It is to be understood that the system may switch from time to time between the language drive semantics in order to inference on structures that lack in one representation but are present in another and thus achieving multi-lingual, multi- custom, multi-domain and multi-hierarchy inference coverage.
  • the system may infer and/or use multi-language and/or multi-cultural capabilities of collaborative fluxes (e.g. monocultural, multicultural) and/or associated factors.
  • the system may maintain particular semantic artifacts for particular contexts.
  • semantic artifacts associated with a drive semantic of BEST FRIENDS FROM SCHOOL may have associated slang and/or particular rules and artifacts that drive semantic inference and narrative in a particular way.
  • the semantics may be associated with patterns, waveforms, chirps.
  • the semantics may be associated with parameters, inputs, outputs and other signals.
  • semantics are associated with a parameter identifier (e.g. name) and further with its values and intervals, potentially via a semantic group.
  • the semantic factors may represent quantitative indicators associated to semantics.
  • the semantic system may use caching techniques using at least one view frame region and/or structure to store semantics.
  • semantic expiration the semantics may expire once the system infers other semantics; that might happen due generalization, abstraction, cross domain inference, particularization, invalidation, superseding, conclusion, time elapse or any other process that is represented in the semantic model. Processes like these are implemented through the interpretation of the semantic model and semantic rules by the semantic engine and further semantic analysis.
  • the semantic inference may use semantic - 67 - LUCM-1-1055Spec linguistic relations including semantic shift, entailment, synonymy, antonymy, hypernymy, hyponymy, meronymy, holonomy, polysemy.
  • Semantic techniques and interdependencies may be modeled within the inference models and semantic rules.
  • polysemy is modeled via semantic composition where the meaning of a polyseme is inferred based on the compositional chain.
  • semantic groups, semantic rules and semantic models may be used to represent semantic dependencies and techniques.
  • Semantic techniques may be implemented via semantic models including semantic attributes and semantic groups.
  • a semantic group containing all the synonyms for “great” is stored.
  • the group comprises semantic factors assigned to semantic components to express the similarity within a group or with the semantic attributes defining the group.
  • the source of information may be assigned semantic factors (e.g. associated with risk) and as such the inference by a system that consume semantic information from the source may be influenced by those factors.
  • the factors can also be assigned to particular semantics, type of semantics (e.g. via semantic attributes), themes and so forth that can be found in the fluxes and streams.
  • Semantic fluxes and streams may be represented as identifiers and/or semantics (e.g. based on annotating them in particular or in general based on a characteristic by a user) and/or be organized in semantic groups as all the other artifacts.
  • the system may use semantic time management (e.g. rules, plans etc.) to manage the semantic factors for the semantic fluxes and streams.
  • semantic time management e.g. rules, plans etc.
  • semantic systems it is desirable that systems be easily integrated in order to collaborate and achieve larger capabilities than just one system.
  • the advantage of semantic systems is that the meanings of one system behavior can be explained to a second collaborative system through semantic means.
  • system A provides and interface and is coupled to system B through some means of communication then the semantic coupling may consist in making system A operational and explaining to system B what the meaning of the inputs/outputs from system A in various instances is.
  • the system B may use sensing and semantic inference to infer the meaning of the received signal from system A.
  • the system A and B can have one common semantic point where the systems can - 68 - LUCM-1-1055Spec explain to each other what the meaning of a certain input/output connection mean at some point.
  • system A and system B are coupled through a common semantic point and also have other signaling and data exchange interfaces between them then when a signal is sent from A to B on an interface, the common semantic point from A to B will explain the meaning of the signal from A to B.
  • the systems A and B are coupled through a semantic stream wherein the common semantic point comprises the semantic flux.
  • the system B may use its own inference model to learn from the ingested data from system A; further, the system B may send his interpretation (e.g. via model) back to A; the system B may just use the semantic meaning provided by system A for interpreting that input/output signal/data or use it for processing its own semantic meaning based on semantic inference, processing and learning techniques.
  • the system B will ask/challenge the system A about what the meaning of a signal is.
  • the semantic fluxes that connect A to B make sure that the semantics are requested on system B from system A when their validity expire.
  • the system B may be proactive in sending those requests and the system A may memorize those requests in semantic routes groups and/or views and process them at the required time.
  • the system may use the semantic budgets for transmission through the semantic network and the semantics may expire in the network once budget is consumed.
  • semantic group resonance may be applied for faster learning (e.g. of semantic groups and/or leadership), safety, communication and/or further inferencing.
  • semantic group resonance system A induces coherent inferences at B (e.g. affirmative toward the goals of B); further, system B induces coherent inferences at A (e.g. affirmative towards the goals of A).
  • semantic group resonance allows (continuous) coherent inferences with potential low/high (entanglement) entropy of A and B while increasing superposition.
  • Semantic group resonance with low (entanglement) entropy is associated with affirmative factors; analogously, semantic group resonance with high (entanglement) entropy is associated with non-affirmative factors.
  • Semantic group resonance factors may be quantified in an example through low confusion, dissatisfaction, concern and/or stress factors between the members of the group and it may collapse when decoherence (e.g. high incoherence, confusion, dissatisfaction, concern and/or stress between the members of the group) occurs.
  • Semantic groups resonance determines and/or is associated with low confusion, dissatisfaction, concern and/or stress factors.
  • - - LUCM-1-1055Spec [00451]
  • the semantic time between resonance and decoherence may be used to infer coherent artifacts and/or operating points/intervals. The system may learn causality (e.g.
  • the system infers DO/ALLOW rules and/or further rules (e.g. time management/ factorization/indexing etc.) when affirmative resonance occurs, and/or DO NOT/BLOCK rules and/or further rules when affirmative decoherence occurs.
  • DO NOT/BLOCK rules and/or further rules e.g. time management/ factorization/indexing etc.
  • DO/ALLOW rules and/or further rules e.g. time management/ factorization/indexing etc.
  • damping may be learned by the system; as such, indexing and/or decaying factors and further rules may be learned based on resonance and/or decoherence (factors) and be associated with damping semantic artifacts.
  • the system learns damping factors and/or rules within the semantic mesh associated with the absorption and scattering of electromagnetic radiation in elements and/or (semantic) group of elements.
  • Damping rules and artifacts are used to infer hysteresis and vice versa. They may be used for adjusting factors, budgets and or quanta in order to control the damping towards goals and/or keep (goal) semantic inference within a semantic interval.
  • Damping rules may be used for example to control the damping components (e.g. of shocks, electromechanical dampers etc.) of a drivetrain (e.g. of posts, vehicles etc.).
  • system A uses semantic artifacts associated with system B (e.g. (portions of) semantic trails, routes, rules, drives, goals and/or orientations etc.) to induce coherent and/or resonant inferences at B and/or reduce confusion at B; this pattern may associate A as a (group) leader.
  • Semantic resonance is high for coherent semantic groups (e.g. the resonant inference in the group does not incoherently collapse). Semantic resonance is low for incoherent semantic groups and/or low coherency semantic groups.
  • the system may infer highly coherent composite goals for coherent semantic groups.
  • the system may use projected resonance on (target) artifacts (e.g. flux, user, patient etc.) and/or groups thereof in order to diffuse, attract, group, increase positiveness and/or to decrease dissatisfaction, concern, stress etc.
  • Projected resonance between (high entanglement entropy) semantic groups may be used to learn damping, hysteresis and/or further rules.
  • - 70 - LUCM-1-1055Spec Model and sub-model distribution/exchange may occur between system A and B. This exchange may be controlled (e.g. allowed, blocked, blurred and/or diffused) via semantic access control and gating.
  • semantic groups related to MRI EXAMS may be blurred; while the system may blur the entity/object groups (e.g. patients, images, patient-images etc.), other semantic groups (e.g. related with language interpretation) may be allowed to pass; alternatively, or in addition, the system may use semantic diffusion in order to convey information in a controlled fashion.
  • the semantic gating is based on semantic budgeting inference and/or speculative inference.
  • a semantic flux B might expose to flux A the semantics (e.g. potentially marked semantics) and the semantic capabilities potentially with estimated budgets and the flux A performs semantic inference on gated semantics and flux B exposed semantics.
  • the system A may choose to filter or reroute the semantics that do not meet the requirements. Entity and language filtering and semantic gating may be combined in any way to allow/deny transfer of information between systems.
  • entity and language filtering and semantic gating may be combined in any way to allow/deny transfer of information between systems.
  • two communicating systems may use explanatory protocols and/or interfaces; as such, a memory conveyed through a first mean is explained and/or reinforced through another mean.
  • the system B may maintain semantics from A and the system keeps semantic factors associated with them that may decay in time. Sometimes, the system B sends the requests to system A when the factors decay, reach a specific threshold and/or based on semantic budgets.
  • An adaptive approach of communication learning may involve a system B learning at first from a system A about the data is conveying and updating its semantic model in order to be able to infer semantics based on that data.
  • the system B learns a new language based on learning interfaces.
  • the learning interface relies - 71 - LUCM-1-1055Spec on common system A and B observations (e.g. sensing, semantic wave) and potentially basic rules and models for inference learning.
  • the implementation of interface learning may be achieved via a semantic point where the interface is described via a language or semantic wave.
  • the semantics of the interface and the relationships can be modeled via a tool that will generate a semantic plug-in model for the interpretation of the interface inputs.
  • the semantic tool and/or plug-in allows the description of the interface based on semantic rules including management rules.
  • the plug-in model may then be deployed to the connected systems and the connected systems use it for semantic connection.
  • the plug-in model may be deployed as part of a separate block circuit and/or semantic unit that connects the systems.
  • the plugin may be deployed in a memory (e.g. flash, ROM, RAM etc.).
  • the plugin modules may comprise encryption capabilities and units whether semantic or not. In some examples the plugin modules are used to encrypt and/or modulate semantic waves.
  • the encryption and/or modulation can be pursued in any order using semantic analysis techniques.
  • the semantic connection e.g. semantic flux
  • the semantic connection may be controlled through a semantic gate that allow controlled ingestion or output of information, data and/or signals through semantic fluxes and/or semantic streams.
  • elements e.g. semantic units
  • links/semantic fluxes As illustrated in Fig.16, a plurality of elements (semantic units) are labeled with letters A through W. Each of the elements may comprise computing and/or memory components.
  • Fig 16 further depicts semantic groups of elements in a hierarchical structure (e.g.
  • Group 1:1 (which is defined by the perimeter formed by G-H-I-J-K-L), 1:2 (formed by elements A-B-C-D-E-F), 1:3 (formed by elements M-N-P-O), 1:4 (formed by N- V-W-O) at level 1;
  • Group 2:1 (formed by N-V-U-T-S-R-Q-O, further indicted by thicker connecting perimeter line), 2:2 (indicated by thicker connection line joining A-F-G-H-I-J) at level 2); it is to be understood that while only two hierarchical levels are depicted, more levels may be present.
  • semantic fluxes and/or semantic streams are ingested by systems and possibly interpreted and/or routed based on semantic analysis.
  • Fig.20 illustrates one example, and as discussed further below a plurality of semantic units may be arranged such as semantic units SU1 through SU9.
  • One or more external signals, e.g. 68a, 68b may be received by one or more of the semantic units.
  • the semantic units are linked to one another in a mesh through semantic flux links, e.g., L1 through L19. - 72 - LUCM-1-1055Spec [00467]
  • the semantic gate may filter the semantics in exchanges.
  • the semantic gate may be controlled and/or represented by a set of access control, time management, rating, weighting, reward and other factor rules collectively named semantic management rules; access control, time management, rating, weighting and reward rules are comprised in patent publication number 20140375430.
  • the semantic gate may allow adaptive control of the exchange of information anywhere between a very fixed controlled environment and a highly dynamic adaptive environment.
  • the semantic gate may contain rules that block, allow or control the ingestion of particular semantic artifacts based on access control rules.
  • the endpoints of a semantic flux (e.g. source and destination) may be represented in a hierarchical semantic network graph and the semantic flux being associated with links in the graph.
  • the source and destination may be associated with semantics and the semantic gate control rules are specified based on these semantics; in an example, such semantics are associated with activities and/or locations and they may be collaboratively or non-collaboratively semantically inferred. Such semantics may be assigned to various artifacts manually, through semantic inference, through authentication or a combination of the former.
  • the semantic gate may be used to control the information flow between any of the elements of the graph and/or between hierarchies.
  • the graph elements and hierarchies are associated with semantics and as such the semantic gate controls the semantic flow based on such semantics.
  • the access between hierarchies is based on access control rules; as explained above the hierarchies may be associated with semantics and/or be identified by semantics. Further, access control rules may be associated with semantic identities and/or further identification and authentication techniques. In some examples, the identification and authentication are based on semantic analysis and/or sensing comprising data ingestion, image/rendering/display capture, radio frequency, electromagnetic modalities and/or other modalities/techniques. [00470] Information flows within and/or between semantic network model artifacts are controlled based on semantic gating. In some examples, information transfer flow between linked endpoints mapped to display interface areas, semantic groups and/or user interface controls is enforced this way.
  • the gating is coupled and/or based on the hierarchical inference within the semantic network model and/or semantic views which provide contextual localization pattern, access control and semantic intelligence pattern of the mapped areas, semantic groups and/or user interface controls.
  • the mapped areas may comprise - 73 - LUCM-1-1055Spec for example displayed text, user interface artifacts, controls, shapes, objects and/or a combination thereof; also, they may comprise and/or be associated semantic groups, semantic identities and/or patterns of displayed text, user interface controls, shapes, objects and/or a combination thereof.
  • the system may create groups, use fluxes and/or allow the flow and/or assignment of information from one mapped artifact to the other only if the semantic gating would allow it.
  • Linked semantic artifacts may be inferred based on semantic analysis.
  • the system infers the purpose and/or goal of artifacts and/or semantic groups in at least one semantic identified area (e.g. window) and may link such artifacts based on similarity of purpose, goal and/or further inference.
  • the linked artifacts may be inferred and/or mapped by selecting, dragging and/or overlaying the semantic areas and/or mapped artifacts on top of each other via any I/O (e.g.
  • semantic groups of artifacts are created by selecting, dragging and/or overlaying the semantic areas and/or mapped artifacts on top of each other and the user is prompted with selecting and/or confirming the (composite) semantic artifacts (e.g. semantics, semantic gating rules, semantic routes, profiles and/or further artifacts) for such semantic groups (e.g. between the group members or with group external artifacts).
  • semantic artifacts e.g. semantics, semantic gating rules, semantic routes, profiles and/or further artifacts
  • a received input may not be ingested or partially ingested if the semantic engine infers a semantic that is forbidden by the semantic gate.
  • a partial semantic determination occurs when some of the semantics are partially inferred on a partial analysis of a semantic route, goal and/or budget; sometimes those semantics are discarded and/or invalidated. However, other times those semantics may not be discarded or invalidated; instead they may be assigned a factor and/or time of expiration or a combination of those.
  • Such partial inference may be useful for example in transfer inference and learning.
  • semantic trails and/or routes associated with semantics in a domain may be partially applied and/or associated to semantic artifacts in other domains based on higher hierarchy inference on the semantic model.
  • Decaying and semantic expiration may be used for controlling a semantic gate.
  • the semantic analysis may be used to update the semantic factors and time management and update the dynamic of semantic gates.
  • - 74 - LUCM-1-1055Spec [00474]
  • the semantic gates may be plugged in to the semantic analysis and/or utilize semantic network models where endpoints represent the source (or a source group) and destination (or a destination group) of semantic fluxes.
  • Source groups and destination groups are represented as semantic groups.
  • a semantic group consists of at least two entities each being monitored in the semantic field that share a semantic relation or commonality via a semantic (e.g. semantic attribute).
  • a semantic group can be semantic dependent when a semantic attribute is assigned to specify a dependency or causality relationship within the group (e.g. A INFECTED B, JOHN PERFORMED MRI_EXAM) or, semantic independent when there is no apparent relationship between the objects other than a classification or a class (e.g. A and B are INFECTED systems).
  • A, B, MRI_EXAM may be on their own assigned to semantic groups, for example for storing signatures of viruses, images from MRI-EXAM etc.
  • the causality relationships and learning may depend on the semantic view and semantic view frames; further, they may depend on semantic field orientation and/or anchoring.
  • the observer’s A semantic view sees the effect of the sensor blinding on B as a result of a laser or photon injection at a later time than the system’s B semantic views detects such blinding effect.
  • the inference time and/or propagation (and/or diffusion) may be circumstantial at/between A and B, and thus, while the order of those collapsed inferences may be more difficult to project, they may be considered as entangled from particular semantic views (e.g. of an observer C).
  • systems’ projected inferences in regard to action/command/observations might comprise a high degree of certainty in relation with semantic artifacts which may be used as anchors for semantic orientation.
  • causality relationship may comprise additional information at a (hierarchical) level associated with the two entities (e.g. a link from A to B “sent malware because it is a slacker” and a link from B to A “this is a bully who’s probing me”, “this is a bully who infected me” etc.).
  • the causality specifies the cause effect of A INFECTED B; it is to be understood that this higher causality may be comprised, inferred, acknowledged and/or represented only for particular views and/or observers (e.g. B might not acknowledge or infer that it has been infected by A probing). It is to be understood that the cause-effect relationship (e.g. infected “because” is a bully) may be modeled as oriented links and used to explain “why” type questions (e.g. why A infected B ?
  • the propagation and/or diffusion between a first and a second endpoint is based on assessing the semantic drift and/or shift of/between the semantic artifacts associated with the endpoints; thus, the system may infer propagation and/or diffusion semantic rules (e.g. time management, access control, indexing, factoring etc.).
  • Semantic anchoring allows the system to determine a baseline for inference (e.g.
  • the anchoring may be based on a collection of artifacts and the system uses projected inference and semantic analysis based on such anchors. Further, the anchoring semantic artifacts may be determined by mapping and/or overlaying a semantic network sub-model, layer, shape, and/or template to a semantic network model (e.g. based on similar semantic based artifacts, artifacts with particular semantics -e.g. goal based, antonym, synonym, orientation based etc. – in both the base and the overlaid network model). The anchors may map and/or project into various hierarchies, semantic views and/or frames.
  • Anchoring may expire based on semantic analysis; once the anchors expire the system may invalidate corresponding semantic views, frames and/or regions.
  • Semantic anchors may be inferred based on leadership inference; further semantic diffusion and/or indexing may be used to expand or contract the anchors.
  • Semantic anchoring, drifts and/or indexing may change based on the orientation and/or intensity of the gravitational field within and/or associated with the semantic field and/or endpoint.
  • the semantic field is a higher hierarchical endpoint associated and/or comprising particular gravitational fields.
  • Semantic drifts may be inferred and/or associated with gravitational fields/waves and/or vice-versa; further, they may be associated with semantic time management.
  • Semantic anchoring may be indexed and/or change based on semantic drifts, semantic fields (and/or endpoints), gravitational fields and/or waves. In some examples the gravitational fields and/or waves are inferred using semantic sensing analysis.
  • the system represents the semantic groups in the semantic network model. In some example’s entities are stored as endpoints and relationships between entities are stored as links. The system may create, activate, block, invalidate, expire, delete endpoints and links in the semantic network model based on semantic analysis and semantic group inference. [00480] The system may use specific hierarchical levels to represent semantic groups of specific and/or leader semantic artifacts.
  • a semantic gate may control the flux between sources and destinations.
  • a semantic flux is an oriented flow which may be assigned to an oriented link.
  • a semantic gate and a semantic flux may be identified by at least one other semantic artifact (e.g. semantic).
  • the semantic gating may update the semantic model and management rules (e.g. collapse the semantic route and associate the collapsed semantic to a semantic rule).
  • the system may discard and reroute the semantic artifact, update/create a semantic rule (e.g. for source, factors); it also may infer additional semantics (e.g. associated with cyber security features for example).
  • a semantic rule e.g. for source, factors
  • additional semantics e.g. associated with cyber security features for example.
  • the system asks for feedback from a user or from other semantic hierarchies, domains and/or themes; in some examples it may use further semantic analysis of the semantic before feedback request (e.g. synonymy, antonymy etc.).
  • a semantic unit may ask a semantic flux cloud if a particular cyber physical entity is associated with HAZARD and/or, in other examples if the entity is associated with POISONED WATER.
  • the system may search or provide inference on semantic areas, domains and/or groups associated with semantic routes of HAZARDOUS POISON WATER and/or POISON WATER and/or HAZARDOUS WATER and/or HAZARDOUS POISON and/or further combinations of the semantics in the semantic route.
  • the interface between various components can be achieved in in a semantic way. As such the connection points and/or signals transmitted between various components can be semantically analyzed and/or gated.
  • a semantic gate may be represented as a circuit or component. As such, the semantic gate controls the signals received and/or transmitted between semantic components.
  • a semantic gate may allow only specific semantics/artifacts/themes/signals to pass through.
  • Semantic gating and flux signaling may be achieved by diffusive processes. Further quantum tunneling phenomena may be used.
  • a semantic cyber security component deployed on a hardware layout may be able to infer, identity, deter and block threats. Further, by being connected to a semantic - 77 - LUCM-1-1055Spec flux infrastructure and/or cloud is able to challenge (or ask for feedback) on particular cyber physical systems, semantics, semantic groups etc. and perform access control based on such information.
  • the system may detect that the inferences related with at least one collaborator and/or semantic group determine incoherent superposition.
  • the system may ask for feedback from other collaborators and/or semantic groups; the system may prefer feedback from entangled and/or conjugate collaborators and/or semantic groups (e.g. having particular entanglement entropies of composite semantic analysis).
  • the system may decay specific factors and/or semantics associated with the collaborators who determine, cause and/or infer incoherent superposition and/or high confusion.
  • Signal conditioning represents an important step in being able to eliminate noise and improve signal accuracy. As such, performing signal conditioning based on semantic analysis is of outmost importance in semantic systems.
  • the semantic conditioning means that semantics inferred based on received measurements and data including the waveforms, parameters, envelopes, values, components and/or units are processed and augmented by semantic analysis. Semantic signal conditioning uses semantic conditioning on unconditioned measurements and signals. Semantic signal conditioning also uses semantic conditioning to compose and/or gate conditioned and/or generated semantic waves and/or signals. Thus, the system is able to use semantic conditioning for a large variety of purposes including inference in a semantic mesh. [00492] In an example, the system conditions a received signal based on a modulated semantic wave signal.
  • the conditioning may take place in a semantic unit comprising a summing amplifier at the front end producing a composed and/or gated semantic wave signal.
  • the composition and/or gating is performed by modulating the output signal (e.g. voltage) based on the input signals (e.g. unconditioned signals 64, conditioned and/or generated semantic wave signals 65) to be added (as depicted in Fig.19 A B C).
  • the amplifier GAIN Rf 66, SU GAIN 67 may be also be adjusted based on semantic artifacts (e.g.
  • semantics, semantic waves etc. and/or be in itself a semantic unit (SU GAIN); adjustments of the gain may be used for access control and/or gating purposes in some examples wherein the output voltage may be adjusted to account for allowable transitions and/or semantics.
  • an amplifier has been used in examples, it is to be understood that in other examples additional and/or alternative analog and/or digital voltage - 78 - LUCM-1-1055Spec adders, operational amplifiers, differential amplifiers, analog blocks, digital blocks, filters and/or other components (e.g. as specified throughout this application) may be used.
  • the depicted examples may show physical and/or logical electronic components and/or blocks including capacitors, resistor, amplifiers, inductors, transistors, diodes and other electronic parts/units/blocks, it is to be understood that they may not be present in other embodiments or they may be substituted with other components and/or parts/units/blocks with similar or different functionality.
  • the capacitors C in Fig 19 might be missing altogether; further the amplifier A may be missing and thus, the front-end block might be purely a signal adder.
  • all resistances, capacitances, inductances and/or gain of components may be adjustable and the system may use semantic means (e.g. semantic modulated signals) to adjust such values and/or control components.
  • the switching (e.g. as provided by MUX) and variable GAIN functionality may be semantically controlled and may be used to implement semantic routing and/or gating. While in the depicted examples those functionalities are implemented in discrete components and/or blocks they may also be substituted and/or composed (e.g. physically; logically via semantic grouping and analysis) with other components and/or blocks and provide similar composite functionality.
  • the semantic unit inputs, outputs and/or gain units may be mapped to semantic fluxes and/or gates.
  • the system may use voltage and/or currents values to represent semantic artifacts.
  • variable voltages for modulating semantic signals
  • variable currents values may be used to modulate such signals and/or represent semantic artifacts.
  • semantic units may be used in a mesh in order to condition and/or analyze the signals potentially in a recursive manner where the generated semantic waves signals are used as conditioning signals in the semantic mesh (e.g. mapped to a semantic network model, semantic fluxes/gates mapped to semantic unit inputs/output/gain).
  • the mapping of the mesh to elements and routing is performed by semantic orientation and/or routing.
  • the semantic waves may be generated as explained throughout this application including those received from other sources, generated on previous received data, measurements and/or conditioning and/or other domain semantic artifacts.
  • Semantic waves waveforms and signals are used and/or stored in the system to represent any semantic artifacts. In some examples, they are used for identification purposes of any semantic artifact. In further examples, the identification may comprise any - 79 - LUCM-1-1055Spec combination of particular identification, semantics, semantic groups and/or other semantic artifacts.
  • the unconditioned signals may come from any entity including analog blocks, digital blocks, front ends, sensing elements, modulation elements, I/O elements or any other hardware element. In some examples, the unconditioned signals are based on AC currents from power lines.
  • the semantic system infers semantics on patterns and compositions.
  • the system detects the pattern for a sensed semantic (e.g. ingested via optical or sound sensing entities) which is coupled to another pattern in a semantic view (e.g. image reconstruction pattern, artifact reconstruction or pattern based on semantic group of attributes etc.).
  • the semantic system may infer a semantic based on a partial signal pattern; the signal pattern may present some partial resemblance with a pattern represented in the semantic system; the system may assign a factor to the new inferred semantic based on a correlation between the actual and resembled pattern.
  • semantic waves may be analyzed based on partial signal patterns.
  • the system may use semantic analysis including orientation and routing for pattern recognition and learning.
  • Semantic wave signals are generated and/or modulated through semantic analysis (e.g. composition).
  • the semantic waves are modulated based on an identification, signature and/or DNA of semantic units and/or gates through which they are routed and pass through.
  • an unconditioned signal originated from at least one sensor element is modulated with the identification, signature and/or DNA of the endpoints and/or semantic units through which is routed, and it passes.
  • the DNA may comprise semantic artifacts related with the respective endpoints, semantic units, semantic groups and/or hierarchies.
  • the system may use sequences of semantic units to infer composite semantics and modulate the semantic wave.
  • sequences of semantic units such as SU1, SU2 then the system may modulate the semantic wave with a composite signature (e.g.
  • the unit SU3 is a border semantic unit between multiple semantic stages and/or hierarchical levels (e.g. Level1 and Level2) and/or semantic stages and thus the collapsed signature (DNASEQ3-Level2) may be available, collapsible or inferred only at Level2 and/or beyond but not at Level1.
  • Endpoint DNA may be replicated with endpoint replication.
  • the inference at an endpoint is incoherent, confused, non-collapsible and/or not matching the endpoint DNA, capabilities, goal and/or purpose; thus, the system may replicate the endpoint together with the DNA until the coherency and/or confusion of the goal and/or purpose is restored.
  • the system may remap the endpoint to endpoints (and/or groups thereof) with similar DNA. It is understood that the endpoint may be replicated and/or mapped/re-mapped on an existing and/or new semantic unit. Thus, semantic identities and/or further artifacts may be associated with DNA signatures.
  • DNA signatures compose during endpoint fusion. DNA signatures may be used to establish and/or infer anchors.
  • DNA based techniques may be used with medical imaging sensors (e.g.
  • modalities such as CT (computed tomography), MRI (magnetic resonance imaging), NM (nuclear medicine), US (ultrasound) etc.) and/or biological sensors in order to model, detect and/or perform semantic augmentation in medical diagnosis, exams, clinicals, prevention, emergency, operating rooms and other healthcare based use cases.
  • biological sensors are part of a semantic unit, module and/or post; in further examples, they are wearable (e.g. surgical gloves, (exo) wearables, braces, bands etc.).
  • Semantic waves may comprise electromagnetic waves generated and/or modulated through semantic analysis.
  • Semantic waves may be modulated, transmitted and received in various environments and using various technologies including electromagnetic, radiative, non- radiative, wireless, wired, optical, electric etc.
  • semantic waves can be modulated and/or transmitted based on the electro-optic effect manifested by particular crystals which change the refractive index based on applied voltages and currents and thus modulating the signal by changing the wavelength of the light based on applied voltages.
  • the refractive index n of certain crystals such as lithium niobate depends on the strength of the local electric field. If n is a function of the strength of the field, then so is the speed and wavelength of the light traveling through the crystal.
  • Semantic waves may be used for semantic control of devices and/or analog blocks. In some examples the semantic waves are used for display purposes where the semantic wave is decoded at semantic display elements and the semantics rendered on the screen (e.g. RED 10 GREEN 5 BLUE 8, H 17 S 88 V 9).
  • the semantic wave is used in a scan type display unit where the semantic wave modulates scanning optical component for creating display artifacts; while the display artifacts may be raster, alternatively, or in addition they may be modeled and mapped as a semantic model and potentially stored in a semantic memory.
  • the system modulates and stores display artifacts and scenes as semantic models. Such semantic models may be modulated as semantic waves.
  • the system may perform semantic scene interpretation, composition and rendering based on superposition of semantic models and inference at multiple hierarchical levels.
  • the system may perform semantic wave conditioning and deconditioning when performing semantic scene interpretation, projections, composition and rendering.
  • the rendering may take place on display units it is to be understood that it may take place as a memory renderings or other analog and digital renderings.
  • the system is able to perform scene composition, rendering, projections and/or analysis at any time.
  • the renderings are relative to a perspective endpoint and/or link in the semantic space and the system performs orientation, factorization, indexing, analysis and/or rendering relative to the perspective artifacts (e.g. from perspective endpoint to - 82 - LUCM-1-1055Spec field, current endpoint to perspective endpoint, link orientation etc.); further, the renderings may be based on semantic routes and trajectories comprising perspective artifacts.
  • semantic waves are used for control plane purposes including pilot or control sequences.
  • turbo codes and low-density parity check techniques for error correction is well known in wireless communication. However, those techniques may require fast interleavers and lookup tables for data encoding and decoding.
  • a semantic wave the data is encoded based on semantics and as such the system is able to understand the signal even in most adversarial jamming conditions by adapting to environment. Further, error correction and cyber safety controls may be incorporated in a hierarchical manner and thus allowing hierarchical and/or domain coherent inferences.
  • semantic waves may be used to convey and/or transfer semantic network models and/or semantic rules.
  • Semantic information is mapped to artifacts such a frame or an image.
  • Semantic waves may be generated by semantic network models and/or rules while conveying a semantic network model and/or rule.
  • models and rules are generated based on recursive semantic analysis on semantic waves, models and rules and used for further generation of semantic waves.
  • at least two semantic waves are composed while the waves are modulated based on the cascading learning.
  • cascading semantic waves models and rules may be used in encryption and authentication schemes. Such schemes may be used for example in semantic model encryption and authentication, memory encryption, collaborative semantic authentication and validation and other applications.
  • Such semantic techniques may be associated with wavelets (e.g. wavelet compression, wavelet encryption).
  • the system reconstructs the frames and images using such techniques.
  • the frames and images are reconstructed based on the semantically encoded semantic network models conveying space, time, semantic attributes, hierarchy and other semantic artifacts.
  • frames and images are deconstructed and semantically encoded in semantic waves.
  • the semantic wave may travel over and between different networks encompassing various modulation and transport protocols.
  • the semantic wave is wavelet compressed before being transferred using such protocols.
  • the addressability within the semantic layer and/or networks may be based on semantic identification.
  • the system may perform gating on artifacts in images and/or frames based on semantic analysis. Further, it may generate artifacts in images/frames based on semantic analysis.
  • an access control rule on a semantic flux/gate may specify that it needs to invalidate, hide or filter objects in the pass-through images/frames.
  • the - 83 - LUCM-1-1055Spec system maps and/or identifies such objects in the semantic network model and invalidate, hide or filter corresponding artifacts of the semantic model, potentially based on further semantic analysis.
  • the semantic network model may be mapped based on a particular format of the image/frame (e.g. semantic artifact compression based on specific or standard formats); also, it may be mapped on a semantic waveform. While this is the faster approach, other variants may perform the mapping and the semantic analysis using semantic gating points and/or units.
  • semantic gating functionality may be incorporated into an I/O, control, sound/speech and/or display unit that render inferred semantics and/or semantic waves on a display and/or other sensory devices (speech, touch, vibration etc.).
  • gating rules are based on various semantic artifacts defining and/or guiding the gating inference.
  • the system may specify semantics that would replace the gated semantics in the resulted semantic waves or gated artifacts (e.g. images, frames, speech, signal etc.).
  • Semantic mapping, compression, semantic gating and/or semantic waving may be incorporated in devices whether they provide capture, recordings, feeds, display, renderings, I/O, sound, speech, touch, vibration. Further such techniques may be applicable to any analog and digital interfaces.
  • semantic waves might be modulated directly on or as a carrier wave, they may be transmitted through other mediums and interfaces (e.g. network) that require the modulation, encoding, segmentation etc. through their own communication protocols and communication links.
  • the system may fine-tune and adjust semantic factors and thresholds on signal conditioning elements to determine or infer a path.
  • the semantic conditioning may be associated with semantics related to signal elements including waveforms, envelopes, amplitude, phase, frequency and so on; the conditioning may be also associated with various modulations, formulas, algorithms and transformations. As such, the semantic system may adapt to various conditions and situations. [00525]
  • the semantic conditioning can be achieved via signal comparison, correction, correlation, convolution, superposition of a generated signal based on the conditioning semantic elements or other comparisons based on transformations and translations as wavelet, Fourier, Taylor and others. Sometimes the semantic conditioning doesn’t yield a good rating/factor and as such the system may generate and/or store additional semantic conditioning elements and rules learned during conditioning cycles.
  • the conditioning may be associated with inputs from other systems, sub-systems, sources and modules. Thus, the system computes the semantic signal conditioning patterns or chips including the conditioning waveform and timing based on collaborative and multi domain intelligence.
  • a conditioning waveform may be used in combination with a baseline waveform or a semantic wave to allow the adaptation of the system in different contexts and improve the accuracy, resilience and signal to noise.
  • the conditioning waveforms may be organized and represented as semantic artifacts including semantic routes, semantic trails, semantic groups, rules and so forth. When a semantic route is associated with a semantic network model it comprises a relative orientation and/or shape in a semantic network space.
  • the system may perform semantic orientation and/or shaping inference based on semantic routing, the identification of the network model artifacts (e.g. endpoints and links) in the shape and/or semantics associated with these artifacts.
  • the orientation may be in an example relative to other semantic routes or to semantic trails; in such an example the system may further perform semantic orientation inference based on the groups of routes/trails and associated semantic network artifacts (e.g. endpoints, links and/or semantic groups thereof, common semantic artifacts, links between routes, semantics, semantic groups, semantic waves etc.).
  • the semantic orientation may be associated with or used to determine relative or absolute semantic drifts and shifts, semantic groups and semantic shapes.
  • Absolute semantic drifts may use an absolute baseline in rapport to a semantic network space, semantic views, semantic view frames, semantic routes, semantic artifacts and/or a coordinate system.
  • the semantic system modulates/demodulates, filters and composes semantic waves and signals based on goals.
  • the goal may be of NEW COMPOSITION in a context of an environment which may generate a routes and drive semantics of AUTUMN, BROWN, FALLEN LEAVES, LATE, QUIET.
  • the NEW COMPOSITION may not benefit from much contextual environmental information and as such the system may pursue very general semantic routes.
  • the goals and indicators are too vague (e.g.
  • the system may ask for feedback and/or infer biases.
  • the feedback and/or bias may comprise semantics and further factors which may determine drive semantics, semantic routes and so on.
  • the system may group such biases and drive semantics with semantic routes and semantic orientation based on further factors and indicators of semantic inference (e.g. factors and indicators matching “belief” semantic routes or high-level semantic artifacts).
  • the system may use semantic - 85 - LUCM-1-1055Spec profiles.
  • the system may perform superposition reduction.
  • the system may perform new 2D and/or 3D designs based on semantic analysis and projections.
  • the user specifies the features that a bicycle rim may have and not have, and the system infers semantic shaping, semantic attributes and rendering of the rim parts and designs.
  • the system may perform the design of 3D bicycle components based on further semantic shaping and analysis inference.
  • Semantic orientation is related with semantic routing in a semantic network model where routes are mapped to various artifacts and hierarchies in the model.
  • the system may perform semantic artifact comparison and/or projections.
  • semantic shapes comprising one or more semantic routes and/or trails are compared allowing the system shape and object recognition.
  • the system uses at least two semantic routes to infer at least two semantics for a shape and perform composition and fusion on those.
  • the system may infer for a shape BLACK BOX 10 and LUGGAGE 4 and because there is a semantic route between BOX and LUGGAGE and between LUGAGGE and AIRPORT (e.g. the semantic associated with the endpoint where the observation occurs) then the system may infer BLACK LUGAGGE 7.
  • semantic view frames, views, models, sub-models, groups may be compared and/or projected based on semantic orientation.
  • a semantic shape comprises semantic artifacts in the semantic network space comprising the shape.
  • the semantic shapes allow meaning determination and inference in the semantic network space comprising semantic network artifacts.
  • the semantic shape comprises all endpoints and/or links associated and/or defined with particular semantic artifacts.
  • the semantic artifacts that define and/or are associated with the semantic shape may be semantics, semantic routes, semantic groups, drive semantics, goal semantics, indexing semantics and any other semantic artifact.
  • a semantic shape may be inferred based on such semantic artifacts and semantic analysis in the semantic network space.
  • the system infers further shape semantics based on the semantic analysis in the semantic shape.
  • a semantic shape may comprise adjacent, non-adjacent, linked or non- linked semantic network artifacts.
  • a semantic shape comprises endpoints, links and any combination of those etc.
  • semantic shapes can span multiple hierarchical layers.
  • a semantic shape inference is not limited to visual mapping modalities, but it may encompass other sensing types and modalities (e.g. - 86 - LUCM-1-1055Spec sound, tactile, pressure, radio frequency, piezo, capacitive, inductive, analog, digital, semantic flux, semantic stream and other signal modalities).
  • a semantic network shape space may resemble at least one layer of a hierarchical semantic network model with semantic shapes and links between them.
  • a semantic shape may represent a (linked) grouping of semantic artifacts (e.g. endpoints, links and/or semantic groups) in a potential hierarchical manner.
  • Semantic shapes may be mapped potentially to fields, data, graphics, images, frames, volumes, captures, renderings, meshes, fluxes, layouts, sensing and further artifacts used in semantic analysis.
  • the access to hierarchies and/or semantic shapes may be access controlled.
  • a semantic shape comprises at least one group of semantic artifacts comprised and/or defined by semantic routes potentially in a hierarchical manner; it is as such, that most of the inference techniques applicable to semantic routes and compositions as explained throughout this application can be used in a similar way for semantic shapes and/or to infer semantic shapes.
  • the system may pursue various semantic routes during semantic analysis.
  • the system may semantically analyze the inference on multiple semantic routes and determine semantic groups and inference rules based on the inference on those pursued routes. Further, the system may associate semantic shapes with such routes, inferences, groups and/or rules.
  • the system uses a higher semantic route of “LOW CLEARANCE” “SHAPE 1” and another one “FAST” “HIGHWAY” and the system associates the lower semantic shaping routes within the semantic model to at least one semantic group, drive semantic and/or shape of CAR and further, if additional related inference and/or feedback is available (e.g. inferring the brand logo, text, external input etc.) to a drive semantic and/or shape for DELOREAN.
  • the system may use various routes and/or rules for inference and augments the factors for the inferred semantics based on the semantic analysis on such routes.
  • different routes reinforce the factors of various semantic artifacts and thus a high-level semantic understanding is likely.
  • different routes determine factors to spread, decay and be non-reinforceable and thus higher-level understanding is less likely.
  • the system may pursue other routes and what if scenarios in order to achieve goals.
  • the semantic orientation and shaping may be based on semantics whether associated with semantic routes and/or semantic groups. The semantic orientation and shaping allows the driving of inference and selection of inference routes and rules based on a subset of drive semantic artifacts.
  • Semantic orientation and shaping uses semantic hierarchy for inference.
  • semantic groups of semantic model artifacts are grouped together in higher level hierarchy artifacts and the system performs orientation based also on the new hierarchy artifact.
  • Semantic orientation is used to group semantic artifacts together.
  • Artifacts are grouped based on semantic orientation and drift.
  • semantic routes themselves may be grouped.
  • Semantic routing may comprise semantic orientation and profiling for a semantic trail.
  • Semantic routing and orientation may use semantic drift assessment.
  • Semantic orientation, shapes and semantic drifts may be used to determine and categorize actions, behaviors, activities and so forth.
  • the system uses orientation and inference towards an action and/or command.
  • the system uses semantic orientation and semantic drifts to infer whether an inferred semantic is associated with an action, behavior and/or command.
  • Semantic routing, orientation, shaping, drifting and further semantic analysis e.g. hierarchical, semantic profiles, gated etc.
  • semantic analysis e.g. hierarchical, semantic profiles, gated etc.
  • the system may project and/or assess/reassess a (strategic) goal based on the projections and/or realization of sub-goals (and/or shorter term) goals.
  • the system may not alter the (strategic) goal and consider it achieved when all the sub-goals complete. However, if the semantic drift is large and/or sub-goals are not met then, the system may infer alternate projections and/or sub-goals; alternatively, or in addition, it may adjust, decay and/or invalidate the (strategic) goal. It is to be understood that the sub-goals may comprise shorter term goals which may be associated with semantic time management rules.
  • the adjustment of the goals/sub-goals is based on a lowest entanglement entropy, drifts, indexing and/or factorizations between the old and the new goals/sub-goals - 88 - LUCM-1-1055Spec and/or further semantic artifacts used in projections.
  • Competing requirements e.g. associated with various semantic profiles
  • the system may perform deep learning feature recognition (e.g.
  • Semantic network models use semantic gating for transferring information from one semantic unit and layer to another.
  • the system may infer that a shape is a DOOR LATCH based on the position relatively the door mapped semantic model which is at an endpoint that is high factorized for LATCH, LOCK semantics and routes.
  • the system recognizes NUMBER 9 on a BLACK SHAPE and associates the RAISED CONTOUR surrounding the number with BUTTON and further infer REMOTE CONTROL for the BLACK SHAPE; alternatively, or in addition the system may recognize REMOTE CONTROL first and subsequently NUMBER 9 and associates the RAISED CONTOUR comprising NUMBER 9 with BUTTON and further REMOTE-CONTROL BUTTON.
  • the system performs system inference using a plurality of routes drive semantics and hierarchy levels in the semantic model. It is understood that the system may use semantic identities moving together in the semantic space (e.g. BLACK SHAPE and BUTTON moving together at the same time in user’s hand) to infer further semantic groups and/or identities (e.g.
  • the system is able to infer and associate semantic identities in context (e.g. REMOTE CONTROL, REMOTE CONTROL BUTTON, NUMBER 9 ON REMOTE CONTROL BUTTON etc.).
  • the system infers and/or uses connection indicator and/or factors.
  • two endpoints and/or semantic shapes are associated each with WHEELS; and the system may infer a semantic group if the wheels are associated with similar and/or identical semantics, semantic routes, drives, orientations and/or groups within a semantic time.
  • the wheels may be comprised in a particular area, endpoint and/or other artifact.
  • the wheels move together and the semantic drift of their behavior (e.g. as inferred based on associated semantic routes and/or semantic views) is within a (coherency) range and/or semantic analysis is coherent.
  • the wheels are comprised and/or mapped to a linking endpoint and/or area (e.g. car chassis). - 89 - LUCM-1-1055Spec [00547] It is to be understood that the shapes and contours including numbers may be inferred through any techniques specified in this application including but not limited to sematic analysis, deep learning, semantic segmentation etc.
  • a conditioning waveform may be used as an encryption medium wherein the conditioning waveform is used to modulate the encryption of a composite data signal or semantic wave in an adaptive way based on semantic analysis.
  • the semantic engine may run on optimized semantic hardware. Such hardware may include ASICs, SoCs, PSOCs and so on.
  • a semantic system may perform evaluation, simulation, testing and/or automation of placements of components on a substrate, PCB or wafer based on semantic analysis including semantic shaping.
  • the semantic system may use a semantic network model which has a set of endpoints mapped to locations of at least one substrate, PCB or wafer and the system performs semantic inference based on the components and substrate capabilities (mapped to semantic attributes); further the system may represent component heating and its impacts via semantic models and semantic rules (e.g. heat semantics mapped to endpoints, semantic time management) ;further, communication protocols are mapped to a semantic model and semantic streams/fluxes.
  • the system may model many aspects of the design including cyber, performance, interference, power consumption, interface, radiation, leakage, heating and, thus, the system is able to determine the mapping of components/semantics/attributes to locations based on semantic inference and semantic network models.
  • the system may infer/simulate the mapping of those components and use the configuration that yields an optimized semantic model based on ratings, rewards, costs, risk or other factors and/or analyses as explained throughout the application.
  • the system may seek particular orientations of semantic routes for coupling and access (e.g. memory access) and perform analysis based on those routes coupled with previously mentioned analyses.
  • the components may include any electronic components and circuits, iCs, substrates, layers and so forth.
  • the hierarchy of the semantic network model may resemble the hierarchy of photolithographic layer imprints and a photolithographic semantic automation engine uses the semantic model to automate the process through actuation and hardware control.
  • the semantic system may be used to determine locations and automate any other processes including traffic control, robotic manipulation, image processing or any other system requiring space, time, access control coordination.
  • the system may extract metadata from various inputs, data and signals and assign semantics to it. Additionally, the system asks for feedback from another semantic - 90 - LUCM-1-1055Spec system; the request is submitted to the system with greatest rating in relation to the theme.
  • the challenge/response mechanism may be realized through semantic fluxes and be controlled through semantic gates and semantic rules.
  • groups of systems can develop group capabilities based on the explanation of the interfaces, where the groups and leaders determine affinities to each other based on semantic analysis.
  • the semantic model may be used to model equations or algorithms.
  • the system may update the equations and algorithms and apply the updated artifacts to semantic inference and data processing.
  • An equation and algorithm may be associated with a composite semantic artifact, collection of semantics, semantic groups and/or semantic routes.
  • sniffers, detectors and memory data may be used with semantic analysis to infer and learn patterns, semantic artifacts (e.g. indicators, routes, groups) of usual or unusual behavior pursued by malware.
  • deep packet inspections and/or protocol sniffers/detectors may be used and the semantic analysis would be performed on packet data and metadata in the protocols (e.g. source, destination, type of packet, packet sequence, flags, ports, offset, ack etc.).
  • the system is able to perform semantic inference related to cybersecurity by combining methods like these that detect malicious behavior with code execution, protocols or other cyber related artifacts.
  • the system may infer potential (attempt) (cyber) breaches if received and/or entered (e.g. by a user, operator, flux, group etc.) authentication information exhibit a high semantic drift and/or (entanglement) entropy in rapport with the current and/or historical legitimate authentication information.
  • a semantic controller may be used to control various hardware and/or software components based on inference.
  • the semantic controller controls a robotic arm.
  • the robotic arm 13 having an upper arm 13a and lower arm 13b as seen in Fig.1, which may be used for soldering and/or component placing on a substrate and/or board (e.g. PCB).
  • the semantic controller accesses and performs the specific actions at the soldering and/or component locations based on sensing, mapped semantic models (e.g. to substrate, layer etc.) and semantic analysis.
  • the semantic controller may be on another system, computer, component, program, task or semantic unit.
  • the component may include general computing components, real time components, FPGAs, SOCs, ASICs or any other general or specialized components capable of interpreting the semantic model.
  • the semantic controllers - 91 - LUCM-1-1055Spec may be networked together for improved knowledge sharing and synchronization.
  • the distributed processing system operates according with the distributed semantic model.
  • the distributed semantic model may be interconnected, transferred and developed using many techniques some which are described in this disclosure including but not limited to semantic flux, semantic gate, semantic streams etc.
  • the semantic controller may be used as a cybersecurity component in the sense that will allow the usage of the system’s resources by the program based on the semantic model and multi domain semantic analysis.
  • the semantic model may include preferred semantic routes, while other semantic routes are deemed risky, hazardous or not allowed.
  • the system enforces the security of the system by controlling/denying access and taking actions for the inferred semantics or semantic routes that are hazardous or not allowed. Semantics and factors associated to access control rules can be used for inferring, allowing, controlling, prioritizing and notifying.
  • the semantic units may use blockchains for authenticating sources (e.g. data source, semantic flux, stream etc.).
  • the system may encrypt semantic waves based on key certificates (e.g. public, private) assigned to identities and/or semantic groups.
  • key encryption may be used to encrypt information to semantic groups wherein semantic waves are encrypted based on a key for the group; the infrastructure may be able to distribute the decrypt keys to particular semantic groups.
  • a semantic wave is modulated at a source based on inference at various levels of the hierarchical structure and further encryption; further, the wave may be collapsed in particular ways and/or only partially by entities, groups, hierarchies and/or levels based on their semantic coverage. In some examples, the wave is not collapsible at some units, groups, hierarchies and/or levels.
  • the semantic unit may be coupled with a semantic authentication and encryption system based on biometric data, certificates, TPMs (trusted platform modules), sensorial, password, location and/or blockchain.
  • the semantic waves and/or components thereof are encoded with the keys and/or data provided by the aforementioned methods and be collapsible by particular artifacts and/or hierarchies.
  • the semantic encryption and decryption may be based on semantic hierarchical inference wherein particular identities, groups and/or keys are allowed access (e.g. via access control, gating) or are associated to particular hierarchies and/or semantic artifacts.
  • the system may perform composition and/or semantic collapse based on the inference on multiple elements and/or artifacts wherein the system may use a determined entanglement entropy to infer the missing and/or erroneous artifacts.
  • the system may consider and/or project the order and/or time of collapse at different entities, fluxes and other artifacts based on semantic model, location, orientation, budgets, semantic factors and further semantic artifacts. Further, it may couple such inferences with its own budgets.
  • a memory used by a communication or transfer module e.g.
  • network card can be selectively transferred to other systems; the data transfer is optimized and the data rate may increase if the transfer is being shared between multiple transmit and/or receive channels.
  • wavelets compressed artifacts may be transferred in parallel or may be transferred selectively with various resolutions and speeds based on semantic inference based on metadata; as such, in an example, the image may be transferred at a base, adequate or required resolution at first and then being built at a higher resolution based on other streams.
  • the system may transfer interleaved information based on various channels, fluxes, routes and semantic groups thereof.
  • a block of memory may be associated with a semantic identifier and the system infers semantics for the identifier and applies semantic rules; the semantic system may use semantic analysis to control the access to the memory for I/O operations, transferring and/or receiving from memory. Analogously with the access control on block of memories the system may perform access to web, collaboration, social, sites, messages, postings, display control artifacts, database artifacts, text artifacts, word processor artifacts, spreadsheet artifacts and so on. [00569] In a semantic flux and/or stream scenario, the transfer rates in such a module comprising a memory may look as follows. The sender has semantic memory and/or buffers that need to be transferred.
  • the sender pushes the data and the semantic information associated with it to the memory and the system decides which data to transfer based on semantic analysis; the system may adjust the communication and transfer protocol parameters based on the quality of service and/or semantics (e.g. the quality of service may be modeled as a semantic; LOW, MEDIUM, HIGH, IMMEDIATE, potentially based on an input from a user).
  • the system may use semantic fluxes and/or streams for transfer to/from memories.
  • a semantic computing system may comprise a grouping of memories connected via semantic fluxes and semantic streams controlled through semantic gates.
  • the memory may be a semantic memory - 93 - LUCM-1-1055Spec organized as a hierarchical semantic network model and as such the level of access control, granularity (e.g. semantic resolution) in semantic inference and representation is increased.
  • the information is clustered based on internal semantic representation for optimal access and performance.
  • the source has, obtains and/or determine semantics on the data to be sent and the system uses the semantic information to intelligently send the data to the destination.
  • the source detects artifacts in the data and infer semantics that are then used to selectively transfer data to the destination; further, the data may be mapped to semantic network models.
  • the data transferred can be selected data, particular data, particular resolution data, particular component data, particular semantic data, particular hierarchical levels and any combination thereof.
  • the source system may selectively transfer the bulk of data since at first it sends the semantic interpretation of the data that can be used by the destination for inference, access control and gating possibly based on semantic factors assigned to the source.
  • the destination may reinforce the inference with its own semantic analysis of the received data.
  • the system sends a semantic from source to destination while preparing data for transfer (e.g. cached, buffered etc.).
  • the selectivity of data may be related for examples with selected semantics and/or factors (e.g. intervals).
  • the system may selectively retrieve only portions of frames, images, videos and/or semantic models based on risk, abnormality, semantic of interest from PACS (picture archiving and communications system), EMR (electronic medical record), VNA (vendor neutral archive) etc.; it is understood that in some cases the images, frames and/or zones of interest are annotated and thus the system maps semantic models to the annotated zone and further perform semantic inference on the mapped annotated zone and on further mapped semantic models on zones comprised and/or comprising the annotated zone.
  • PACS picture archiving and communications system
  • EMR electronic medical record
  • VNA vendor neutral archive
  • the destination may not require the remaining data to be transferred from the source and as such it may inform the source of that aspects, let the transfer expire (via a semantic expiration) or block the transfer through access control (e.g. via semantic gating).
  • the source sends only o particular semantic scene from the original data together with its semantic interpretation and the destination asses the accuracy factor (e.g. based on risk, rewards, cost etc.) of the semantic interpretation in rapport with its own model; if the accuracy factor meets a goal (e.g.
  • the destination may accept all the semantic interpretations of the source without further semantic analysis and/or further reception of the data; further, this technique may be applied on a sampling basis where the source sends samples of the original data and semantic interpretation at semantic intervals of time.
  • the destination may control the data transfer in the sense that it asks the source of particular data (e.g. data associated with particular semantic artifacts, resolutions, locations, image regions, particular memory areas, particular endpoints, links, sub-models etc.) and the sender sends the data on demand.
  • the destination may ask and/or be provided with access to various artifacts in memory based on semantic access control rules or other techniques explained in this application.
  • the system intelligently stores data on nodes.
  • the distribution of data is based on localization, semantic and semantic rules. Further the data may be distributed as a hierarchical semantic network model. As such, the system is able to map access the required data in a more effective manner.
  • the mapping of the semantic models may comprise memory, blocks, devices and/or banks of the former.
  • a semantic management rule in a compute node specify a semantic or a semantic attribute in its rule then the semantic system will eventually cache the data at/for the node, the related objects and/or semantic network artifacts that are potentially related and be affected by that semantic; other objects may not be required and if the system detects unknown objects may automatically infer out of ordinary events and/or unknown events.
  • the system may further pursue semantic challenge/feedback to the node structure and/or feedback from a user for finding more information about the subject.
  • the system will selectively store parts of a larger semantic model based on the semantic rules at each semantic unit.
  • a semantic memory may be optimized for semantic inference and semantic sharing. Segments of memory may be mapped and/or associated to endpoints and links; the memory links may be mapped and/or associated to semantic fluxes and gates.
  • the semantic memory may be segmented based on semantics and the access control rules determine access to specific semantics and/or memory segments. The system checks (e.g.
  • semantic memory segments must stay unchanged while other segments may be updatable based on various conditions including access control rules.
  • the system may preserve such interrupted inferences and further factorize and/or decays associated factors (e.g. risk etc.) and/or associated artifacts based on the reconnection time, delay, availability etc.; in an example the system factorizes the risk and/or cost based on the increased channel incoherence. Further, the system may use the factorization of risk to further factorize and/or index the decaying of associated artifacts; in an example the system may not decay the inferences occurred prior to a lost connection if the incoherence and the risk factors of unfinished inferences is high.
  • factors e.g. risk etc.
  • associated artifacts e.g. risk etc.
  • the system may use the factorization of risk to further factorize and/or index the decaying of associated artifacts; in an example the system may not decay the inferences occurred prior to a lost connection if the incoherence and the risk factors of unfinished inferences is high.
  • a semantic autonomous system may contain a plurality of semantic memory segments with some segments that contain the hard-wired rules having different access rules than segments which contain the customizable rules.
  • the hard-wire rules may include general rules for safe operation of the system and hence the access to change or update those rules are strictly controlled or even forbidden.
  • the customizable rules on the other hand may be changed based on various factors including local regulations, user preferences and so forth. As such, the customizable rules may be automatically updated by the system when it infers a semantic based on location data and requires a new set of rules associated with those locations; other customizable rules may be also be determined, defined and/or customized by the user.
  • an autonomous car roams from a legislative state to another which has different autonomous driving rules; as such, semantic modeled artifacts and rules (e.g. semantic routes, time management rules etc.) may be ingested to comply with current regulations.
  • the car’s semantic system may be modeled by a user providing guidance through various sensing and actuation interfaces and the system determines semantic routes based on those inputs. The system may infer, comprise and/or ingest such customizable rules comprising time management rules.
  • the user specifies its preferences and/or priorities in particular circumstances and/or activities and the system infers time quanta, the order and actual time for starting and stopping the semantics associated with the circumstances (e.g. activities).
  • Optimized configuration may be also based on semantic groups and possible semantics and/or locations. - 96 - LUCM-1-1055Spec [00583] In one example semantic identification command is used to identify a semantic group and the semantic group is configured with the optimized configuration. [00584] Semantic gate allows the control of the semantic information being exchanged between various semantic entities. The semantic entities may be organized in a hierarchical semantic network model and include memory, processing units etc. The access and the control of a semantic memory used for data transfer is optimized for applying the semantic rules associated with the semantic gate (e.g. filtering and routing of semantics based on access control rules and/or semantic routes).
  • semantic rules associated with the semantic gate e.g. filtering and routing of semantics based on access control rules and/or semantic routes.
  • semantic memory artifacts and semantics e.g. memory associated with semantic memory and marked semantics
  • semantic memory artifacts and semantics may stay active and/or reinforced until they are factorized, decayed, gated, invalidated and/or inactivated based on semantic analysis including time management.
  • semantic memory artifacts and semantics e.g. memory associated with semantic memory and marked semantics
  • semantic inference e.g. memory associated with semantic memory and marked semantics
  • the activation of memory may include electric voltage and current control, chemical, biological and DNA agents, other discrete and analog control whether electric or chemical in nature, biosensors, bio-transducers and others.
  • the system When the system infers a new semantic based on inputs (e.g. data, signal, waveform, value, pattern, etc.) or semantic analysis it issues a refresh challenge of the semantic analysis to the memory, corresponding memory hierarchy level and/or select segments of memory based on the semantic. The memory then refreshes the semantics, semantic model, reinforce/reevaluate/deactivate/expire the semantic together with associated artifacts.
  • the refresh of the semantic analysis propagation to various levels and stages may be based on semantic gating, semantic routing, semantic shaping, semantic factors, time management, access control, and so forth.
  • the system may use hierarchical memory to store hierarchical semantic network models.
  • the memory hierarchy matches the semantic network model hierarchy and potentially the access between hierarchies is semantically controlled (e.g. through semantic gates, access control etc.).
  • semantically controlled e.g. through semantic gates, access control etc.
  • the virtualization may be based and comply with semantic views connect and semantic gating requirements. - 97 - LUCM-1-1055Spec [00589]
  • the hierarchy of memory may be virtualized thus abstracting hardware implementations.
  • the virtualization may be based and comply with semantic views connect and gating requirements.
  • the virtualization may rely on semantic groups of resources.
  • Memory caching processing and preemptive processing may be based on semantics, on component semantic models, hierarchies and other techniques as explained in the application.
  • the system may use semantic components and/or associative memory for implementation of semantic memories.
  • a semantic artifact and/or semantic identifier is active in a short-term memory (e.g. short-term semantic view) until it decays. Potentially, may be inactivated, expired, deleted and/or transferred to another memory (e.g. recycle, longer term, higher level etc.) if its factor reaches a certain threshold/interval.
  • the system uses semantic time management for structures of memory associated with semantic artifacts including view frames, views, routes and so on.
  • the system may generate or associate a particular semantic and/or identifier with an access control rule; they can be associated with a memory block and/or with an entity or semantic group that require access to the memory block.
  • the access control rule may be associated with semantic groups, possibly via a semantic attribute and other semantic identifier.
  • a semantic group comprises a memory block semantic identifier and an entity semantic identifier and as such the computer is able to control the access to the memory in a more facile manner by associating access control rules to the semantic group.
  • the access to memory may be evaluated based on semantic analysis including synonymy, antonymy, meronym etc.
  • the access may be also evaluated on causality semantics (oriented links and/or associated endpoints and their related causality attributes etc.).
  • the management plans may include access control plans and rules.
  • the access control rules are used to control access rights to various resources including memory and memory segments, disk and disk segments, networking and data transfer channels, sensors, controllers and any other hardware and software modules.
  • the resources including memory
  • the resources may be associated and/or organized as a semantic model with endpoints comprising segments, zones and links comprising channel and buses.
  • the system may increase cybersecurity for example, by assigning risk factors to communication links and memory related endpoints and areas.
  • - 98 - LUCM-1-1055 Spec the signal (e.g.
  • a semantic sink may communicate with the semantic engine via a semantic gate. Any entity can incorporate the semantic sink and interact with the semantic engine.
  • the semantic engine performs semantic inference on the data and signals received via a semantic sink; the semantic sink may comprise a semantic flux and the semantic engine performs semantic analysis based on the data and signals received via the semantic sink flux.
  • the semantic engine may be used to synchronize and/or control the workflow in hardware and/or software components which embed or incorporate the sink on local or remote computer units and/or systems and further for cybersecurity controls.
  • the hardware components may be any components, devices, blocks and/or interfaces whether analog, digital, continuous or discrete.
  • a trail of semantics may be recorded based on a semantic route or a drive semantics whether inferred and/or specified by user. Sometimes a semantic gating is used for recording semantic trails.
  • the semantic model can be defined and configured locally for each system based on user interfaces, provisioning, configuration management or data stores. The semantic model can be shared between various systems. Additionally, the semantic systems can share parts of the semantic models and potentially exchange semantic model updates in a way that if one system is determined to have a better semantic model or parts of thereof, be able to improve the other semantic systems models as well.
  • the system may use semantic gating for semantic model exchange. Sometimes the gating may be based on identifiers, names and so forth.
  • the system uses gating for transmitting (or not transmitting) and/or forwarding (or not forwarding) parts of the semantic model that are associated with particular semantics and/or semantic groups; in further examples the gating may be based on gating drive semantics where the system gates parts of the semantic model based on the semantics associated with the gating drive semantics.
  • the semantic model exchange may take place in a semantic network environment where a model in at least one endpoint is gated to another endpoint.
  • collaborative intelligence is superior to non-collaborative intelligence. This is also associated with swarm intelligence and group intelligence.
  • the collaborative intelligence may be materialized through distributed semantic systems.
  • the semantic systems may be coupled through various semantic connection techniques and artifacts including semantic flux, semantic streams and semantic gate.
  • Semantic systems may register and/or send advertisements with their level or semantic knowledge and/or capabilities (e.g. themes, semantics, semantic factors, budgets etc.). Those advertisements or registrations may be based on location and space-time semantics in an example. Further, the registration may include operational rules, semantic routes, parameters and other semantic artifacts.
  • the receiving system may generate, and map semantic models and rules based on the registered artifacts and locations of those artifacts.
  • Semantic systems may register with any semantic identity, potentially based on semantic profiles; further, those semantic identities may comprise owner, installer, capabilities and so forth.
  • Semantic identification and/or semantic group may determine inference of capabilities and/or semantic attributes.
  • the system determines that the leadership semantic of a DELOREAN is the DRIVING EXPERIENCE and thus in order to project improvements, increase ratings and/or desirability of DELOREAN it may select goals which elevate the GOOD DRIVING EXPERIENCE related factors and/or decay the BAD DRIVING EXPERIENCE related factors while allowing drifts of (inferred/projected) budgets based on risk projections (e.g.
  • semantic systems may advertise capabilities
  • semantic systems may infer lack of capabilities in potential collaborators and/or advertisers.
  • the inference of the lack of capabilities may be inferred for example on failed inference, incoherent inference, elevated confusion, projections, budgeting and/or further semantic analysis.
  • systems that were not able to meet semantic artifacts, goals, projections, budgets, coherence, confusion and/or other factors and budgets may be associated with semantic rules and routes which reflect the decaying biases towards such artifacts.
  • the preferred method of functionality comprises propagating semantics through the semantic connect once they occur, sometimes a semantic system (e.g.
  • semantic system need to challenge or obtain information about particular semantic artifacts and themes. This may happen when the semantic system is not connected a-priory to sources for that semantic/theme and/or the semantic/theme is not trusted or relatively decayed (e.g. low weights, other low semantic factors, sub-thresholding); as such, the semantic system issues a - 100 - LUCM-1-1055Spec challenge or request for information to the other collaborative systems (collaborators). Sometimes the response should meet a required semantic factor/weight threshold and/or semantic budget. The semantic system may specify the required factor/weight level and/or budget to the request potentially through another semantic and/or semantic artifact.
  • the system may asses the best collaborative systems (including on an semantic group basis) that may respond to that request for information and ask and route only through only a selected few of collaborative systems for such information; the route may be based on a semantic orientation.
  • the selection of a system may be based on factors that an initiator holds about a collaborator.
  • the requestor may determine the themes of interest and sends the requests to the selected collaborative systems that may provide the best factors for a particular orientation and budget.
  • semantic flux/gates may expose and maintain semantic capabilities with potential semantic budgets and the system uses those for semantic inference and orientation. Further, systems may maintain those semantic flux/gate capabilities updated continuously based on semantic analysis and/or similar requests, techniques in the semantic network.
  • the requestor may aggregate the received responses and use factor/weighting rules to fuse the data from multiple semantic systems.
  • the fusing of data may use any semantic analysis techniques for fusion including composition, route, trail, synonymy, antonymy, meronymy etc.
  • the system may determine the best components and collaborators based on semantic orientation within the sub-model holding component and collaborators capabilities and mapping.
  • the collaborators process their factor for the information that they receive as a result of a challenge.
  • the response may include the computed factor by the collaborator.
  • the requestor may use the received factor and its internal factor level of the particular collaborators (e.g. general rating/risk or the rating/risk for the particular drive semantic or theme) to compute an overall factor on the response. Further, the collaborator may provide semantic trails of the requested semantic artifact or inference to a requestor and the requestor uses such semantic trails to perform further semantic analysis and orientation. - 101 - LUCM-1-1055Spec [00613] The selection of collaborators can use similar techniques used for semantic grouping, semantic identification, semantic routing, semantic marking and/or inference. [00614] The selection of the collaborators, authoritative semantic sources and the routing to and through those systems may use semantics and/or semantic techniques.
  • Inference on multiple semantic fluxes and/or groups determines entanglement of inferred semantic artifacts.
  • the inference system preserves an entanglement trail which may comprise the semantic identities and/or DNA signatures of entangled semantic artifacts and/or contributors.
  • a semantic group may have leaders; sometimes the leaders are authoritative for particular or on all semantics of a group. The authoritative qualification and/or level may be provided via semantic factors. As such a requestor may decide or be forced by the semantic rules to route and obtain information only through a leader system (e.g. having a semantic factor for a semantic artifact that deems it as a leader).
  • the leaders may be established based on ratings, weights or other semantic factors within the group related to particular semantics and/or subjects.
  • the leaders may be the only ones in a group that publish gating and flux semantics related with their authoritative semantic artifacts. As such, they may be the ones that coordinate the couplings of units in the group for particular leader semantics and artifacts.
  • the leader type hierarchy may extend to the semantic network model where particular semantic network model artifacts or subject entities (e.g. master post) are leaders of a particular group, level and/or hierarchy.
  • Collaborative systems may not need to be directly connected in order to collaborate. They may be dispersed in one semantic group or multiple semantic groups.
  • semantic groups may be represented by leaders for particular semantics or in generalized manner; further the leaders may consist of semantic groups or partial leader groups within the group hierarchy and any combination of the former.
  • the semantic intelligence and/or compute may reside on the cloud and/or nodes in a distributed manner. In an example such distributed intelligence is used for managing smart posts or autonomous robotic infrastructure.
  • the semantic distributed architecture comprises semantic groups and/or leaders at various levels within the architecture.
  • a semantic group of semantically related artifacts (e.g. meanings) may have an authoritative leader based on the particular contexts of semantic inference and/or - 102 - LUCM-1-1055Spec analysis.
  • a leader may comprise semantic artifacts such as component semantics, semantic groups, semantic routes, goals etc.
  • the semantic group formations may be based on semantic analysis.
  • the semantic group formations and leadership are space time, capabilities, context, objective and goal aware.
  • the semantic group formations and leadership is based on artifacts in the semantic network model, where semantic artifacts are inferred at different levels of hierarchies.
  • the system defines semantic groups and leaders in a hierarchical manner on the larger areas (e.g. higher endpoints) based on the semantics associated with such endpoints and endpoint hierarchy and, based on semantic analysis, defines groups and leaders within the hierarchy of semantic network model and semantic groups.
  • semantic systems can exchange semantics via semantic fluxes and the semantic fusion consider them based on a factor/weight assigned to each flux.
  • Semantic fusion takes in consideration the semantic model, semantic rules and semantic factoring for each composition when performing the fusion.
  • the semantic fusion or composition may update the semantic factors and semantic budgets of related semantic artifacts including those involved in fusion and composition.
  • a semantic view comprises and/or conveys semantic artifacts used and/or inferred by a semantic system and/or subsystem.
  • a semantic view may be associated with snapshots or frames of past, current and/or projected semantic analysis.
  • a semantic frame view comprises a frame view based on a subset of semantic artifacts.
  • Semantic analysis may be performed on any type of data including text, binary, waveforms, patterns, images and so on.
  • a semantic stream e.g. based on images and/or frames in a video or spatial rendering
  • interpretation may correlate artifacts - 103 - LUCM-1-1055Spec from various domains; further collaborative semantic image interpretations from various systems ensure multi domain knowledge fusion. For example, if a system needs to infer how many people are cycling at one time, then the system might collect data from various fluxes and fusion, challenge (e.g.
  • the system may use semantic rules for semantic flux management including semantic routing.
  • the system may perform searching based on elements that are assigned drive, route and/or leadership status in semantic inference.
  • the system is able to infer semantic groups and/or trails, rendering and/or storing those graphically, textually, binary and/or via semantic augmentation.
  • a flux might be deemed more reliable (e.g. high reliability factor, lower risk factor etc.) than others in a particular semantic and/or theme and hence is weight being adjusted accordingly.
  • the trust and the semantic factors of semantic fluxes may be determined based on the environment on which the semantic flux provider operates. If an RF and/or optical system operates in a high noise environment, or on a contested or crowded environment then the semantic determinations based on RF and/or optical sensing provided through the flux may be assigned semantic factors conveying high risk, hazard, low trust.
  • Receivers may correlate information from different fluxes in order to assign semantic factors on fluxes and flux semantics.
  • the semantic flux may be associated with semantics and/or semantic identifiers and participate in inference. The association may be based on external inputs, inputs from a user, semantic inference and so on.
  • Templates and/or semantic rules comprising fluxes are used to develop the semantic system.
  • a template or rule may specify that a flux may be taken in consideration for a particular semantic or theme based on its factor for that particular semantic or theme. Sometimes this is modeled through semantic gate and/or access control rules in which semantics are gated.
  • a semantic system may preserve the best, relevant or leader semantic fluxes for ingestion and semantic inference on various themes, semantics and/or goals.
  • a cyber security system may asses and update the ratings of fluxes, themes, semantics and such; it may ingest the low rated factor semantic artifacts and determine patterns of usage that determined the low ratings/factors and assign semantics to it.
  • the cyber units and/or semantic engine uses access control rules to control access to resources.
  • the resource may be any computer resource, hardware or software unit, analog or digital block or interface, component, device whether virtualized or not.
  • the trust of a collaborator is based on vulnerabilities information processing in rapport with the collaborators capabilities or characteristics (e.g. modeled via semantic attributes) which may be impacted/affected by such vulnerabilities.
  • the system might adjust its own semantic inference model, by fusing semantic model artifacts received via fluxes into its own semantic model. E.g. if a factor of a flux is high on a particular semantic then the sub-model for that semantic might be updated with inference artifacts from the higher factorized system.
  • a semantic sub-model that functions well for a system might not function always that well for another system due to particular conditions and functional environment.
  • Various smart sensors can capture various features and semantics with a high degree of certainty. Smart sensors may embed the semantic engine within an internal processing unit. Hence, the semantic analysis and semantic fusion is closer to the sensor. [00639] The semantic analysis and fusion may resemble a hierarchical approach based on the hierarchies associated with the endpoints and/or links in the semantic model. In an example, the system groups elements in the semantic model based on semantic analysis (e.g. composition). In such a way endpoints and/or links may be composed at any level of the hierarchy. In a similar way, semantic analysis may be based on grouping of semantic model artifacts.
  • semantic analysis e.g. composition
  • the grouping of endpoints may be based and/or determine semantic composition on the semantics associated with the endpoints.
  • semantic technology sensor fusion is more efficient and relevant more so when there is a high degree of correlation between the data from various sources. For example, infrared image/frame and an ultraviolet visual image frame in the same field of view may be correlated in order to derive the types of objects in the image.
  • the processing - 105 - LUCM-1-1055Spec is optimized if the two images can be superimposed or layered and/or translated to the same field of view, coordinate system and/or spatial network models for coordinates matching. If the system based on sensors operating at various wavelengths (e.g.
  • the fused data associates the unknown object with a car based on overlaying and semantic analysis on the separate frames and overlaid frames.
  • overlaying is achieved via separate hierarchies assigned to the frames.
  • two or more semantic fluxes may feed in approximately the same semantic time interval information (potentially timestamped) related to an artifact in the semantic field (e.g. via messaging posts) and be able to fusion the inferences on the same theme, semantics and/or artifacts using semantic analysis.
  • the system may be able to identify objects that artifacts are related to and the system associates the inferred semantics to it.
  • the information from two or more semantic fluxes may come from semantic groups of systems based on semantic routes that determine the routing through such systems. Thus, the semantic fluxes allow the propagations and semantic analysis through various semantic groups and by using various semantic routes.
  • the semantic model comprises semantic templates and patterns.
  • a semantic template and pattern might include factorization and time management.
  • the template pattern and template may be associated with groups of elements or semantic artifacts in the semantic model.
  • the semantic systems may use a particular language or symbology for meaning representation.
  • the continuous development of the semantic models may potentially rely on language interfaces including speech, gesture and sign languages, text recognition, text inputs and such.
  • semantics can be expressed or derived through these kinds of interfaces.
  • the interface relies on localization techniques to infer/convey meaning, where network model graphs may be mapped on the front-end sensing of such systems/elements to infer the semantics of movement of artifacts from one location to another and/or from determining patterns of movement.
  • the proper syntactic formations are modeled through the semantic model and semantic rules.
  • the system may translate the language of meaning representation to - 106 - LUCM-1-1055Spec another particular language.
  • the artifacts of the language meaning representation may be associated with other particular languages via semantic relationships (e.g. semantic attributes, semantic groups, semantic rules etc.).
  • the system may duplicate the meaning representation in various languages for optimized processing (e.g. duplicate the semantic artifacts and relationships in two languages).
  • Syntax may be based on time management and rules templates in some examples.
  • the semantic attributes may be associated to other semantics in order to specify their characteristics (e.g. VERB, NOUN etc.).
  • the semantic attributes may be group independent or group dependent.
  • the group independent semantic attributes may represent the type of object, the class of the members or other non-causal or non-dependent relationship (e.g. found in the same location or scene); the group dependent semantic attribute may signify a causality and/or the dependency of the objects in the semantic group.
  • the semantic system may use the semantic model and determinations to derive verbs.
  • Verbs may be associated with the semantic management rules. For example, the system may determine the tense of the verb by just examining the time of a semantic inference including examining a semantic trail and a semantic route; e.g.
  • John and Mary became friends may be derived just by examining the semantic trail, time, semantic time and/or semantic management rules for the semantic attribute “FRIENDS” associated with the semantic group (John, Mary); as such, the system knows that the semantic attribute “FRIENDS” for the group has been inferred past the current semantic view frames and/or view and such it infers the past tense of the verb. Based on semantic time management and semantic composition the system may infer appropriate tenses for the verb and produce semantic augmentation outputs. [00649] In an example, the tenses are based on the distance in the semantic determination in a semantic trail. The distance may be based on time, semantic factors, indexing, semantics, semantic drifts and/or semantic interval.
  • Semantic factors decaying in a semantic trail can also be used.
  • Semantic indexing may be used to determine space-time distance, correlation and/or orientation in a semantic network model and for semantic groups.
  • the semantic systems convey meanings through language and symbols which may be the same or different from the language of meaning representation.
  • the particular language terms may comprise encryption, encoding and modulation which are semantic based (e.g. generated based on semantic inference).
  • the translation from another language to the main language of meaning representation may include decryption, decoding and demodulation.
  • the semantic model may learn representations from various sources based on direct observations or by documentation of those sources and their representation rules. As such, any schemas may be described and/or understood.
  • the system may ingest data through various means including text, optical, pointing and touch interfaces. In case of optical, pointing or touch ingestion the system may interpret inputs, locations, schemas or drawings via mapping of the data and/or data renderings to endpoints and/or links in a semantic network model (e.g. semantic network graph). Other optical recognition techniques and deep neural networks may be also employed. Optical recognition (e.g. shape, character) may be based on a semantic network model mapping.
  • mapping between semantic model artifacts and data and/or data renderings is based on a location including a physical region, area, point location, shape whether relative to the data rendering, frame, image, captured environment, observer, relative position, global position or a combination of those. Actual locations or virtual locations may be mapped in such a way. In further examples the mapping is associated with locations in a frame or image (e.g. pixels, segmented areas, objects, labeled or unlabeled regions, bounding box areas etc.). [00655] Based on the use case the system may adjust inference and semantic models by information in semantic near and/or far fields. Based on inference of semantic near and/or far fields, the system may hierarchically map, adjust and infer models and sub-models.
  • the system may combine such operations with semantic gating.
  • the semantic mapping consists in mappings between data and representation of the system with semantic artifacts of a semantic network model.
  • Taxonomies and other vocabularies may be described and learned.
  • the efficiency of the semantic systems allows them to have the data processed closer to a sensor element (e.g. on a microcontroller, processor, (semantic) memory or specialized circuit residing on the same PCB, MEMS, ASIC etc.), possibly in a hierarchical fashion; this may increase the processing speed, operational capabilities and the efficiency of the operational decision making.
  • Some sensors on a chip may capture data related to various parameters (e.g.
  • Semantics may be conveyed and/or inferred through speech/sound, visual/optical, touch, sensorial, signal and/or waveform, rf and any combination thereof.
  • Semantic models ensure that the signal and data features are molded into a human centric knowledge generation process.
  • the semantic model can include rules that are used for further expansion and adaptability of itself.
  • the semantic analysis comprises semantic techniques as synonymy, semantic reduction, semantic expansion, antonymy, polysemy and others.
  • the user specifies semantic groups and/or provide semantic routes of synonyms, antonyms and other semantically related elements and inference rules. Elements in a group are by themselves related via semantic attributes or semantics (e.g. SYNONIM, ANTONIM). Semantic reduction and/or expansion of groups and inferences may be achieved through semantic composition, semantic routes collapsing and other semantic representations and techniques.
  • a user may specify the semantic relationship via a pointing and/or touch interface; in such an example terms are presented on a screen on a graph representation (e.g. chart, graph etc.) and the user drags one or multiple lines within the representation representing its semantic orientation perception between the terms. Further if terms such as “quick”, “clever”, “fast”, “sharp”, “night”, “light” are presented in a chart the user may select a trajectory that resemble the precepted semantic drifts between such words. Further, if the operation is associated with at least one representative (e.g. drive) semantic, the trajectory may resemble the precepted semantic orientation in rapport with the at least one representative semantic.
  • the system may create semantic groups and semantic routes based on representative semantics and semantic trajectories in the semantic model.
  • the distance of the selected trajectory to the semantics locations may be used to assess semantic orientations and drift.
  • a user may specify correction, goal and/or desired trajectories on displayed graphics (e.g. graphs, text, window and/or display controls etc.); further, a user may specify interest points, areas and/or endpoints. The user may enter and/or the system infers semantic artifacts associated with such trajectories and/or endpoints.
  • the system may define further endpoints at intersections of trajectories with the graphic and perform inference comprising semantic mapping, orientation, shaping, indexing, factorization, analysis, rule, template and/or model overlay learning. It is to be understood that such learned artifacts may be later used in such sematic inference when similar semantic contexts are inferred (e.g. - 109 - LUCM-1-1055Spec shaping and overlay learned models on renderings, graphics, images, frames and/or perform semantic analysis etc.).
  • User pointed trajectories on a display surface may trigger semantic inference on the semantic network model artifacts that the trajectory selects, encompasses and/or intersect; further, the inference may spread to further associated semantic artifacts.
  • the network model artifacts in the trajectory and further associated semantic artifacts may be selected and/or activated based on access control (e.g. the user may have access only to specific user controls as related to semantic artifacts and/or identities).
  • access control e.g. the user may have access only to specific user controls as related to semantic artifacts and/or identities.
  • the user draws and/or specifies areas and/or oriented trajectories associated with the display artifacts and their associated semantics; in some examples, such semantics may be associated with indicators and/or factors (e.g. risk, desire, intention etc.).
  • the user trajectories may be associated and/or used to derive goal artifacts; thus, the system infers semantic drifts, indexing, overlays, routes and/or sub-models based on the overlaying of the user trajectory to the semantics and/or model mapped and/or representing the display/ed data. Further, the system may display such inferences on the display artifacts mapped on semantic network model artifacts and/or hierarchical structure encompassing the network model artifacts. In some examples, the system redraws and/or overlays such information on a display unit. Alternatively, or in addition, the system may invalidate the previous information and/or semantic network artifacts on the display unit controller.
  • the display unit controller may control and/or be incorporated in graphic processing units, graphic cards, semantic units, CPUs, ASICs, FPGAs, DSPs, tensor units, graph processors and so on.
  • the system acquire, groups, links, displays, invalidate, query, overlays semantic artifacts based on context comprising user authentication, semantic profile, wallet and/or access control. Further, the access control may be used to allow access to such artifacts.
  • the system uses the inputs from I/O including mouse, keyboard and graphics to determine the objects rendered, activated, their semantic identification and/or mapping; further, the system performs semantic analysis and learning and overlays the semantic network artifacts on the display screen based on I/O graphic operations.
  • Overlays may be associated with templates comprising semantic identities, profiles, hierarchy level, groups, trails, routes, trajectories and/or composable artifacts and further profiles and templates comprising such artifacts; the system overlays the semantic artifacts associated with the template semantics in the mapped area, display, control and/or further user interface.
  • the overlays are rendered and/or mapped based on such profiles and/or templates.
  • Overlaying and further semantic analysis may be used to further determine rendering of semantic artifacts based on inferred semantics related to color, blurring etc. Further, such rendering is based on semantic profiles (e.g. GREEN, RED may collapse to 30 BROWN based on a semantic profile and/or 40 GREEN based on another semantic profile; GREEN, RED, BLUR may collapse to a GRAY and as such endpoints, regions are blurred to gray etc.).
  • the system uses an additional orientation and/or drive semantics provided by user (e.g.
  • Narratives may be generated by the system based on semantic analysis. Narratives may be of a general nature, based on a theme, drive semantic, semantic route etc. The system may select areas of narratives, link them and/or assigns actions to such artifacts potentially based on a further mapping to semantic models. In further examples, the system may use semantic analysis and mapping to highlight, select, link and/or overlay display artifacts on narrative components. [00672] In further examples, a user may identify semantic group artifacts (e.g.
  • the user selects and/or identifies a display area comprising a set of semantic artifacts and then selects a target trajectory and/or area intersecting further areas, endpoints and/or semantic artifacts, thus allowing the system to associate the semantic artifacts in the selected and/or identified area with the intersected semantic artifacts.
  • the system may mark and/or associate the semantic artifacts of the selected and/or identified area with the semantic artifacts of the target trajectory/area and/or intersections.
  • the system may perform semantic analysis between the selected and/or identified semantic artifacts and those of the target trajectory/area and/or intersections and further, associate the semantic analysis inference artifacts to either or both of the selected and/or identified semantic artifacts and/or target trajectory/area and/or intersection semantic artifacts.
  • the system selects an area with a plurality of attributes and/or terms associated with diabetes semantics and selects a target trajectory/area through endpoints associated with cardiology, arthritis, psychology and other themes artifacts and as such the system is able to present inferences related with the effect of diabetes on different themes, graphics, controls and/or areas.
  • the system - 111 - LUCM-1-1055Spec may use similar techniques to display the impact of rain to various trajectories on a road infrastructure. It is understood that in some cases the impact may be continuously adjusted based on the continuous inference on the conditions of the selected and/or identified area semantic artifacts and/or target trajectory/area and/or intersections area artifacts.
  • the system is able to populate/update a group of graphical control element (and potentially associated labels) and/or semantic groups thereof (e.g. as part of a target trajectory and/or area) with information (e.g.
  • the populate inference may be based on semantic inference and/or gating between the information associated with the target graphical control element (e.g. label, control type, control content, color, font and/or other assigned and/or inferred attributes) and the selected and/or identified semantic artifacts.
  • the system may perform semantic inference based on drive semantics and/or gating associated with the target trajectory artifacts and/or groups thereof (e.g. labels, graphical controls, content, control type, groups etc.) and/or selected and/or identified artifacts.
  • the system performs semantic analysis based on selected and target shape attributes and further render sematic shapes resulted from analysis (e.g. the system has a composition rule specifying that a selected artifact associated with an elephant and a target artifact associated with a (one wheel) bicycle should compose, display and/or route when dragged and/or overlaid on top of each other in a not-allowable icon, smiley face, image, frame, display field, a question request and/or other artifacts; such artifacts may be associated with semantic identities, semantic attributes and/or further semantic artifacts inferred, determined and/or challenged by the system, and, further the system may use further semantic analysis on such composable inferences.
  • a composition rule specifying that a selected artifact associated with an elephant and a target artifact associated with a (one wheel) bicycle should compose, display and/or route when dragged and/or overlaid on top of each other in a not-allowable icon, smiley face, image, frame, display
  • an image associated with a SMILEY (BEAR) FACE semantic identity is stored (e.g. in memory, disk, mesh, device etc.), generated and/or challenged to be retrieved (e.g. from storage, from an inferred and/or preferred semantic flux etc.); the (BEAR) attribute may be optional and/or being more specific for a requested and/or available semantic identity and/or profile.
  • the system may infer, allow and/or generate artifacts (e.g. images, sounds etc.) associated with NICE SMILE based on ratings, profiles, orientation, group resonance and/or further semantic inference.
  • the system may allow SMILEY PANDA BEAR but gate SMILEY GRIZZLY BEAR based on semantic artifacts, entropy, divergence, diffusion, drift and/or further rules and/or profiles.
  • SMILEY artifacts are not available the system may generate, challenge and/or gate (for) artifacts associated with semantic identities with a particular drift and/or entanglement entropy from SMILEY; further, it may gate SMILEY - 112 - LUCM-1-1055Spec antonyms (e.g. GRUMPY) altogether (e.g. based on (configured) entanglement entropy and/or factors).
  • Semantic profiles, factorizations and/or projections may be used to determine SMILEY and/or related artifacts; further, the semantic artifacts associated with SMILEY FACES may be stored (e.g. in memory, database, disk, mesh, file, wallet, device, unit etc.) and/or rated based on inferences and/or inputs from users as results of challenges. In further examples, the user may augment the artifacts and/or compositions when challenged by the system (e.g. provide semantic attributes, circumstances, rules, guidelines etc.).
  • the system may not perform augmentation, render and/or display artifacts associated with high incoherence and/or confusion factors; however, the system may perform augmentation, render and/or display artifacts associated with high incoherence and/or confusion factors when challenging the users and/or semantic fluxes for additional information in order achieve the goal of decaying the confusion and incoherence factors.
  • the system may perform augmentation, render and/or display artifacts associated with high coherence and/or low confusion factors. It is to be understood that the system may perform augmentation, rendering, displaying and/or challenging at endpoints associated with high augmentation factors (e.g.
  • the system is able to select, enable, render and/or update display labels, graphics and/or fields based on semantic analysis. In some examples, such display labels, graphics and fields are associated with semantic artifacts whether gated or/not. Further, the system is able to perform inference based on the information on the display controls and the information of any linked semantic fluxes. [00675] In some examples the system populates and/or selects items in the graphical controls based on information from fluxes based on particular semantic identities.
  • the semantic profiles allow the sharing of various levels of semantic identities based on the semantics of queries/challenges (e.g. BIRCH CLIMBER, 60 LIKE FUCHSIA HAT, 40 DISLIKE FUCHSIA HAT etc.) and thus the system is able to map those and/or select the relevant artifacts (e.g. match and/or map items in a combo-box UI control based on the semantic identities).
  • queries/challenges e.g. BIRCH CLIMBER, 60 LIKE FUCHSIA HAT, 40 DISLIKE FUCHSIA HAT etc.
  • the system gates images, video frames, semantic waves and/or other artifacts based on semantic identity; alternatively, or in addition the system may replace and/or augment one semantic identity with another. Further, the system may mask (e.g.
  • the system may mask objects and/or tags in documents and/or files; as such, the system analyses the documents and/or files for semantic identities and mask the leadership features of identities.
  • the system may transform the document in a rendering, image and/or frame where the semantic identities show and/or are tagged as masked as previously explained.
  • the system may gate the semantic identities and associated semantic artifacts at various levels of the semantic model hierarchy and/or semantic infrastructure.
  • Such gating may be based on access control rules and/or semantic analysis.
  • Synonymy implies in finding synonym semantic artifacts based on factoring/weighting, comparison to thresholds, semantic routing, semantic orientation, semantic drifts and other semantic analysis.
  • the system uses synonymy to perform semantic clustering and semantic group inference.
  • antonymy implies in finding a semantic form for an artifact or collection of artifacts based on antonyms.
  • Semantic processing units can be used to process semantic models.
  • Semantic processing units can comprise systems on a chip potentially using field programmable logic and configurable computing where the configuration of logical gates and processing cores are being configured based on semantic determinations, semantic routes, semantic views, view frames and/or semantic network model.
  • Semantic units and architectures are in general more safe and secure than a general processing unit due to build access control in the model.
  • Semantic models may be configured by authenticating users via various authentication techniques including biometrics, password, mobile device codes, location proofing, time proofing and so on.
  • An important aspect of IOT systems is security; a semantic system handles information at a semantic level is much better positioned to asses, detect, isolate, defend and report system intrusions and anomalies. - 114 - LUCM-1-1055Spec
  • the IOT systems have higher security and privacy concerns and hence controlled information sharing is required.
  • a semantic gate is a way of controlling semantic information sharing and acts as a semantic privacy and dissemination controller based on semantic gating and/or access control rules for example.
  • Access control and filtering is used for controlling the interconnection to other systems and fluxes.
  • Semantic circuitry may consist in a plurality of electronic components wherein each component has at least one semantic input and output (e.g. semantic, semantic flux) wherein the input/s is/are transformed to outputs via semantic analysis. Further, the components are associated with semantic groups based on an inferred composite semantic and possibly, factors obtained at a stage in the semantic inference.
  • Semantic circuitry may be semantic gate driven and thus it can be referred as a hardware semantic gate.
  • the system may use optical components such as polaritons for semantic circuitry.
  • the semantic flux between various components may be conveyed and controlled in a semantic manner in which the information is controlled based on semantic rules and model as explained in this application; this may be achieved via a semantic gate.
  • a semantic wave or signal may form as a waveform modulated at each element based on semantic analysis (e.g. composition, time management, access control, gating etc.).
  • the semantic wave is modulated based on a semantic inferred at the element and/or semantic waves received from other sources/inputs.
  • the semantic wave represents combinatorial semantics which can be further combined while the semantic wave passes through elements.
  • the semantic waves are routed based on semantic routing to other elements or groups of elements based on its semantic components. Semantic routing may be managed using semantic gating on fluxes.
  • the semantic waves may be generated and disseminated in similar ways with semantic conditioning or other semantic techniques as explained in this application.
  • the semantic flux and/or waves may use encryption and authentication at least two elements (e.g. source and destination).
  • the semantic gate may be controlled based on semantics artifacts. Such semantic artifacts may be validated and/or inferred in relation with the authenticity in a distributed semantic engine manager based on semantic groups. Distributed identification, validation, encoding/decoding and semantic wave generation/interpretation may be based on semantic groups or multiple semantic groups whether hierarchical or not.
  • the semantic groups may comprise or define the distributed semantic engine and be used in semantic chaining and validation.
  • semantic artifacts are used to represent, encode and/or encrypt semantic trails. In one example semantic trails are associated with chains of custody.
  • a chain may be represented or associated with a semantic network model with endpoints comprising or being associated with the semantic information and the links representing chain relationships.
  • the semantic network of/and distributed ledger may use semantic analysis and inference for authentication, validation, encoding/decoding, encryption and chain improvement.
  • semantic wave encoding/decoding is used to generate/interpret, encrypt/decrypt and validate semantic trails.
  • other non-semantic techniques may be used for encryption, encoding and other operations on semantic artifacts including semantic trails.
  • a semantic flux source and/or semantic wave may issue or comprise at least one semantic in a semantic block chain and the authenticity is based on a semantic distributed ledger comprising the block and represented or associated with semantic artifacts (e.g.
  • semantic groups of subjects, devices, blocks etc. are formed to encode/decode a semantic wave; in some examples, no single member or subgroup of such semantic groups and/or ledgers comprises all the semantic artifacts to perform such operation, but the operation is performed collaboratively using any of the semantic analysis, conditioning and collaboration techniques explained in this application.
  • a semantic wave may also encode the source of the semantic modulation at each stage.
  • semantics are associated with factors, waveforms and/or patterns; composite semantics may be associated with a combination of those. They may be associated with waveforms modulated in a specific way (e.g.
  • a semantic wave can be simple or composite; a semantic wave may comprise the semantic composition and potentially the identification of modules in - 116 - LUCM-1-1055Spec the semantic route and/or trail modulated into the wave via any of those techniques or combination thereof.
  • Semantic waves may modulate the semantic rules in the waveform in order for a receiving processing unit to update its rules, routes, condition and/or infer the modulated semantics. The system performs processing between a semantic wave and a semantic based on semantic analysis including orientation and drift.
  • the system may use covariance, correlation and convolution of semantic waves coupled to semantic analysis. Further, the system performs semantic orientation and semantic drift between the semantics and semantic routes comprised and/or inferred from the waves.
  • Semantic waves and/or fluxes may combine based on semantic composition routing, semantic rules and semantic gating. They may combine based on semantic time management. The encoding of the trails and/or route in a waveform may be based on the marked or inferred semantics at the nodes.
  • Semantic waves may be associated with semantic fluxes and routed through semantic routes. They may be encrypted and/or authenticated via distributed semantic inference where the distributed parties are semantically inferred and/or defined (e.g. based on semantic groups).
  • semantic trails and routes which may be encoded in the wave itself and the system checks the validity or authenticity of a wave and route based on semantic analysis including orientation.
  • the orientation and drifts may be assessed based on the encoded data and the internal semantic model and rules. In some examples, if the semantic drift of semantic analysis and orientation is large the system may not authenticate the information.
  • the semantic artifacts are inferred by direct observations; hence a semantic model developed in a certain environment would have certain characteristics of that environment including a semantic model based on that environment. Additionally, semantic systems can observe semantic fluxes that come from various sources and can update their models based on these semantic fluxes and trust relationships that have been configured or established.
  • a semantic system will develop based on these direct observations or observations of other semantic systems in the same or different environments. While the semantic systems with similar semantic coverage capabilities that develop in the same environment might have similar semantic model characteristics or signatures, semantic systems that develop in different environments might have different semantic signatures; sometimes those signatures might complement each other. However, in general, the core semantic inference rules to which the models have been configured will drive the development of semantic models. - 117 - LUCM-1-1055Spec [00703] Coherent semantic inference allows a system (and/or semantic group) to reduce superposition via semantic analysis including composition and/or semantic collapse. [00704] Semantic signatures may be based on semantic groups.
  • Coherent semantic groups allow coherent semantic inference based on their semantic signatures at least on group and/or leadership semantic artifacts.
  • Incoherent semantic groups may exhibit a continuous increase in superposition.
  • the system may assign and adjust coherence/incoherence indicators, factors and/or goals; further such indicator and goal artifacts may be associated with a quantum, budget etc.
  • Incoherent superposition may determine incoherent collapse (collapse due high superposition factors and/or decayed quanta/budgets).
  • the system may infer coherent and/or incoherent semantic artifacts (e.g.
  • High incoherency may be related for example with cyber-attacks, channel errors, jamming and other abnormal or challenging conditions.
  • high incoherency and/or decayed budgets may collapse into safety drive routes, hierarchical and/or domain level inferences.
  • a system may learn based on ingestion, fusion and inputs from multiple semantic units running current, conflicting, trusted, non-trusted and/or opposed semantic models in the same or different environments.
  • the current model may incorporate other signatures while keeping the boundaries of semantic inference through access control rules and feedback from trusted sources (e.g. users, other trusted systems etc.).
  • trusted sources e.g. users, other trusted systems etc.
  • the nature of similarity or dissimilarity between models is provided by the semantic relationships of semantic rules, semantic orientation, semantic groups, semantic leaders, drive semantics, semantic routes, and other semantic artifacts between the two or more models.
  • the models may be grouped in semantic groups with one or more models or groups running on different semantic units.
  • the model semantic groups may be determined by semantic attributes which specify the nature of semantic relationships between models and/or groups (e.g. antonym, synonym, not trusted, trusted etc.). - 118 - LUCM-1-1055Spec [00711]
  • the system may consider the signature of the environment described by other sources when performing inference on direct sensing data. The signature of the environment described by those sources may be biased and the system uses semantic analysis based on the fusion techniques explained for semantic fluxes.
  • the system may infer leader flux/streams from where to refresh particular semantics, themes and/or categories. Sometimes the system uses plans where the system defines or determines a theme template based on semantic factors and the system uses those plans for semantic inference on flux/stream leadership.
  • the system A specifies that it can trust a flux/stream from system B 0.5 on news and 0.9 on weather and as such when semantics are received on those themes the system B ponders (e.g. multiplying, summing, averaging, semantic factoring etc.) the composition factors with these trust factors.
  • the system may perform semantic analysis, gating, convolve and/or cross correlate the semantic waves for deriving resulting semantic waves.
  • A may trust flux/streams C on news with 0.7 and as such composes the news from B and C while pondering, convolving and/or correlating it based on the trust, other semantic factors and semantic plans.
  • the pondering and correlation may be based on semantic distributions and spectrograms in intervals of time (e.g. semantic time).
  • a spectrogram associated to semantics and/or themes, potentially in a semantic flux and/or wave may be used.
  • more granular semantics may be refreshed once they expire or before they expire.
  • the semantics may be refreshed individually or as part of a group, category or theme. Further semantics may be refreshed as part of a semantic route, goal semantic and/or factor-based inference and/or any other semantic inference.
  • the system reassesses the validity of a semantic view and/or view frame.
  • the system may not expire inferred semantics but instead ask for feedback on other fluxes/gates about the candidates to be expired. If the system is able to receive feedback and refresh the semantic (e.g. potentially within a budget), the system may not expire it; however, semantic factors may be affected, and further semantic inferences may be required. If the system is unable to receive feedback, it may elect to expire the semantic and perform further inferences based on the expiration including updates to semantic routes, views, view frames etc. Further, the system may use semantic factors and semantic budgets exposed - 119 - LUCM-1-1055Spec through semantic gates for inference. Alternatively, or in addition to expiration the system may use semantic decaying.
  • the system may use semantic expiration to infer negations of the expired semantic.
  • semantic expiration may be used to infer negations of the expired semantic.
  • the system may infer a semantic of SCREEN NOT TOUCHED until the SCREEN TOUCHED is inferred again.
  • negation semantics may determine and/or be represented using high entanglement entropy and/or conjugate factors.
  • the negation, conjugates and/or entanglement may be represented using weights, factors and/or modulated signals; when added and/or composed, the weights, factors and/or modulated signals of the negation, conjugates and/or entanglement result in decayed values which may further trigger lower entanglement entropy and/or semantic collapse. It is to be understood that the weights and/or factors may be represented as values and/or as modulated signals. [00718] The system may associate some intrinsic behaviors with semantic identities and/or semantic groups.
  • the system requests from a stream/flux a semantic/theme with a particular factor and/or budget; if the factor is not satisfied then the target flux system may perform inference until the target is achieved potentially in the requested budget; it is to be understood that such inferences and assessments (e.g. projections) may be performed in a recursive manner in the semantic network.
  • the flux may convey related semantics for a requested semantic theme.
  • a semantic wave may comprise/modulate/encode a semantic route and/or trail. Semantic drifts between semantic routes and/or trails may be calculated at each of the elements based on local semantics (e.g. marked or inferred semantics) using any methods described before. Further routing of the wave and/or flux may be based on the drift. In some examples the drift is used as a semantic indexing factor and the routing and/or budgets based on this factor.
  • the semantic indexing is applied on a semantic artifact or - 120 - LUCM-1-1055Spec semantic drift tolerance, threshold or interval and the semantic indexing factor is calculated based on the semantic and/or route.
  • the system relies on increasing noise to detection ratio on various semantic fluxes and semantic waves based on semantic analysis.
  • Natural phenomena are interpreted via sensing and semantic interpretation.
  • While detecting a natural phenomenon the semantic system infers or augments a semantic artifact through various path in the model representation.
  • a camera or heat sensor is detecting a bright light, might infer that is either a sun reflection or a light bulb ‘BRIGHT’, ‘SUN’, ‘BULB’; additional vision or heat sensing observations might show that the light is attached to a pole ‘POLE LIGHT’ which will actually infer that the light comes from a powered light bulb.
  • the semantic fusion takes into consideration the factors associated with the determinations, so if the confidence factor of ‘BULB ON’ is low because/and the ‘SUN BRIGHT’ is high, and/or because the determinations is taken during DAY semantic, and/or maybe because the ‘POLE LIGHT’ is low then the system infers that the ‘SUN BRIGHT’.
  • the system might infer that ‘LIGHT BULB ON’.
  • semantic flux challenge, inference and additional fusion elements which might not have taken in considerations due to lower factors may be a good tie breaker in cases of uncertainty (e.g. high confusion factors, superposition, decayed budgets etc.); alternatively, or in addition the system may infer additional cues and/or change the orientation in rapport with the semantic space and/or observations (e.g. change the orientation of a device, model overlay, mapping and/or semantic route, use different semantic routes, anchors, conjugate and/or entangled semantics etc.). It is to be understood that the system may organize such composite semantics in semantic groups.
  • the system learns that the BULB provides LIGHT which can be ON or OFF (e.g. via BULB LIGHT, BULB LIGHT ON, BULB LIGHT OFF).
  • inferences of light parameters may determine for example inferences of sensor attacks (e.g. infer blinding attack by a third party when there is a projected risk of attack and further infers SUDDEN BRIGHT LIGHT, LIGHT OBTURATION COVER VERY HIGH while there are no projected sources of blinding other than the potential attacker).
  • a core semantic artifact or rule has very high or absolute weights and/or factors which never change or decay.
  • Semantic analysis, semantic gating including semantic wave modulation may be based on state and/or metadata information from various protocols including network protocols (e.g. TCP/IP, 802.11, 5G NR, Bluetooth, TCP/IP, SMTP, HTTP/S, EPC), data exchange protocols etc.
  • network protocols e.g. TCP/IP, 802.11, 5G NR, Bluetooth, TCP/IP, SMTP, HTTP/S, EPC
  • the segmentation of computing platforms is important in obtaining secure computing systems. The segmentation includes network segmentation, data segmentation, function segmentation and others.
  • a semantic system can create adaptive/ad-hoc networking subnets, can organize data dictionaries and access control (e.g. on data, processing etc.) in such a way that the optimal segmentation is achieved; further it can use processing segmentation based on semantic models, flux/gating and semantic analysis. It can also assign computing power based on the complexity and/or budget associated to a factor, goal, route, inference etc.
  • the semantic system may assign/route/requests resources (e.g. semantic units, semantic fluxes) based on that assessment and possibly on a semantic budget.
  • resources e.g. semantic units, semantic fluxes
  • Such scenarios and operations may take in consideration the potential collaborators advertised and/or published semantic capabilities including their semantic budgets.
  • it can request that a particular semantic inference be computed in a certain semantic budget and pass that information to a resource hypervisor and/or semantic unit that may allocate and/or semantic route to the necessary resources in order to process the data in the required time frame.
  • the semantic composition includes composing semantics and also gating and/or expiring semantics based on time, other semantics, factors, access control and others.
  • a semantic expiration mechanism may help with controlling parameters and/or the system resource utilization including memory, processing power, specific processing operations and others.
  • the control may also include bandwidth and processing related to digital to analog conversion, analog to digital conversion, mixing, filtering, amplifying, up/down conversion, squaring, analog and/or digital signal processing and so forth.
  • - 122 - LUCM-1-1055Spec As such the system may eliminate, prune, invalidate, inactivate or disable the semantics and related semantic artifacts that are linked to lower semantic factors and are not used in semantic routes and semantic composition.
  • the semantic expiration and inactivation/activation control helps with efficiency by releasing and optimizing resources; semantics related with system resources and/or the semantics related to computational requirements, operation, and/or processing might determine to choose a different semantic route over the other for an operation or task; if an inferred semantic or the semantic route is linked to semantic rules/gates (e.g. access control, semantic gate) then the system may guide the task or operation to a particular unit based on the rules/gates; such routing and gating may take in consideration the potential collaborators’ advertised and/or published semantic capabilities including their semantic budgets; additionally, or alternately the system may control the allocation of resources based on similar principles.
  • semantic rules/gates e.g. access control, semantic gate
  • the system may use a plurality of semantic routes and/or fluxes at any given time; the system may choose semantic routes and/or fluxes with various semantic spreads (e.g. based on shift, drift, diffusion, entanglement and/or entropy) in rapport to goals and/or projections.
  • a semantic system may be configured as static or more dynamic. In a more dynamic environment, the system may adapt the semantic routes. In more static systems the semantic routes closely resemble semantic trails and as such the system has a more predictable outcome. The predictability of a dynamic system may be also achieved by controlling the factors of the semantics and semantic artifacts comprising semantic attributes, semantic groups, semantic routes, semantic budgets and so on.
  • the semantic system may use those semantic factors for composition, semantic route selection, routing and any other semantic analysis technique.
  • Biases may be used to control the semantic factors of artifacts; in an example, the system is instructed to LIKE POTATOES and as such the system will bias (e.g. increase/decrease) the semantic factors for routes that comprise vegetable related artifacts because POTATOES and VEGETABLES are associated in a semantic group. In further examples, the system may be instructed to NOT TO LIKE VEGETABLES and as such the system detects superposition factors in regard to this instruction and LIKE POTATOES.
  • a POTATO may be a part of a VEGETABLES semantic (independent) group then the system may factorize more a likeability indicator associated to the route comprising the group member.
  • the system may perform projected based inference on questions and/or routes such as (DO I) LIKE POTATOES (?), (DO I) NOT LIKE VEGETABLES (?) and further infer factors for such routes; further it may infer routes such as IN GENERAL DO NOT LIKE VEGETABLES BUT - 123 - LUCM-1-1055Spec LIKE POTATOES.
  • the system may ask for additional feedback in order to resolve the superposition.
  • the system uses inference based on profiles and/or semantic leadership in order to control the inference.
  • the system may setup leadership semantic artifacts (e.g. LEISURE, PLEASANT, NO RUSH, 50% LESS POTATOES, 80 EVERY MEAL WITH MEAT) potentially based on semantic profiles. It is to be understood that when the leadership semantic artifacts are not met during particular time management (e.g.
  • the system may pursue the current meal inference and create a semantic route, time management and/or goal of MEAT–- NEXT MEAL; further, the system may consider denied/blocked semantics such as based on LACTOSE ALLERGIES which would block them from (projected) meal goals. Alternatively, or in addition, it may factorize the EVERY MEAL WITH MEAT artifact by possibly increasing and/or decreasing factors based on the outcome of the experience associated with MEAL WITH NO MEAT.
  • the system may not pursue the current MEAL drive inference, perform challenges and/or further inferences on alternate trails, routes and/or semantic groups.
  • the semantic artifact EVERY MEAL WITH MEAT comprises the discriminator EVERY which may be used as a discrimination bias in current and/or further inferences based on the factorization inferred after such experiences.
  • Semantic groups of semantic profiles and/or composite semantic profiles are inferred and/or formed by the system.
  • the artifacts stored in profiles e.g. rules, routes, trails etc.
  • the system may need to perform superposition and/or confusion reduction (e.g. due to high superposition and/or confusion factors in inferences using the fused profiles) and thus may reassess the fusion of such profiles.
  • the hardware may be optimized for semantic inference. As such the signals/inputs/data/information are split in various streams (for example based on semantic gating and send and/or routed to various processing units. As such the system may process inputs on more fluxes/streams and/or chains based on the semantic model, semantic rules and semantic routes.
  • semantic processing units may synchronize based on semantic time management semantic signaling inference (e.g. signal, waveform, values, patterns, pulses) and/or semantic waves.
  • semantic time management semantic signaling inference e.g. signal, waveform, values, patterns, pulses
  • semantic waves e.g. signal, waveform, values, patterns, pulses
  • the system may align waves/signals from various sources possible based on cross correlation, covariance, peak-pattern analysis, semantic analysis, determine and learn semantic time management rules.
  • the system may use semantic time management to align two signals and use the techniques specified before to perform semantic learning (e.g.
  • the signal alignment may be determined based on semantic routes wherein one or more semantic routes are correlated with the signals and/or between them; further the alignment may be based on semantic conditioning.
  • the system uses semantic drift and orientation to learn semantic artifacts and also uses semantic artifacts for signal analysis and pattern matching.
  • trajectories of artifacts may be aligned, and semantic rules learned.
  • a trajectory may be partially segmented (e.g. based on gating, endpoints, routes, links, sub-models, sub-trajectories and/or semantic groups) and further rules and semantic routes learned.
  • two trajectories are synchronized based on leader semantics and associated semantic artifacts and/or factors associated with at least one common/similar drive semantic (e.g. composite semantic) in the routes and/or oriented links tracing the trajectories.
  • the factor may be positive or negative in value.
  • the system may infer through semantic analysis indicators such as a rate factor and/or indicator of growth/decrease/decaying of factors.
  • the trajectory inference and comparison may be based on semantic analysis or any semantic artifacts associated with the trajectory. Semantics associated with trajectory endpoints, links, routes, rules can be analyzed and composed in any way.
  • Trajectories and/or orientations may be analyzed based on comparing the semantic routes determined by the semantics associated with elements mapped to the semantic network model. Further, two trajectories and/or orientations may be compared based on the semantics associated with links mapped between endpoints from the first trajectory - 125 - LUCM-1-1055Spec and/or orientation to endpoints of the second trajectory and/or orientation.
  • the orientation may be based on semantic composition on particular trajectories. Alternatively, or in addition, the orientation is associated with a drive semantic artifact.
  • the mapping of links to trajectory endpoints may be also based on such techniques and/or correlated on time management; as such, the links may represent a semantic correlation in time between trajectories and the system perform semantic analysis on the resulted semantic network model to determine the semantic space-time correlation between trajectories.
  • the trajectories may be analyzed based on conditioning/deconditioning of signals based on their waveform mapping to semantic network models.
  • the system creates transient analysis models, views and view frames for semantic analysis including route and trajectory comparison.
  • Semantic abstraction and generalization may work until a certain semantic level is reached (e.g.
  • the system may plan for a semantic budget (e.g. time, cost), and perform the semantic estimation based on generalization on that budget.
  • the generalization/abstraction may be related with multi-domain and/or hierarchical knowledge transfer.
  • the semantic models are hierarchical and/or composable and may comprise semantic relationships at any level for any artifacts whether semantic, endpoints, links or any others.
  • the semantic network models can be composed and/or coupled.
  • the composition may be achieved through semantic gating on any of the links and/or endpoints. Further, the composition and/or coupling may be achieved at any level of hierarchies.
  • the semantic network model A layer GAME is coupled with the semantic network model B layer GAME.
  • the layer A-GAME has a different hierarchy level than level GAME of B.
  • the layers are coupled and/or routed on a semantic factor basis of the hierarchy levels (e.g.
  • the hierarchy levels are coupled based on the assigned semantic factors of semantic artifacts associated with the levels and the system couples the models based on a semantic factor interval and/or threshold; alternatively, or in addition, the system uses group leadership for model coupling.
  • the system may couple any other semantic artifacts used in inference (e.g. endpoints, links, routes, view frames, views, sub-models, hierarchies and any combination thereof).
  • the system uses such couplings and mappings to enhance the mapped coverage (e.g. in a frame, image, semantic vision model, microscopy, spectroscopy etc.).
  • composition of models encompasses overlaying models based on location and/or other semantic artifacts (e.g. semantics, semantics at endpoints, links, orientation, trajectory etc.). Overlaying and/or composition may be based on trajectory alignments based on semantic trails and/or routes.
  • the system may apply masks based on semantic gating before composing models and semantic artifacts.
  • the model coupling is based on projected and what-if type of inference for achieving particular goals.
  • composition of models may entail performing or issuing commands to the elements mapped to the composable or composite model.
  • a certain semantic unit might be assigned a budget to perform semantic analysis on a semantic until a semantic factor (e.g. weight) achieves a level (e.g. a threshold); then the semantic or maybe other semantics inferred based on thresholding may be conveyed further, possible by a semantic gate.
  • a semantic factor e.g. weight
  • level e.g. a threshold
  • the system may asses goal achievement or inference.
  • the semantic may be or not conveyed based on the inferred factor.
  • Parallel computation might be achieved through these techniques and the results aggregated based on semantic composition and analysis.
  • a semantic/computing unit doesn’t respond in a particular time and/or budget the system continues with the semantic inference which doesn’t include the unit’s potential response or semantic.
  • the processing is based on a budget the unit may send a partial inference or a no-inference response after the budget is exhausted.
  • the system may stop the semantic inference and/or update the semantic model and rules at a unit based on a semantic feedback from the other units, potentially organized as a semantic group; alternatively, the system doesn’t stop the semantic inference but waits until the semantic inference is completed (or partially completed) and/or routes the semantic artifacts to the appropriate units based on the semantic rules and routes.
  • entangled semantic artifacts provide complementary and or - 127 - LUCM-1-1055Spec additional inference routes.
  • the routing may include or consider any left non-consumed semantic budgets and/or overspent budget (e.g. borrows budgets from another entity in a semantic group it belongs). As such, the routing and processing is adaptive based on semantic budgets.
  • the system issues challenges to semantic groups for semantic inference on a budget and performs semantic and routing inference within the semantic groups based on semantic analysis, potentially when the budget lapses.
  • the system may challenge a first entity, collaborator and/or group about a second entity, collaborator and/or group and vice-versa. As such, the system may infer factors and/or budgets about the first and/or the second collaborator and associated semantic artifacts. In some examples the system may infer that at least the first and/or second collaborator is compromised and thus increases the risk factors of such entity potentially in rapport with inferred compromised indicators and/or artifacts.
  • the system uses any of the semantic routing techniques described throughout the application to perform semantic flux/gate connection.
  • the system may be highly predictive, adaptive, dynamic, static and/or semantic biased.
  • Multiple waveforms possibly sampled/derived/coded/chirped from a single signal can be processed using semantic techniques.
  • Semantic streams or flux are routed to different units and chains; analysis of semantic budget trails may determine new semantic budgets and new semantic budget routes.
  • the semantic time management, factorization, budgeting and gating allow the inference of the system resources and is critical for semantic route selection.
  • Semantics may be associated to artifacts in relation to channel estimation, band/width, frequency selection, modulation, signal waveforms generation and processing.
  • semantics may be used for resource and/or budget estimators and feed into the semantic chain and/or the semantic model.
  • semantic time management plays a critical role in a system’s capacity to adapt and perform in a reliable manner.
  • semantic connect technologies and semantic fusion ensure timely semantic inference for a semantic connected system.
  • - 128 - LUCM-1-1055Spec Because semantic inference may be goal and budget dependent it is therefore important to be able to estimate, measure and/or control the inference in a distributed environment where multiple pieces are glued together through semantic means.
  • estimation and evaluation may be required.
  • the estimation and evaluation may be based on or result in semantic goals and/or semantic budgets.
  • the resource allocation for semantic inference is prioritized based on the indicators and/or required/allowed budget.
  • the quality of service can be specified based on indicators and/or semantic budgets.
  • Semantic budgets may be based on time management rules and may be represented, associated or comprise semantic factors.
  • the semantic route can be evaluated based on semantic analysis including semantic gating with each system performing management of resources or, in the case of distributed inference, routing to the optimal collaborative systems based on semantics, semantic budgets and other semantic artifacts.
  • a sub-system when a sub-system receives a request for inference with a specific budget, the sub-system executes an evaluation of the goal (e.g. based on what-if and/or projected semantic routing and analysis) for meeting the inference (e.g. GIVE ME ALL YELLOW CARS SPEEDING UNTIL NOON or SHOW ME IN THE NEXT 2 MINUTES THE TEN BEST PLACES TO CONCEAL A YELLOW CAR WITHIN TEN MILES OR TEN MINUTES FROM A/THE COFFEE SHOP).
  • the system may be provided with a goal budget (e.g.
  • the system may project based on the specified and/or inferred budgets; further the goal leadership being CONCEAL with a semantic identity of YELLOW CAR the system may look/project for artifacts which obscure and/or mask the semantic identity of YELLOW CAR at and/or within (specified) (semantic) times.
  • the goal leadership may be hidden and or implicit based on semantic identity (e.g.
  • BEST YELLOW CARS and the system infers the goal leadership as of being related with factors associated with YELLOW CARs wherein the factors are based on semantic inference and the semantic groups and/or routes associated with YELLOW, CAR, YELLOW CAR.
  • the system demands and/or ask information in relation with semantic identities, endpoints and/or areas (e.g. GIVE ME ALL YELLOW CARS WITHIN PARKING LOT A IN THE LAST HOUR) and further the system analyses, challenges and/or interrogates the artifacts (e.g.
  • the system may require semantic fluxes to GIVE ME ALL YELLOW CARS WITHIN PARKING LOT A IN THE NEXT HOUR OR UNTIL JOHN’S DELOREAN APPEARS and as such the creates a time management and access control rule which would allow the gate publishing of YELLOW CAR semantic identity and/or associated artifacts (e.g.
  • time management and access control rules are based on semantic identities such as JOHN’S DELOREAN and further assessment of the NEXT HOUR semantic in associated with the composite request (e.g. using an internal clock inference; and/or using a semantic flux connected clock (e.g. conveying and/or inferred that it can MEASURE HOURS, MINS, SECS) which will be requested for a NEXT HOUR semantic in rapport with the composite request, wherein the clock capabilities may be determined by sensing and/or semantic analysis).
  • semantic identities such as JOHN’S DELOREAN
  • further assessment of the NEXT HOUR semantic in associated with the composite request e.g. using an internal clock inference; and/or using a semantic flux connected clock (e.g. conveying and/or inferred that it can MEASURE HOURS, MINS, SECS) which will be requested for a NEXT HOUR semantic in rapport with the composite request, wherein the clock capabilities may be determined by sensing and/or semantic analysis).
  • the system may select endpoints based on a projection where a/the YELLOW CAR may go, reach and/or be located in TEN MINUTES relatively with an anchor (e.g. a/the COFFEE SHOP).
  • the system may apply offensive and/or defensive behaviors with the YELLOW CAR and/or containerized/encompassing/carried/supported objects (e.g. passengers, patients, contents etc.) to project encompassing location endpoints.
  • the system infers that the driver of the YELLOW CAR may be more offensive in movement (or movement semantics/themes) due to (carried/possessed) items and/or further inferred emergencies, shoplifting etc..
  • the system may determine that the YELLOW CAR and/or containerized/carried/supported objects (e.g. such as passengers etc.) may exhibit more defensive behaviors for particular semantics/themes.
  • the system infers that despite the driver of the YELLOW CAR (as a contained object and/or (first) agent) is in offensive mode, the car (as a container/carrier/support, (second) agent and/or a semantic group comprising the car, driver, - 130 - LUCM-1-1055Spec passenger and/or other artifacts) may behave and/or move more defensively due to the inability/unwillingness/setup/configuration of the driver and/or car to pursue driving offensiveness (e.g.
  • car capabilities due to driver’s and/or car’s capabilities (setup/configuration) and/or further semantic groups composable capabilities (setup/configuration)).
  • the car capabilities may refer and/or compose in a hierarchical manner from car’s general capabilities to more particular capabilities associated with a particular car (of the make, type, characteristics etc.).
  • the car capabilities and/or inferred semantics may compose with driver’s capabilities and/or inferred semantics to determine composite capabilities and/or semantics for the container (e.g. car) and/or associated semantic group and/or (composed) semantic identity.
  • Offensive/defensive inferences determine projections associated with traveled paths and/or endpoints and/or an affirmative/non-affirmative entangled party. Affirmative/non-affirmative factorizations of entangled parties may be based on semantic times and/or be associated with friend/foe inferences. [00773] Further, we mentioned that the system may implement fight and/or flight inferences. As such, when the factorization is affirmative for flight and/or non-affirmative for fight the system non-affirmatively factorizes (e.g.
  • the paths and/or endpoints may be also in superposition in relation with the flight and/or fight indicator and thus, the system may discard its factor in further inferences.
  • Entropic (composed) indicators e.g. such as fight and/or flight
  • semantic groups are normalized and/or deduced one from another in the same semantic view.
  • - 131 - LUCM-1-1055Spec In different semantic views they may be factorized accordingly based on the semantic views and/or fluxes.
  • the system may infer that the driver and/or car capabilities may be particularly factorized (e.g. reflecting impairment and/or lack thereof) and thus, the composed semantic group and/or agent/carrier/support (e.g. a particular semantic identity (e.g. John’s YELLOW CAR, THE EMERGENCY YELLOW CAR etc.)) may be assigned composed factorized capabilities (e.g. reflecting the impairment and/or lack thereof).
  • the system determines and/or projects budgets for capabilities (e.g.
  • the car has gas and/or move/drive/range capability for 5 miles and/or 5 mins at 60mph; and/or the driver (steering and/or (further) driving capability) is impaired and/or will pass/phase out in 3 minutes etc.) and factorizes those in compositions and/or semantic groups.
  • the entities whether containing/carrier/supported (e.g. car, post, devices) and/or contained/carried/supported (e.g. passengers, posts, devices etc.) may be associated with (composable) (flow) agents.
  • the system may be configured, store, infer and/or apply factorizations between contained, carried and/or supported associated indicators. Alternatively, these may be used interchangeably.
  • factorization rules reflect the fact that (at semantic times) those indicators may be used interchangeably.
  • the system may be configured, store, infer and/or apply factorizations between capabilities and/or (contained) activities.
  • factorization rules reflect the fact that (at semantic times) those capabilities and/or (contained) activities may be used/performed interchangeably.
  • the system learns the goal based on further explanation – e.g.
  • SPEEDING might be relative to location mapping and/or semantic profiles; thus, the system and/or observer infers speeding based on semantic analysis based on such circumstances.
  • the system may parse video/audio formats and/or frames and perform semantic augmentation.
  • the system analyzes the video/frame/sound content, captions and/or descriptions associated with such videos/frames/sound and performs semantic analysis and gating thus, rendering, augmenting and/or providing users with the required frames, - 132 - LUCM-1-1055Spec video/audio snippets, semantic artifacts and/or semantic groups thereof (whether group dependent and/or group independent).
  • the streaming of video/audio may be based on a variety of transport formats/containers, protocols, compressions, encryptions and/or codecs.
  • those include, but are not limited to, MP4, WebM, MPEG-TS, MPEG-DASH, SRT (Secure Reliable Transport), HLS (HTTP Live Streaming), RTMP (Real-Time Messaging Protocol), MSS (Microsoft Smooth Streaming), WebRTC (Web Real-Time Communication), VC-1, VP8-VP10, AV1, HEVC, H.264/265/26x, wavelet, MPEG, Opus, Theora, Vorbis, AAC, ALS, SLS, TTSI, MP3, ALAC etc.
  • the system processes incoming streamed/fluxed signals, packets, data and/or files and performs augmentation.
  • the system receives signals/packets/data/files with a first format/compression/codec/encryption/protocol and converts it to a second format/compression/codec/encryption/protocol for storage, generation, transmission and/or augmentation.
  • the system may store incoming and/or converted data/frames/snippets in a cache for/during conversion and/or for applying/filtering (augmentation) artifacts (e.g. advertisings, objects, frames, sounds, texts etc.).
  • the cache contents and/or particular cached artifacts may be invalidated and/or cleared based on semantic times.
  • the system is challenged by a user with GIVE ME ALL INSTANCES WHERE JOHN DELOREAN DRIVES A DELOREAN and/or GIVE ME ALL INSTANCES WHEN JOHN DELOREAN DRIVES HIS CAR and the system analyzes the videos/sound/frames content and artifacts based on the semantic group, composite semantics and time management rules associated with JOHN DELOREAN presence (e.g. as detected by inferring semantic identification, artifacts and/or routes associated with JOHN DELOREAN, DRIVES, DELOREAN and further JOHN DELOREAN DRIVES (JOHN’S DELOREAN) etc.
  • Such snippets may contain only the frames and/or artifacts associated with the goal and/or activity (e.g. from where and/or when the composite semantic is inferred to where and/or when expires potentially based on inferred and/or stored time management rules, semantic groups of activity associated artifacts etc.).
  • snippets When such snippets are presented via semantic augmentation they may be extracted from the original media artifact (e.g. video, sound format/file) and presented with inferred captions associated with further semantic augmentation. Alternatively, or in addition, they may be presented without being extracted from the original media artifact; in some examples, the identified snippets are marked and/or played in the context of the original media artifact.
  • the system overlays semantic augmentation with briefs related to projections and/or goals (of user, context, situations, objects, John, semantic identities, groups etc.) and/or further augmentation based on semantic analysis; the semantic augmentation may proceed in some examples based on a challenges from the user (e.g. WHY IS JOHN SO SUCCESSFUL, WHAT ARE THE BEST PARTS OF A DELOREAN , HOW JOHN DRIVES A DELOREAN, WHEN AND WHERE I CAN MEET JOHN etc.) and the system uses the semantic leaderships of semantics of such challenges to perform semantic augmentation. While the system may infer a bias, drive and/or leadership from the user based on challenges (e.g.
  • JOHN IS VERY SUCCESSFULL may perform augmentation based on semantic analysis and/or profiles exhibiting various degrees of drift, divergence, (entanglement) entropy and/or spread from such biases, drives and/or leaderships.
  • the system infers a semantic artifact exhibiting high drift and/or entropy between (inference on) various semantic profiles and as such the system performs semantic augmentation (e.g. by displaying, rendering etc.) of the semantic profiles and the associated spread artifacts.
  • the augmentation may present various views, layouts and/or overlays.
  • the system may segment, diffuse and/or display (with particular rendering semantics) the inferred semantic identities and/or semantic groups. Alternatively, or in addition, it may map and/or overlay semantic artifacts on such semantic identities and/or semantic groups; semantic profiles may be used for such inferences thus personalizing experiences based on viewer/s semantic identities. Thus, it is possible to present semantic augmentation to the user during the semantic time using various semantic views based on various semantic profiles. It is to be understood that the system may switch between semantic views based on the inferred visualizing semantic identity and/or semantic view.
  • multiple views are displayed and overlaid on top of each other; further, the system may consider and/or use semantic augmentation in regards to entropy, coherency/incoherency and/or confusion factors of such composite semantic views and display/render them based on further inferences and/or intervals related with such factors.
  • semantic overlaying may be used for example to suggest and/or analyze team plays in sports games (e.g. hockey, football, soccer, basketball, volleyball etc.), analyze (medical) imaging, maps, routes, object placing etc.
  • such displaying and/or overlaying techniques may be access controlled and thus only allowed artifacts are rendered (e.g.
  • a team member may have access to all artifacts while a TV show host may have access only to particular artifacts, levels, views, shapes and/or granularity).
  • the system may be challenged on showing (e.g. SHOW) instead of giving (e.g. GIVE); thus the system may use a different augmentation method based on circumstances (e.g. SHOW entails rendering on a display while GIVE may entail other modalities such as sound, tactile, wearable feedback, vibration etc.).
  • SHOW entails rendering on a display while GIVE may entail other modalities such as sound, tactile, wearable feedback, vibration etc.
  • the challenge may specify a semantic identity (e.g.
  • the system may use further associated semantic identity semantic profiles for augmentation; while specific semantic identities may be provided, alternatively, or in addition, the system may infer semantic identities based on circumstances and/or semantic analysis.
  • the semantic identities may be beamed to the objects, entities and/or artifacts to which they belong.
  • S2P2 infers based on sensing a semantic identity of “chair by the fireplace” (and/or “door close to John”) and thus, it may beam (e.g. by directional transmissions and/or transmission signal parameters adjustment such as explained in US patent application US20140375431) and/or (further) semantic routes within a semantic mesh of participants (e.g.
  • the system may use the gating and/or publishing capabilities to infer on which devices and/or semantic groups to allow and/or perform semantic augmentation; further, such devices and/or semantic groups may be associated with at least one user, profile and/or semantic group thereof.
  • Semantic identities and/or semantic groups of devices may be associated with access control rules which allow the augmentation to be performed on such devices (and/or semantic groups thereof) if the access control rule, publishing, capabilities and/or gating allows.
  • a device and/or semantic group may be associated with allowing all and/or particular (e.g. based on publishing, budgets, factors, enablement, diffusion etc.) semantic augmentation - 135 - LUCM-1-1055Spec capabilities while others may have the semantic augmentation (and/or content) diffused, blocked and/or gated possibly on particular semantic artifacts.
  • the system may provide gating and/or access control based on inference on content (e.g.
  • the system communicatively couples at least two artifacts such as posts, devices, components, modules, units, fluxes, UI controls, video renderers and/or further artifacts based on semantic inference and/or routing.
  • artifacts such as posts, devices, components, modules, units, fluxes, UI controls, video renderers and/or further artifacts based on semantic inference and/or routing.
  • such coupling is achieved by establishing ad-hoc networking, flux and/or stream connections.
  • the system establishes ad-hoc networking/flux/stream connections and/or routing based on location, endpoint and/or inference that particular artifacts are associated with the same user, profile and/or semantic group.
  • the system may perform implicit leadership and/or routes based on semantic profiles.
  • SHOW CARS may determine an implicit route and/or leadership for YELLOW CARS based on a semantic profile of the challenger and/or the challenged.
  • the system may perform semantic gating based on location, endpoint, semantics at locations and/or endpoints.
  • Semantic gating may be based on semantic analysis and/or semantic profiles. In some examples the system infers that a CHIEF SUPERVISOR ON DUTY may visualize and/or have access to YELLOW CARS associated artifacts at a location/endpoint associated with moderately elevated risk while SUPERVISOR OFF DUTY may visualize/access such YELLOW CARS only in high risk or emergency situations (e.g.
  • the system may route/re-route the request, may gate the request based on the semantic model and route the parts to different sub-systems.
  • the sub-system may allocate the necessary resources for performing the semantic inference within the budget. If the sub-system implements semantic based virtualization (e.g. dynamically allocate resources on a virtualization platform based on semantic inference), then the sub-system may use the evaluation to allocate and/or spawn new virtual resources for the specific semantic artifacts.
  • the system may use semantic inference to infer semantics for locations and further perform location-based searching.
  • the system keeps up to date published and/or gated semantics associated endpoints (e.g. via semantic analysis including - 136 - LUCM-1-1055Spec time management).
  • the system may infer diffusiveness factors which may be used to index and/or diffuse semantic artifacts in the semantic field and space.
  • diffusive semantics artifacts the system assigns and/or factorizes HAZARDOUS semantics to endpoints based on diffusive (gating) capabilities (of the oriented links between endpoints).
  • the semantic diffusiveness may be based on diffusion (e.g.
  • the diffusiveness may be coupled with semantic shaping.
  • the system may perform propagation analysis (e.g. electromagnetic). The propagation analysis may take in consideration semantic shapes of objects and/or further semantic artifacts as mapped and/or detected to semantic space.
  • the system challenges the system (e.g.
  • challenges such as GIVE ME THE PATH THAT I CAN LIKE results in sub-goals with higher reward to risk factors.
  • the reward may be and/or comprise a risk indicator and thus the reward to risk factor would be elevated and/or maxed out (e.g. 100%, 0.5V, 3A, vertical polarization, no quantum spin, 1 etc.).
  • the system maintains and manages resources, entities capabilities and allocation based on semantics, semantic artifacts and semantic analysis.
  • the resource advertises, publish and/or register inferences and capabilities; further, the system may represent and organize resources and capabilities as models, model artifacts and/or semantic artifacts (e.g. groups, attributes, routes, endpoints, links, sub-models etc.). The system is capable to optimize resource allocation based on semantic routing and semantic budgets.
  • semantic capabilities of a system may be exposed, published and gated via semantic fluxes and semantic gates.
  • a semantic flux and/or gate may publish semantic capabilities together with validity, decaying times and or semantic budgets for - 137 - LUCM-1-1055Spec particular semantic capabilities (e.g. semantic artifacts, goals, factors etc.).
  • validity and decaying times are used by a connected system to assess the routing for inference.
  • the capabilities are inferred based on semantic groupings and semantic model at various hierarchical levels (e.g.
  • semantic posts group A mapped to an endpoint EA and group B mapped to an endpoint EB form a group C and the group C capabilities mapped to an endpoint EC comprising EA and EB are inferred from those of group A and group B).
  • semantic budgets may be used for assessing the optimal routes for inference.
  • the semantic gates may refresh this information on a frequency based on semantic time management associated to particular goals.
  • the system may perform goal-factor analysis in which the system performs the inference for achieving particular semantic goals and establishes/infers the factors and indicators (e.g. rewards) associated with achieving the goals or not (e.g. having those factors within an interval or threshold).
  • the goals may be associated with factors/ratings/indicators for objects and/or semantic artifacts, for inferring, associating or dissociating particular semantics (e.g. to/from artifacts, objects, entities) or any combination of those.
  • the semantic goals may be inferred or specified based on user inputs.
  • the user may specify through an interface the targeted or allowed factor/indicator for an operation (e.g. risk, cost etc.). and the system performs semantic goal analysis based on the targeted semantic for the operation and specified factor.
  • the user specifies on a graph dashboard and/or (semantic) (enhanced) display optimal locations or trajectory 63 of the goal.
  • the dashed line between numbered nodes or endpoints illustrates an actual physical path of travel.
  • the solid lines between nodes represent semantic links between nodes, including a link and permitted direction.
  • the system may map the locations and intersections of the trajectories on the graph to a semantic network model and perform semantic analysis of the graphs and trajectory at intersection points coupled with the semantic routes/trails of the graphs; further, it may be coupled with semantics and factors specified or inferred based on inputs from a user (e.g.
  • a user specifies the semantic artifact, indicator and/or factor for a divisional link, endpoint, intersection endpoint, trajectory etc.).
  • Such inputs may consist for example in pointing or dragging a pointing device or finger on a surface, display and/or touch interface.
  • the semantic analysis may be used to adjust the semantic model in order to minimize the semantic drift that was determined/inferred based on the feedback.
  • the dashed lines in Fig.17 and 18 may represent, convey and/or be substituted with any representative graphs, charts, plots and/or display elements/components (e.g., statistical, line, bar, candlestick, OHLC, motion, timeline, map, graphs, charts, maps, diagrams, etc.) which may be related to semantic artifacts (e.g. semantics, attributes, indicators, factors, overlays etc.). Further, the system may infer and/or map semantic artifacts based on techniques such as mentioned in this application.
  • any representative graphs, charts, plots and/or display elements/components e.g., statistical, line, bar, candlestick, OHLC, motion, timeline, map, graphs, charts, maps, diagrams, etc.
  • semantic artifacts e.g. semantics, attributes, indicators, factors, overlays etc.
  • the system may infer and/or map semantic artifacts based on techniques such as mentioned in this application.
  • Semantic drifts and factor comparison may be used for assessing goal drifts; further, the comparison may be associated with a factor of a drift semantic (e.g. semantic capturing semantic differences) that may be used by the semantic inference as a semantic thresholding comparison.
  • a factor of a drift semantic e.g. semantic capturing semantic differences
  • rewards or functions of rewards e.g. accumulation
  • the system may reevaluate the factors (e.g. rewards) within the model based on semantic inference.
  • the system sets the goals and performs inference on the goals for determining a set of semantic routes which are potentially cached, saved and/or activated in memory in association with the goal; if the goals are pursued, the semantic engine compares the semantic drift between the goal or drive semantic artifacts with the current inferred semantic artifacts (e.g. comprised in a semantic route or trail). If the drift is exceeding the threshold (e.g. based on a factor value, interval and/or thresholding semantic) then the system may readjust the goal or quit the semantic inference while associating an inferred drift to goal factor or indicator to the inferred semantic artifacts, routes, semantic trails and goal (e.g.
  • the system may use indexing factors associated with semantics in order to perform drift, cost, reward, rating and/or other factors adjustments and/or calculations.
  • the system may use goals, factors, and indicators rules and/or plans for adjusting and/or indexing goals, factors, indicators and any combination of those.
  • the factors and indicators plans may be associated with semantic time management, composition rules, factor rules and other rules.
  • Semantic groups of components may pursue common and/or composable goal-based analysis, wherein the semantic exchanges and routing between components is performed through semantic fluxes, semantic gates, semantic waves etc.
  • Those goal based semantic groups may change based on the change of the drive semantics. As such the semantic groups may change based on goal-based analysis and/or collaboration.
  • - 139 - LUCM-1-1055Spec The system may pursue goals that are inferred and/or received.
  • the system infers goals indicators, goals and drive semantics.
  • indicators are specified in the semantic network model via semantic rules and the system infers the indicators based on semantic inference; in some examples such indicators may be inferred and/or selected and provide optimal inferences.
  • the system may infer semantics associated with interfaces, sensors, graphs, graphical control types, dashboards and used for performing semantic augmentation.
  • the system may pursue goal and/or effect post-inference analysis.
  • the system performs semantic analysis to determine why the goal has been or not been achieved as budgeted.
  • the system uses the recorded semantic trails to perform analysis (e.g. using what-if and/or projected) and infer the semantic artifacts that have been the most consequential (leaders) of success/unsuccess or realization of goal related factors; the analysis may be performed for example on multiple projections of semantic view and/or frame views and further, the system may ask for feedback on projections potentially until a particular goal (actual and/or projected) has been met.
  • the system may infer new semantic routes, groups, leaders and artifacts.
  • the system creates a semantic route and/or groups of recommended and/or forbidden semantics and/or artifacts in certain contexts as comprised by the semantic routes, views, groups and other semantic artifacts.
  • Post-inference analysis may be used with semantic displaying of information.
  • the system determines the indicators, factors, routes and further semantic artifacts that may have caused the success, failure and/or other indicators/factors/factorizations; in some cases, indicators/factors/factorizations may be specified by users while alternatively, or in addition, may be selected by the system based on high factorizations, goals/sub-goals matching/drift and so forth.
  • the system may mark, group and/or label the display artifacts that are inferred in such a way.
  • the system groups and/or labels controls, dashboards of indicators and/or other user interface artifacts based on semantic analysis and rendering.
  • the user interface controls are rendered based on semantic artifacts mapping and/or semantic diffusiveness and/or hysteresis.
  • renderings may facilitate better visualizations and augmentations of projected factors, inferences, semantic units control, device control and/or simulated environments.
  • the system may project the inference of particular semantics during semantic scene and/or view development (e.g.
  • the system may further increase the - 140 - LUCM-1-1055Spec factorization (e.g. weights, risk, success etc.) of the routes, rules and semantic artifacts which were used in the projected inference. If the projected inferences are not met then the system may create a new semantic group/s based on alternative, additional and/or composite semantics associated with the object (e.g.
  • the system may update the semantic artifacts used in the initial projection to include a factor (e.g. for weighting, risk etc.) for the newly inferred semantic group/s and link and/or associate them with the newly created semantic artifacts.
  • the system may update, factorize and/or invalidate the original semantic artifacts used in inference (e.g. update the semantic identity, decay etc.). The decay and/or invalidation may happen for example, if the system is unable to differentiate (e.g.
  • the system uses goal and/or post-inference analysis to adjust semantic models and artifacts. For example, at a beginning of a goal-based inference the system may associate a factorized indicator and/or threshold to a semantic artifact which may be adjusted and/or changed based on post-inference analysis.
  • the system may adjust and/or associate semantic artifacts to factorized indicators and/or thresholds.
  • the system has and/or infers a semantic artifact of TYPE X GATE DELOREAN (LIKELY 90 % TOO) NARROW; however, after pursuing the goal of DRIVING CAR THROUGH TYPE X GATE with a factorized degree of success it may adjust the initial semantic artifact to TYPE X GATE DELOREAN (LIKELY 10 %) NARROW and/or TYPE X GATE DELOREAN NOT NARROW. Further, the system may adjust the semantic groups and further semantic artifacts associated with the semantic identities in the inference (e.g.
  • TYPE X GATE and DELOREAN When the system seeks multi-goal inference, it may prioritize the semantic goals via indicators and factors and form pluralities of semantic groups and pursuing those in semantic analysis/inference. [00819] The system may accumulate and redistribute factors (e.g. rewards) based on the pursuing of goals, routes and/or potential feedback. The rewards, feedback, ratings and - 141 - LUCM-1-1055Spec other factors may be received and inferred from any data and input including user, sensing entity, internal, external etc. [00820] Semantic routing of collaborative components/systems/views/view frames/hierarchies may entail local semantic routing within local model, routing between models and/or routing between components.
  • factors e.g. rewards
  • the rewards, feedback, ratings and - 141 - LUCM-1-1055Spec other factors may be received and inferred from any data and input including user, sensing entity, internal, external etc.
  • the models and sub-models are coupled based on semantic routing and/or semantic gating.
  • Semantic fluxes, models and sub-models may be coupled based on semantic analysis on the gated semantics.
  • goal and/or mission-based analysis may be used to implement semantic contracts.
  • at least two entities are bound by a contract encompassing one or more contract clauses and conditions.
  • the semantic system defines such clauses and conditions as indicators, goals and/or factors to be achieved and further to infer further completion and/or alerting semantics during or after goal completion.
  • an entity A providing manufacturing for an entity B is bound by a contract comprising a clause DELIVER EVERY QUARTER 10000 PAIRS OF SHOES FOR SIZES THAT ARE UNDER 100 PAIRS IN THE NY WAREHOUSE (e.g. and/or REPLENISH STOCK ONCE THE STOCK IS UNDER 100).
  • the system may infer the UNDER 100 PAIRS IN THE NY WAREHOUSE for an entity type (e.g. SPRINT BLACK SHOES SIZE 10) based on semantic analysis (e.g.
  • the system is able to continuously perform semantic analysis and matches the initiation and realization of goals whether based on semantic groups or not. Analogously, the system may consider the routes matching or comprising particular semantic groups associated with particular entity instances.
  • the contracting is based on semantic groups and the system analyzes the contracts clauses and/or goals based on whether they are met on a semantic group composite basis.
  • the contractual clauses and/or goals may be access controlled (e.g. selectively and/or controlled accessible to participant and/or observing semantic identities) in a potential hierarchical manner. Further, during the semantic inference the semantic artifacts and/or semantic views may be access controlled and thus the semantic inference and augmentation toward the goals will pursue and/or reveal only allowable routes and further semantic artifacts. [00826] The system may further analyze the risk of the contract not being met and adjust the risk indicators and/or factors in connecting semantic fluxes and gates. It is to be understood that multiple risks may be inferred by various entities and groups (e.g.
  • the system may infer difficulty factors for the goals and/or further semantic artifacts and use them to infer rewards, risks, budgets, indexing and/or further factors.
  • the system infers that during winter storms the difficulty of keeping the warehouse stocked according with the goals is higher (e.g. the risk of failure is higher) than non-storm days and at hence it may increase the risks (during goal development), rewards, ratings and/or other factors of the providers in relation with the achievement of goals.
  • the user may use indexing on semantic artifacts to further adjust based on such circumstances.
  • the system may keep track of activities, tasks, projects and/or (associated) goals assigned to various semantic identities.
  • the system performs an activity of LEARN ABOUT ENGINE SENSOR SUITE in order to achieve the goals of A VERY GOOD ENGINE MECHANIC, A GOOD (CAR) MECHANIC; it is to be understood that in the examples the activity and/or goals refers to semantic identities and/or semantic routes which may comprise further hierarchical semantic identities and/or routes – e.g. ENGINE SENSOR SUITE comprises semantic identities and/or semantic routes such as – SENSOR, SENSOR SUITE, ENGINE SENSOR SUITE.
  • the risk factor of not achieving the goal may entail assessing routes such as LEARN (FROM) BOOKS, LEARN (FROM) COURSES, LEARN HANDS ON etc.; it is to be observed that the route LEARN FROM BOOKS may entail the activity of LEARN in relation with a semantic identity of BOOKS with a further semantic localization specifier (e.g. specifying artifacts comprised in BOOKS artifacts and/or endpoints associated with BOOKS) such as FROM which may be inferred based on circumstances. - 143 - LUCM-1-1055Spec [00828]
  • the system may perform and/or guide the semantic analysis based on or of the loss (e.g.
  • the system infers and/or projects semantic artifacts, goals, routes, budgets and intentions based on (composite) loss indicators and/or factors (e.g. risk of loss, cost of loss, reward of loss etc.).
  • Loss factors indicate and/or are associated with positive and/or negative sentiments; positive and/or negative sentiments can be modeled through loss factors.
  • the system may pursue loss goals, routes, budgets and/or intentions.
  • the system may perform gain based semantic analysis based on gaining (associating, grouping etc.) of particular semantic artifacts for particular semantic identities.
  • the system infers and/or projects semantic artifacts, goals, routes, budgets and intentions based on (composite) gain indicators and/or factors (e.g. reward of gain, cost of gain, risk of gain etc.).
  • Gain factors indicate and/or are associated with positive and/or negative sentiments; positive and/or negative sentiments can be modeled through gain factors.
  • the system may pursue gain goals, routes, budgets and/or intentions.
  • the system may perform inferences and/or projections on factors (e.g. risk, cost etc.) of going over or not meeting the budgets. Further, the system uses semantic analysis for inferring budgets based on projections, goals and/or factors. [00830]
  • the semantic budgets may be associated with semantic groups.
  • the budgets may be for example inferred within semantic groups and published via groups leaders and/or gating. Further, only particular semantic identities and/or groups may have access to particular budgets in a selective way; the system may select one budget over the other based on identification in the semantic network. Alternatively, or in additions, semantic profiles may be also used for providing access, inferring and/or selecting one budget over the other.
  • a network component e.g. network card, ASIC, I/O module, I/O block, digital block, analog block etc.
  • a network component e.g. network card, ASIC, I/O module, I/O block, digital block, analog block etc.
  • the network plug-in may be used for example to infer semantics on the type of data that passes through a link and use semantic routes and access control rules for transferring it to other systems and/or components.
  • the network card may comprise a semantic unit and/or include a semantic gate functionality (hardware unit/block or software) in regard to connections to other systems and/or components.
  • a semantic gate functionality hardware unit/block or software
  • the system may map endpoints to near field and far field features and objects; in some examples the mapping is achieved based on perceived depth - 144 - LUCM-1-1055Spec semantics, semantic time and/or semantic indexing.
  • the system maps endpoints to particular features, regions, characteristics and/or objects while preserving an overall hierarchical model for the whole semantic field.
  • the system may perform the mapping on raw data and/or other renderings of the artifact.
  • the raw data and/or renderings are augmented with additional information (e.g. annotations, bounding boxes, labeling, object/region boundaries, segmentation etc.).
  • additional information e.g. annotations, bounding boxes, labeling, object/region boundaries, segmentation etc.
  • the system may be able to correlate at various points (e.g.
  • the system maps the semantic model to areas or locations in a frame/image/capture and/or data rendering of the object.
  • the system may use semantic inference on the two models to derive the conclusion that the object has been rotated and eventually derive boundaries.
  • the system may use the movements of the detected features, edges and shapes between endpoints for semantic inference on a semantic network graph.
  • a composable or equivalent single model configuration in a mesh and/or hierarchical structure may be used.
  • the system may be able to correlate and infer semantics even further based on the light and luminescence characteristics found at each endpoint.
  • the visual semantics associated with each point may be coupled to semantic inference within the semantic network models.
  • the correlation may be described based on leaderships semantics which are essentially semantic attributes assigned high factors in semantic analysis (e.g. it is assigned a very high factor in semantic composition and is highly discriminative against other semantic determinations and/or artifact identification).
  • the system may use trajectory comparison and semantic analysis including semantic orientation for semantic inference and mapping of shapes, modifications, motions and boundaries (see picture).
  • the semantic model might map to a two-dimensional representation in some data renderings (e.g. images, frames).
  • the system may perform near to far field semantic inference and semantic model mapping.
  • depth - 145 - LUCM-1-1055Spec detection is available (e.g. electromagnetic scattering/reflection sensors, time of flight camera, depth camera, laser, radio frequency sensors) it captures the depth as well and couples it with semantic inference.
  • location-based endpoints is it to be understood that it may refer to a location in a particular context and/or semantic field.
  • the location may be related with physical coordinates, volumes, regions whether mapped to an environment, artifact (e.g. frame, image, object) and/or potentially with a location in semantic spaces related with sensing, displaying, mapping, rendering, meaning, symbol and/or language representation.
  • artifact e.g. frame, image, object
  • Depth detection helps the system to identity object edges more efficiently based on the detected difference in depth in the rendering or scene.
  • the depth detection may be based on arrays of photodetectors that either, expect a reflective and/or scattering response based on a transmitted semantic wave or semantic modulated signal, and/or based on time delay and/or rate of photons detection (e.g.
  • Semantic models may be updated based on observations from single or multiple observers.
  • the composed semantic field of multiple observers may not perform exhaustive coverage of a semantic field of a compact area (e.g. represented by an endpoint) or a semantic group.
  • the composed semantic field may not be exhaustive (e.g. covering all locations or endpoints) due to masking or obturations of endpoints in rapport with observers.
  • the system determines depth and distance semantics between objects by determining the time difference between when an endpoint semantics change from a particular semantic and/or group to another.
  • an object detection example if at a first time car A partially obstructs car B and an endpoint E is mapped in the field of view to the car A and later to car B maybe because the car A do not obstruct car B anymore at particular endpoint E the system detects the time semantics of changing conditions and/or semantics at endpoint E (or at the sensing elements associated with endpoint E) and determine depth, distance and potential further semantics based on such time semantics.
  • the system updates the risk factors of driving through particular endpoints and/or groups (e.g.
  • the risk factors are positively factorized when distance and/or movement semantics are further factorized (e.g. 60 APPROACHING 80 FAST, 80 FAST APPROACHING, GETTING VERY CLOSE, 100% FAST MOVING, MOVING FAST etc.); analogously risk factors may determine factorization of distance and/or movement semantics (e.g. the SLIDE RISK is high then the distance semantics are factorized and/or indexed accordingly).
  • the semantic inference may be coupled with the semantics associated to the movement of a sensor in order to correlate locations and artifacts in models and further control the sensor based on the inferred semantics.
  • An endpoint in the semantic network model may be mapped with elements in a sensor (e.g. a photodetector element in a photosensor, an element in a rf sensor) and the semantics at an endpoint are inferred based on data from the sensor element and attributes associated with the sensor elements.
  • endpoints may be associated with own semantic network models and/or with semantic groups of elements.
  • a semantic network model the semantics are assigned to artifacts in a graph and the system adjusts the semantic network model based on ingested data and semantic inference.
  • the semantic network graph comprises endpoints and links in a potential hierarchical structure with graph components representing another semantic network graph. In some embodiments the links are not oriented.
  • Semantic network models allow management of paths, fluxes, routes and semantic inference within the hierarchy. In an example, the system calculates the cost, drifts and/or factors of the semantic inference based on the levels in the hierarchy that need to be crossed to link or correlate two or more semantic artifacts.
  • each hierarchical level may be associated with at least one semantic artifact, factor and/or indicator
  • the system may perform semantic composition, semantic factoring, semantic cost/reward analysis while traversing the hierarchical structure.
  • the traversal may be determined or inferred based on semantic routes.
  • the system may use semantic budget and goal semantic (e.g. semantic, factor, goal/factor) analysis to determine the hierarchies that need to be coupled, composed and/or traversed and additionally may use access control rules to determine access within the semantic network model (e.g. between the levels of the hierarchy of the semantic model).
  • the system would not use inference on a level in the hierarchy until certain semantics or groups are not inferred at a first level of the hierarchy and an access control rule would allow the transition (e.g.
  • the transitions may be related with risks, targets, costs and other semantic factors and goal - 147 - LUCM-1-1055Spec indicators. Further, the system may use semantic analysis and semantic access control to determine the coupling and composition of semantic models and sub-models.
  • the endpoints in the semantic model may be connected via links.
  • the endpoints and links in the semantic network model may be associated with semantic artifacts.
  • the semantic network model is adjusted based on the semantic inference; the adjustment may include the topology coupling, gates, fluxes (e.g. published and/or access controlled), budgets and any other semantic artifacts associated with the semantic network model elements.
  • a semantic network model may be mapped dynamically or relativistic on an object.
  • the mapping comprises mapping the semantic sensing field in a more absolute way relative to the detector elements.
  • the semantic network model may be mapped statically on the field of view and if the camera consists of multiple photo detectors, it may include mapping of photodetectors to the endpoints and/or links in the model (see Fig 16).
  • model can be mapped in a hierarchical way with higher model levels representing potentially higher-level semantics; further the higher-level model levels participate in semantic composition only with the highest semantics in the previous layer/s that are allowed to pass between layers.
  • the semantic composition may be based and controlled based on semantic gating and/or access control.
  • hierarchy levels may comprise frames of at least one sensor semantic network map captured previously; such a structure, with links between endpoints (e.g. sensing elements or group identification) within or between levels provides scene development information.
  • the system may generate semantic frame maps for frames captured from cameras, vision sensors and other devices and which are used to map and/or store pixels, groups, locations and/or other features to the endpoints and/or links.
  • the system receives semantic scene artifacts (e.g. images/frames) and receives or infers semantics associated with them (e.g. potentially via same or other sources such as voice, text, display buttons and interfaces etc.).
  • the system may infer a semantic factor/drift/shift between its interpretation of the semantic scene artifacts and the received semantics (e.g. from a description), eventually inferring semantic/groupings factors in relation to the routing and grouping of particular semantics and the source.
  • the system detects camera obturation (e.g. lens or collimator covered by dirt, shadowed, broken etc.) based on frame processing and semantic - 148 - LUCM-1-1055Spec analysis.
  • camera obturation e.g. lens or collimator covered by dirt, shadowed, broken etc.
  • the system detects artifacts, patterns, areas and patches in the frame processing that do not change in time according with semantic analysis or the change is not conclusive; such artifacts create a static pattern and/or dynamic anomalies in the semantic analysis based on a mapped semantic network model and/or do not pass a threshold of certain static and/or dynamic factors for the semantic analysis. It is to be understood that the system may combine static and dynamic factors for assessing such obturation patterns.
  • the static artifacts comprising factors, mapped endpoints and patterns may be used to assess obturations.
  • dynamic factors as detected in the semantic network model and movement semantics and factors e.g. speed, acceleration etc.
  • dynamic factors as detected in the semantic network model and movement semantics and factors may further help inferring anomalies of static artifacts (e.g. if a post is MOVING then a static pattern and static semantic inference on the pattern in the semantic network model mapped to visual/infrared/terahertz image frames may be inferred as an obturation).
  • a post is MOVING then a static pattern and static semantic inference on the pattern in the semantic network model mapped to visual/infrared/terahertz image frames may be inferred as an obturation.
  • the system detects obturation it may mark the obturated area and models accordingly so that the semantic inference would consider and eliminate obturations’ noise.
  • the movement inference and trajectories of rain drops, wipers may be considered in a potential contextual inference (e.g. dirt present etc.).
  • the semantic coverage or capabilities of a semantic system are related to the capacity of generating semantic inference based on observations of the semantic field. Accordingly, patterns can be learned through semantic inference and mapped to various contexts and environments via semantic artifacts. In an example the system learns a pattern comprising at least one control rule and/or at least one-time management rule and represents it as a semantic route, semantic group or another semantic rule. Semantic routes and semantic rules may be associated with semantic groups.
  • the semantic rules may be associated with semantic artifacts such as semantic routes. Therefore, the routing and control aspect is important in guiding and breaking down the semantic inference. In an example, the access control would allow/disallow inferences based on semantic artifacts, rules and/or routes.
  • various classes of objects may be parts of a same semantic group and hence sensor data patterns may be related based on the group. For example, if two cars from different vendors share the same chassis, and we have data patterns attached to semantics for one of the cars during an off-road trip, we can then use related semantic inference artifacts to the second car and be able to infer potentially when that car goes off-road.
  • the suspension can be different and is important to correlate the two signals or data by taking in consideration the characteristics of the suspension (e.g. via signal or semantic waveform - 149 - LUCM-1-1055Spec conditioning and/or suspension semantics gating) and as such mapping this data to the semantic model and rules allow the improvement of semantic inference.
  • Causality may be modeled where patterns, artifacts, entities and/or groups influence one another.
  • the causality may be modeled as semantic routes, endpoints, links (e.g. oriented) and/or other semantic artifacts. [00854]
  • an oriented link and associated semantics represents/models a causality relationship between endpoint A and endpoint B.
  • Sensors and sensor devices and other data sources can flush data at predetermined and/or semantic intervals.
  • Sensors in general produce large data sets and then transferring it over a communication link or network might pose a challenge with both communication, storage and interpretation.
  • the sensor should be coupled to a low power processing unit or device which is able to intelligently draw inferences before transmitting it and/or semantic gating it to other devices.
  • the advantage of semantic systems is that they are able to understand the meaning, nature, value and importance (e.g. via factors) of information and hence its transfer requirements.
  • a semantic element/module/unit may store, expire and/or transmit information selectively and adaptively based on the overall context assessed at the system elements, potentially based on distributed intelligence.
  • the semantic model associates various semantics to various patterns of measurements, inputs, data and/or semantics.
  • the system may intelligently route and perform semantic inference on the distributed semantic hierarchy mapped to various devices.
  • semantic techniques like semantic groupings, semantic relationships and semantic composition the sensor data patterns can be further extended.
  • a general semantic model may be built to satisfy the requirement of a generalized audience, it might be that the semantic model need to be adapted to various personalized requirements.
  • a person, identity and/or semantic group might associate a IS COLD semantic to temperature of 50F while to another the same semantic might be associated with 60F.
  • personalized semantic models, sub-models and analysis are used based on semantic user preferences and profiles.
  • the semantic profile models may be hierarchical in nature where the user’s semantic profile models are based on views of another - 150 - LUCM-1-1055Spec profile/role model (e.g. potentially linked based on a semantic group), which in turn may be a view of a more general model and so forth.
  • the semantic views may be hierarchical. It is to be understood that the semantic profiling and views may be based on drive or orientation semantics associated with a profile at any level.
  • Semantic profiles may be associated and/or based on semantic groups. Thus, various profiles and their associated drive semantic artifacts may be activated based on the inference, identification and/or authentication of related semantic identities and groups (e.g. potentially in a hierarchical manner).
  • the system adapts the inference based on current and/or projected semantic identities and associated profiles.
  • the system may use semantic profiles and semantic gating to ingest and organize information from a variety of sources.
  • the system ingests text data from a source and create and/or associate the source of data to a semantic profile which is then used during semantic analysis; further, the system associates inferred semantic artifacts based on ingested data to source and/or inferred semantic profiles in a potential hierarchical manner. Further, the semantic profile may be assigned or associated to semantic identities and/or user preferences. [00866] The system may learn and/or infer sentiments based on semantic profiles and/or semantic groups. In an example the system infers that JOHN IS A GOOD BASEBALL PLAYER while further may infer that HE or THEY or TEAMMATES–- THINK THAT JOHN IS AN AVERAGE BASEBALL PLAYER.
  • the system uses semantic profiles to adapt inferences based on circumstances.
  • the system may use and/or factorize a semantic profile and its artifacts based on (inferred) semantic identities and/or semantic groups.
  • the system may factorize a semantic profile and artifacts of REAL FANS, COACH ZIDANE’S FRIENDS etc. and further use such factorized profiles and associated artifacts in semantic analysis.
  • a smart semantic sensor, device or component may have a way of knowing which semantic should report or allow access based on different semantic profiles, semantic analysis (e.g. semantic time) and/or possibly on authentication of a user and/or request.
  • semantic devices may incorporate only particular artifacts, hierarchies and/or levels of a more general semantic model thus, allowing them to efficiently infer particular semantic artifacts (e.g. lower level); it is to be understood that such models may be transferred - 151 - LUCM-1-1055Spec between devices and within the distributed cloud based on gating, access control, authentication, semantic profiles, device purpose, goals, contract goals/clauses and/or any other techniques as explained in this application.
  • a smart semantic sensor, device or component may report (data) to a semantic receiver based on an authentication of the semantic receiver and/or group. Further, it may report to other interested parties based on the profile owner publishing.
  • Jane publishes her weather interpretation profile and/or associated themes, rules, levels, endpoints, branches and thus, interested parties may obtain related and/or allowed weather information interpreted based on Jane’s profile and/or model. It is to be understood that the publishing may be based on access control and/or diffusion.
  • a receiver provides a version and/or a semantic identity of a semantic profile which need to be applied to (sensor) data. In case that the profile cannot be identified the smart semantic sensor, device or component may report the raw data without semantic interpretation.
  • the system may send an alarm if it receives a highly entropic version and/or semantic identity in rapport with the receiver’s request.
  • the semantic wave may coherently collapse only if the unit has the collapsible model (e.g. the model needed for coherent semantic inference).
  • Semantic groups of devices may have the collapsible model on particular themes, semantics, semantic routes and semantic profiles modulated in the semantic wave.
  • some devices or units may have access only to particular hierarchical levels (e.g. based on gating, access control) and as such, the particular unit might have a limited semantic coverage on the semantic wave, potentially lacking domain transfer, generalization and abstraction capabilities.
  • Differentiation in semantic coverage may be used to perform encryption for example, wherein only particular entities may collapse particular information or areas of semantic waves.
  • some units may be provided with a gated or profiled model and/or gate the model and inference based on particular interest semantics and semantic routes.
  • a gated model artifacts associated with the gated semantics, semantic routes and associated compositions are disabled, invalidated and/or eliminated.
  • the semantic flux published semantic artifacts may be accessible only within particular semantic groups and/or profiles. In some examples, only specific themes and associated semantic artifacts as specified by a user are shared and/or published with particular - 152 - LUCM-1-1055Spec semantic groups and so on.
  • Semantic inference produces semantic artifacts.
  • semantic artifacts are associated with raw and/or rendering data and/or rendering constructs.
  • Semantic artifacts may be reduced or composed to semantics related to shapes, features and/or colors; representations (e.g. maps, models) or other artifacts (e.g. visual artifacts, rf noise artifacts) may be used in inference and/or created during inference and so on.
  • Semantics may be inferred based on model, inputs, location, time and other data.
  • the system infers semantics and recognizes entities by composing multiple localized semantics, possibly based on semantic factors and applying threshold comparisons to the result.
  • the composition may stop once the system reaches a particular threshold, indicator and/or factors in an inference (e.g. based on a goal). Sometimes the system performs semantic drive and drift inference based on indicators only.
  • the system may recognize shapes by semantic inference and grouping on the semantic network model and/or map. As such, the system may group elements/artifacts based on semantic grouping and/or semantic linking.
  • the system has three endpoints EA, EB, EC which may be adjacent.
  • the system groups EA and EB because they are each associated with a semantic LEFT RNA.
  • the system groups EA and EC into an ABNORMAL group because EA is associated with a semantic LEFT RNA and EC with the semantic RIGHT RNA and the system contains a semantic composition rule associating RNA at LEFT and RIGHT in close proximity with a composite ABNORMAL semantic.
  • at least one of the endpoints EA, EB, EC or groups thereof may be linked (e.g. using a model link) via a semantic of LEFT or RIGHT to other endpoints ED and EF and groups thereof and the system infers groups based on similar principles and potentially clustering those artifacts and/or groups for more optimized memory access.
  • the grouping and clustering is hierarchical.
  • the groupings (LEFT, (ABNORMAL, EAC)), (NORMAL, EDF)) implies (ABNORMAL, EAC, EDF) or (ABNORMAL, EACDF). While the example doesn’t use semantic factors is to be understood that factors can be used as well.
  • the endpoint mappings and groupings may be associated to sensing (e.g. element identification, semantic identification, address, location, state etc.); alternatively, or in addition, elements and artifacts in the scene, image, frames, maps or renderings (e.g. pixels, area, locations, sub- scenes, sub-frames, objects) are mapped and grouped.
  • the - 153 - LUCM-1-1055Spec symbolic representation is used in this example to convey semantic artifacts, semantic models, semantic routes, and other semantic techniques and structures.
  • the mapping and association of semantics to raw data may allow the system to compose/generalize, construct/deconstruct semantic scenes and observations. As an example, if a person knows that in a downtown area there is a big mall and a two-lane road then the system is able to reconstruct the observations by combining the semantics and the internal representation (e.g. images, groups, models etc.) of those artifacts. [00880] If the system uses semantic groups of elements to capture information and perform inference (e.g.
  • the stored semantic artifacts may be reconstructed/projected based on the mapping, localization and/or semantics of the element/identifiers/groups to the projected environment.
  • the projected environment may be a virtual environment, remote environment, training room, simulated environment, operating room etc.
  • the system may replace or fuse semantic artifacts when there are no strong links or relationships to such artifacts (e.g. strong semantic routes, factors, view, view frames, fluxes, groups etc.).
  • a strong semantic link occurs when the semantic is part of a strong semantic route or a strong semantic trail.
  • the strong factorized semantic artifacts are the ones that are highly semantic factorized in absolute value.
  • a semantic trail may be high negatively semantic factorized when the experience of the trail execution had a high negative sentiment (e.g. the outcome was far off or even opposite from an initial goal or expectation; and/or the system learned strong new routes).
  • a semantic trail may be high positively factorized if the experience had a high positive sentiment (e.g.
  • the negatively semantic factors are higher in absolute value than the positively semantic factors for a particular semantic artifact each time when there is an inference on the particular semantic factors and artifact.
  • Orientation and drift inference between semantic trails and projections based on the semantic trails and further semantic routes, rules and/or goals may determine further factors, indicators, sentiments (e.g. nostalgia, regret, guilt, fear etc.) and/or intentions.
  • - 154 - LUCM-1-1055Spec It is to understood that the inference of high intention factors and/or cues may determine, infer and/or be associated with low entanglement entropy routes, goals and/or budgets.
  • Semantic factors may comprise positive or negative values to reflect positive or negative sentiment indicators, potentially in rapport with a view, route, view frame, group and any other semantic artifact.
  • Semantic artifacts may not be always represented with the original resolution of data; instead they are represented using an approximate of the original data or shape for the representative sampling, pattern or waveform. Thus, the system is able to reconstruct semantic artifacts in a more approximate manner by performing semantic inference/analysis on the semantic artifacts and/or the goals thereof.
  • Objects, observations and scene interpretation rely on semantic attributes inference. Semantic attributes may be related with characteristics of semantic artifacts and/or detected objects thus providing superior context interpretation.
  • Scene interpretation may comprise factorized estimation.
  • semantic artifacts may be assessed or compared with/within an area of the scene and based on comparison the system may continue to interpret the scene and area until the goals or factors in the assessment of the scene are achieved. As such, while a particular area of the semantic scene may not yield a particular satisfying result, the overall semantic scene may yield a satisfying result and be classified accordingly based on the semantics associated with the scene.
  • the signal processing components take in consideration the semantic scene composition. As such, the system may filter multiple sources of signals and/or assigns it to particular semantics or objects based on the scene interpretation and semantic model. In one example, the system filters noisy signals from a semantically identified artifact in the semantic scene (e.g.
  • the system may use the mapping of artifacts and/or signals to endpoints to perform noise reduction based on semantic analysis.
  • the system may filter low factorized signals, semantic scenes, frames and/or sources.
  • the system may perform semantic signal conditioning and/or gating based on semantic groups and/or hierarchies.
  • the conditioning signals, routing and/or gating is/are based on the members of at least one group/sub-group; further, such conditioning and/or gating may be performed on a composite basis, pipeline and/or hierarchical basis.
  • the conditioning and/or gating waves and/or signals are composed based on the artifacts (e.g. waves, signals, voltages, sub-groups, trajectories etc.) associated with the members of the semantic groups and/or hierarchies.
  • the system uses groups and/or hierarchies of semantic cells and/or units as a pipeline for applying conditioning (e.g. based on semantic groups, on at least one member in leadership positions, each member, sub-groups etc.).
  • conditioning e.g. based on semantic groups, on at least one member in leadership positions, each member, sub-groups etc.
  • the system may use gradients between such artifacts mapped to a layer of a semantic network model.
  • color gradients of or between semantic groups of pixels and/or regions are mapped to a semantic network model.
  • endpoints may be mapped to pixels, sensing elements and/or semantic groups thereof and oriented links represent the color or shade gradient between or detected by such artifacts.
  • frame gradient processing may be used prior to mapping the semantic network model to the processed frame.
  • the system may hierarchically calculate and map gradients. The system may use a hierarchical semantic model of gradients for inference. [00889] In similar ways with color gradient processing the system may use other gradient mapping to semantic network models.
  • Such gradients may include but are not limited to gradients on curves, shapes and/or mathematical graphs potentially in a multiple coordinate system (e.g. representing object edges, features, evolution of stock indices graphs, velocity graphs, acceleration graphs, correlation graphs/matrices etc.). Further, in such examples the intersection of gradients in the particular coordinate systems may determine new endpoints in the semantic network model.
  • Location plays a role in determining the semantic scene. Endpoints, links, semantic artifacts and/or semantic scenes are mapped to sensor elements or groups and the system performs analysis on the sensor elements grouping, their characteristics and identities. In some examples semantic analysis allow and/or is based on the inference of only the differences between/within semantic scenes.
  • the differences between semantic scenes and/or further semantic views may be inferred based on semantic drift and/or entropic factorizations.
  • the system may update the mappings, semantic groups, hierarchical levels and others semantic artifacts.
  • the update inference may include only the changes and/or comprise only affected artifacts.
  • the system may use difference in appearance between semantic model artifacts and/or semantic groups to interpret or render the scene.
  • the system may use gradient processed frames with semantic mapping.
  • the color gradients between regions, areas (e.g. pixels), features, sensing at elements and/or semantic groups of the former are mapped in the semantic network - 156 - LUCM-1-1055Spec model as links and/or endpoints.
  • a processed gradient image e.g. based on convolution and/or filtering
  • frame is mapped to the semantic network model.
  • mapping can take place in a recursive and/or hierarchical manner; in some examples the mapping proceeds based on semantic inference (e.g. achieving a semantic goal and/or an area/endpoint is semantically covered, the semantic view doesn’t produce new semantically relevant artifacts at particular levels and so on).
  • the semantic models may be mapped to rendering data and/or semantic scenes and the system performs inference on the semantic models mapped on different data rendering sets and/or at different times. In some examples the system performs inference only on the routes affected by changes of semantics (e.g.
  • endpoints and/or links changing semantics it is to be understood that the system uses hierarchical assessment of semantic updates and changes.
  • the system keeps layers of model mapped to specific artifacts, locations in order to maintain focus (e.g. follow a goal, a semantic route etc.) and/or preserve high level semantic coherence.
  • the system expires, disable, or invalidate semantic artifacts.
  • entire hierarchies or models may be invalidated. The invalidation may be based on semantic expiration (e.g. expire a hierarchical level or sub-model associated with a semantic or semantic group).
  • the system may need to steer/remap the element grid based on the semantic field interpretation.
  • the system may remap the elements or groups of elements to endpoints, links, locations, semantic artifacts and/or semantic scenes (e.g. based on address, grid, location, identification of the elements, semantic artifacts) while preserving the high-level semantic view frames and views.
  • the remapping may comprise updating the associations of the addresses, endpoints, links, locations and/or identifications of the grid elements to the semantic artifacts, locations and/or semantic scenes within semantic view frames and/or views.
  • mapping on any detected and/or rendered scene and artifacts e.g.
  • the system coordinates detection based on current semantic network model.
  • the system may point detection resources (e.g. beams of lasers, infrared, radiofrequency) to areas associated with the network semantic - 157 - LUCM-1-1055Spec model that don’t have associated semantics and/or the semantics expired and/or they don’t comply with a goal-based inference.
  • the network semantic model is continuously updated and refreshed based on the semantic analysis including semantic expiration.
  • the semantics/artifacts/signals of a semantic view frame may be mapped and/or stored in a semantic route.
  • the system may hold more than one view frame and the system compares the view frames in parallel. The comparison may be based on semantic orientation, gating, conditioning and other semantic analysis.
  • the view frames may comprise or be organized as semantic network models and the system performs inference on such view frames.
  • the system may assign a semantic budget to a view frame for reaching goals, indicators and/or factors; the budget may be updated as the inference develops.
  • the system manages the content semantic view frames based on semantic analysis and inference. Semantics may be gated on semantic view frames based on semantic access control.
  • the system uses semantic analysis on the goals, indicators and budgets to allow or disallow artifacts in the view frame.
  • the system disallows some inferred artifacts and/or provide rule updates, ask for feedback and/or generate alerts.
  • the semantic scene interpretation is optimized in context; contexts may be captured via semantic models, semantic orientation, semantic projection, semantic artifacts inferred via semantic analysis and captured in semantic routes, semantic views, semantic view frames.
  • Various semantic routes may be preferred over other. As such the semantics of those semantic routes may be assigned higher weights/factors than those that are not preferred.
  • a context may entail a collection of previously inferred semantics, semantic views, semantic view frames and/or semantic trails and as such the semantic system may assign or adjust the factors/weights for the semantic routes based on a factor assigned to each semantic in a leadership semantic group.
  • the semantic system may also adjust the factors of the semantics in the route, view frame and/or views based on such leadership; in some instances, the system may perform further factorization in a recursive manner (e.g. until a goal is achieved).
  • the semantics may have associated particular factors for each semantic route within a view frame and/or view. Further those factors may be also adjusted based on goals and leadership inference.
  • the factors assigned to semantics may determine the expiration and/or semantic decaying; in some examples, the factors may be associated to a quantum/quanta - 158 - LUCM-1-1055Spec and/or value/series/waveform in order to perform inference and/or decay on semantic artifacts; alternatively, a semantic and/or factor may be used as an indexing value to be applied to a quantum/quanta.
  • the quantum factorization may determine a time quantum.
  • the time quanta may be associated, determine and/or comprise semantic time management thus the system being able to “measure” or to rapport inference to the “passing” of time.
  • the quanta are an energy quanta or entropy quanta and the system “measures” or rapport inference to energy and/or entropy.
  • the management of semantic quanta uses semantic rule management.
  • the quanta is a signal and the system performs inference and decay based on a quantum signal, quantum wavelet and/or quantum signal data (e.g. amplitude, frequency, phase, envelope, spectral envelope, spectral density, energy, entropy, gradient, spectrograms/scalograms etc.).
  • the system performs the assessment based on semantic analysis on such data and/or waveforms wherein semantics are mapped to values/signals and/or are mapped via semantic network models on signal data (e.g.
  • a semantic wave collapse may occur when a semantic quantum factorization occurs for the wave. Semantic wave collapse may be used for example to model quantum phenomena. A semantic wave collapses onto semantics via semantic gating when there is a semantic view and/or model for semantic analysis on the semantic wave.
  • Semantic validity may be related with the validity overall or with the validity within an association with a semantic group, semantic route or trail. In the same way the validity may be related with association within a semantic view frame and/or semantic view.
  • semantic time management e.g. validity
  • semantic time management e.g. validity
  • semantic time management e.g. validity
  • semantic memory cache contains semantic artifacts (e.g. semantic routes) that are selected for semantic view frame and/or semantic view.
  • the semantic memory cache select/maintain relevant semantic artifacts in the context.
  • the selection and/or refresh may be semantic driven (e.g. inference, time management etc.)
  • Semantic memory caches may comprise semantic view frames and/or semantic views which may be organized in a semantic hierarchical structure.
  • semantic memory may comprise semantic artifacts and be organized as a hierarchical structure resembling semantic models. The addressability, control, management and transfer may be based on semantics, semantic analysis, marking and semantic waves.
  • semantic memory may be a DNA storage.
  • Semantic artifacts may be inferred and/or associated to DNA encoding/decoding, DNA storage, DNA chains and other DNA artifacts. Further, semantic artifacts may be inferred and/or be encoded in/as DNA chains, molecules and/or proteins. In some examples, the semantic artifacts are related with sequences of amino acids and/or sequence of a genes. In further examples, semantic inference (e.g. decaying, expiration) is associated with protein binding, protein lifespan and other protein associated processes. Protein complexes may be associated with semantic inference and analysis (e.g. protein semantic groups, formation/disaggregation semantic routes, semantic rules, time management, access control etc.).
  • Semantic orientation and/or route on the semantic memory cache may prove that matching semantic artifacts are not available or, the associated calculated cost is not acceptable or not matching a semantic budget.
  • the memory cache is reinforced with semantic artifacts which are more likely to occur within that orientation context and the factors associated with selection indicators of those artifacts and their components are increased every time when they occur in similar contexts. Factors can be associated with drive semantic indicators which can used for orientation/routing to particular goals (e.g. based on higher level indicators).
  • the selection indicators, contexts evaluations and routing may be evaluated based on absolute or relative semantic drift and orientation to drive semantics of the semantic routes, trails, shapes, views and view frames.
  • the system may use semantic selection and marking within the semantic memory. As such the system may select/deselect and/or activate/deactivate semantic artifacts based on semantic analysis. In some examples this is based on semantic routing within the semantic memory. - 160 - LUCM-1-1055Spec [00918] Drive semantics inferred at higher levels influences inference and orientation at lower levels and vice-versa. This may occur via semantic gating between the hierarchical levels. [00919] A semantic memory block may be associated with a theme or semantic and thus most if not all of the semantic artifacts related to that theme are memorized/cached there.
  • a semantic memory may be represented as a semantic network graph at logical and/or physical level (e.g. based on semantic hardware components) and the system performs routing and transitions including hierarchy transitions based on semantic analysis.
  • Semantic artifacts are grouped based on semantic analysis on multi domain contexts. Multi domain contexts comprise semantic analysis based on data received from multiple heterogenous sources of information and/or projected to different domains (e.g. sensing, cyber, network, user interface etc.).
  • Semantic orientation infers pattern of semantic artifacts potentially represented in a semantic network model. Thus, spatial shapes may be formed (e.g. composed) based on semantic orientation and/or semantic routing.
  • spatial shape patterns may determine semantic routes/trails based on the mapping of the points (e.g. locations) in the spatial shape to endpoints in a semantic network graph.
  • the mapping may be determined based on inputs from users, location and presence information, sensing, multi- domain data and other relevant information.
  • the system may perform semantic drift analysis of these paths, shapes and patterns.
  • the system is able to represent shapes and trajectories or perform semantic inference on shapes and trajectories; the system is able to perform semantic comparison (e.g. semantic drift, drive semantic decaying, semantic collapse) of two or more shapes and trajectories and/or derive semantic sentiments of shapes, trajectories, comparisons and infers further semantics.
  • the system may determine the similarity between two shapes and/or if the two shapes are related. Further the system determines a semantic attribute of a shape and/or identifies a shape. In another example the system determines indicators between two shapes (e.g. risk, likelihood, risk to reward, risk to reward likelihood etc.). In a further example the system determines a complexity factor indicator between two shapes. [00925]
  • the shape comparison may comprise semantic orientation. [00926]
  • the semantic group comparison may be based on a drive or reference semantic or semantic group (e.g. represented as a semantic network model) where all the - 161 - LUCM-1-1055Spec candidate semantic groups are compared against the drive semantic artifact based on semantic orientation, leadership and/or semantic drift.
  • the system may perform semantic route orientation.
  • the semantic routes are represented as semantic groups.
  • the system may perform semantic orientation in/on semantic view frames in rapport with drive semantics, semantic routes and/or semantic shapes.
  • the system may infer drive semantics that are compared with semantic routes and shapes (e.g. candidates).
  • the system may route the semantic inference based on the drive semantic artifacts in the semantic view frames and on semantic orientation and gating (e.g. hierarchical gating). Such inference may take place in any embodiments whether the semantic network model is mapped to physical artifacts, virtual artifacts, picture/video frames, locations etc.
  • the drive semantics may decay during inference. Once the drive semantic is decayed the inference on that drive semantic may stop. In addition, if a conjugate or entangled semantic is associated with the decayed drive semantic then the drive inference may continue on the conjugate or entangled semantic artifact.
  • the system may use and/or infer conjugate semantic artifacts (e.g. semantic identities, groups, shapes etc.). Entangled semantic artifacts may determine an entangled composition artifact. Conjugate semantic artifacts factors of the same/similar/synonym indicators may decay completely when composed; also, a conjugate indicator may be inferred and/or used in further inference when one of its conjugates is highly or maximal negatively factorized.
  • Conjugates may be related with antonyms, negations and/or conjugate variables in various domains (e.g. time-frequency, Doppler-range, position- momentum, voltage-electric charge, gravitational density – mass etc.).
  • the drive semantic may be replaced by the next leader semantic artifact in a semantic group associated with the drive semantic artifact.
  • the semantics and semantic profiles can be associated to sensor data and patterns using semantic inference based on localization. For example, if we identify via radio frequency or optical means that an object is traveling between various endpoints, we can record the patterns of sensor data and associate semantics to links between endpoints.
  • the system is able to identify through semantic inference on localization data and a semantic network model that a person is climbing a stair, then we can extract various features, patterns and rules from the data reported from accelerometers, gyroscopes and magnetometers and use that data in future uses to identify or augment a - 162 - LUCM-1-1055Spec semantic of CLIMBING.
  • the system may create semantic groups associated with CLIMBING for the person, endpoint, link and/or drive semantic artifacts.
  • the system can learn and create additional semantic rules (e.g. time management rules) based on detected semantic groups, semantics, semantic intervals and so on.
  • the semantic rule learning may comprise templates, models, semantic artifacts and drive semantics (e.g.
  • a semantic view is a snapshot in a semantic inference process which may be associated with inferred semantics and semantic model.
  • the system may decide that various semantic routes and/or shapes are not feasible at various times; however, if the system decide that a semantic route or link is feasible it may use the information related to the nodes in the route and possible current semantic view in order to initiate various actions, commands etc.
  • direct sensing e.g.
  • an actuation action is based on an access control rule comprising a semantic time interval with the system keeping a voltage or current value constant or changing based on factors, intervals and/or plans. Once the semantic time interval changes or expires another control rule may come into effect which may change and/or modulate the value further.
  • semantic rules, semantic interval, semantic timing, weighting, rating, factoring, budgeting and any other semantic rules may be combined in any way and may be specified as a combination of semantic artifacts, factors, quanta, etc. If a semantic route determined or is related with particular locations, then the system infers various lateral force semantics/factors/routes based on the potential trajectory. Alternatively, or in addition, the system may calculate the lateral forces based on the trajectory and activate only those semantic network model artifacts that are feasible and/or safe to follow. While performing those inferences the system takes in consideration the goals including driving goals where the area/locations/width are required to fit the dimensions of the portion of the vehicle requiring access at any given time.
  • the vehicle itself and pedestrians may be mapped to a spatial semantic network model and as such the system may perform the guiding based on - 163 - LUCM-1-1055Spec semantic routing, shaping, semantic model coupling, time management and any other semantic technique.
  • the system may communicate and coordinate with a semantic group of vehicles (e.g. within an endpoint) and/or based on semantic orientation.
  • the semantic models may be mapped relatively to the location of the observer (e.g. car, sensor, person etc.) and georeferenced and synchronized based on additional coordinate determination (e.g. land-based positioning, satellite positioning, landmark etc.).
  • a command may involve more than one actuation or sensor and hence the semantic model may encompass these interdependencies in semantic compositions, semantic rules, semantic routes, access control rules, semantic model, semantic factors and so forth.
  • the semantic is the command and the associated factors comprise and determining actuation values and/or indexing values. Further, the actuation parameters and/or values may be associated with indicators. In other examples the semantic is a composite specifying routes of actuation.
  • the system may infer based on observations on the semantic field a PEDESTRIAN IN THE ROUTE (or PEDESTRIAN HAZARD) and subsequently selection of a link/trajectory/route that avoids the hazard potentially coupled with a semantic route of 1.0 EMMERGENCY
  • the system uses factoring rules, time management, access control and the semantic network model to determine the factoring required for such commands (e.g. STEERING ACTUATE LEFT +1.1V, ACTUATOR FRONT BRAKE -2.2V, BRAKE ACTUATOR REAR 20PSI or, in case where unit semantics/indicators are hidden or implicit STEERING ACTUATE LEFT 1.1, ACTUATOR FRONT BRAKE -2.2, BRAKE ACTUATOR REAR 20).
  • the system may maintain all the available oriented links from the current locations and continuously update semantic routes that would allow the car to follow such oriented links and/or trajectories.
  • the system may factorize, eliminate and/or block from the models the links that are not feasible or pose a danger from the current location.
  • the links that may be associated with a car rollover are marked or factorized as high risk, blocked, invalidated and/or eliminated from the model.
  • the example provided has been using a BRAKING assessment for achieving the allowed trajectory - 164 - LUCM-1-1055Spec is to be understood that the system may have been using alternate or additional assessments such as ACCELERATE semantic artifacts.
  • the system inferred an EMERGENCY indicator type of situation that might have used an EMERGENCY orientation routing and or template for handling the situation. In the case that the situation have not been deemed as HIGH EMERGENCY (e.g.
  • the system may have used a different route such as 0.2 HAZARD
  • the system may provide indexing commands and factors such as INCREASE STEERING LEFT 0.1 EVERY 2ms UNTIL HAZARD GONE, ACTUATOR FRONT BRAKE +3, ACTUATOR REAR BRAKE +3.
  • the INCREASE STEERING LEFT 0.1 EVERY 2ms UNTIL HAZARD GONE route could have been inferred based on a semantic goal inference such as AVOID BOX IF POSSIBLE (e.g.0 RISK); thus the system infers the goals, drive semantics and routes as time management rules, potentially projected into the future or soon to be determined (e.g. UNTIL HAZARD GONE, UNTIL REACH 80 MPH, TO 80 MPH etc.).
  • the system may generate semantic inference rules for projections based on routes, templates and continuously adjust their factors (e.g. based on indicators such as feasibility, risk etc.). Further the system may store, adjust, invalidate or expire such rules based on the current or projected factors and/or goals.
  • a command may be associated with semantic budget rules comprising various actuation and sensor devices. Further, the commands may be associated with semantic factors, factor rules and plans (e.g. for indexing, linear/non-linear control, progressive/regressive control etc.). Commands may be exercised via semantics and factors.
  • Semantic commands may comprise semantics, factors and semantic routes, semantic budgets and/or semantic time management associated with those (e.g. PERFORM COVERAGE ANALYSIS UP TO DISK USAGE OF 50%); thus, the system infers the factors, budgets and/or limit semantic time management (e.g. DISK USAGE HIGHER THAN 50%) and associate them to the drive semantics and goals. - 165 - LUCM-1-1055Spec [00941] In some examples the system may infer drifts, biases or shifts to goals (e.g. applies negative decaying drift to limit semantic DISK USAGE HIGHER THAN 50% in order to maintain original goal of UP TO DISK USAGE OF 50%.
  • drifts, biases or shifts to goals e.g. applies negative decaying drift to limit semantic DISK USAGE HIGHER THAN 50% in order to maintain original goal of UP TO DISK USAGE OF 50%.
  • semantic budgets may be composed.
  • the system may asses or measure its effects and receive feedback (e.g. through sensing devices, semantic fluxes etc.) thus associating the inferred semantics of the response with semantic artifacts that generated the command; in this way the system may learn and develop its semantic model through action, effect, reaction and learning.
  • the system performs group dependent semantic grouping of any cause effect semantic artifacts and objects including semantics, goals, routes, groups, fluxes, objects, identities, factors etc.
  • the objects may include detected objects or objects providing feedback through semantic fluxes.
  • a goal may be achieved or not; when is not achieved the system may adjust the semantic factors of the semantic drive route in comparison with semantics and detected semantic trails in the semantic view and potentially adjusts and/or form new semantic groups and rules.
  • a semantic goal and/or command may be associated with a rendering task where the system uses the goal and/or command to plot objects/features on a rendering environment and/or device.
  • a semantic goal and/or command may be specified in terms of PROVIDE INFORMATION with the goal to INFORM USER and the system uses the semantic model to infer the best semantic route and semantic profile for achieving that command and/or goal which may vary based on semantic views and/or view frames.
  • the system may choose a semantic route which is associated with providing the results of semantic inference, semantic artifacts and the semantic factors on a display and/or dashboard style interface for example.
  • the system may use semantic models inference, semantic routes and semantic profiles to organize, view and position the information on the display and/or dashboard.
  • the system may use actuation to control devices and provide to the user the information that way.
  • Display or dashboard style interfaces may be generated based on semantic analysis and inference of semantic artifacts associated with symbols and/or semantics of symbols (e.g. graphical symbols).
  • dashboard and/or controls features - 166 - LUCM-1-1055Spec may be mapped to a semantic network model and the system renders the semantic network model based on the display controller interface which may comprise a semantic unit.
  • the semantic unit performs the rendering or display by issuing commands such as controlling display units (e.g. pixels) color, illumination, fading and so forth (e.g. via a voltage, current, optical signal, photon, laser, evanescent wave, polariton etc.).
  • a semantic unit may be used to display dashboards and controls by ingesting and/or outputting semantic artifacts associated with tags, scripts (e.g. HTML), templates (e.g. XSLT) and/or programming languages.
  • the system may be able to use one or more semantic units and display in any format based on semantic inference.
  • a semantic unit is used to render dashboards and/or other user interface controls via direct I/O and/or display surface control.
  • it may output, overlay and/or display other surface controls based on any other protocols, formats and transformations some of which are explained within this application.
  • the display surface control may entail using display/graphics frameworks and/or programming interfaces, display/graphics drivers control, display/graphics devices control and/or other display/graphics capabilities; display/graphics capabilities may be related to semantic units, graphical processing units, display/graphics cards and/or components, field programable arrays, other display and/or graphics components and any combination thereof.
  • the display output may entail overlaying gated semantic network artifacts on the display surface.
  • the display, overlay and/or linking of the user interface artifacts may be based on inferred semantics and/or associated artifacts mapped to locations and/or areas on the rendering medium (e.g. display, memory, buffer, graphic interface etc.).
  • the semantic unit rendering semantics are determined via semantic analysis.
  • display areas user interface controls and/or display components may be mapped to semantic view frames and/or views and the system uses semantic display plans to render those semantic view frames and/or views.
  • the semantic display plans may be possibly based and/or using semantic artifacts in the view frame/view and, the current goals, indicators and/or budgets associated with such view frames/views.
  • the system maps semantic network artifacts (e.g. endpoints and/or semantic groups) to areas on the screen comprising display interface controls (e.g.
  • semantic fluxes and semantic gating - 167 - LUCM-1-1055Spec uses semantic fluxes and semantic gating - 167 - LUCM-1-1055Spec to transfer information between endpoints (e.g. from a source to a destination) and thus between mapped controls.
  • the system may use semantic time management and semantic analysis including semantic routing to enable or activate the transfer of information between linked endpoints and to issue commands once the transfer is completed.
  • the commands may be based on semantics, semantic routes, semantic rules and further semantic analysis associated with an endpoint mapped to a user interface control (e.g. “COMMIT” link, button, auto-commit field etc.).
  • the system comprises a semantic trail/route of SERVICE FIELDS TRANSFER COMPLETED, COMMIT SERVICE REPORT and thus the system may use the COMMIT SERVICE REPORT semantic to identify an endpoint mapped to a commit button and/or the action to be executed (e.g. virtual click, send event, click, submit, reset, clear etc.).
  • the system identifies the display controls based on frame location mapping and associates identification based on composition in context and/or route (e.g.
  • the system may consider the contextual semantic identification and/or groupings (e.g. of JOHN DOE, dependent and independent semantic groups of artifacts, categories etc.) and/or semantic access rules and profiles thereof to gate, allow or block the flow of information, commands, inputs and/or control.
  • the system may generate semantic model artifacts, semantic groups and semantic routes for the identified display controls and infer the linking of such artifacts and associated semantic rules; such inferences may be overlaid on a display and further validated based on a user feedback.
  • the system may use I/O interfaces (e.g. display, touch, mouse, graphic cards, buses, sensors, actuators etc.), operating system interfaces, software and/or hardware interfaces, development kits, calls, events, memory, buffers, registers and/or combination thereof to perform detection, inferences and control.
  • the system may use images, frames and/or videos whether captured from a display, on a memory/storage and/or streamed.
  • the system may infer a disablement status and/or gradual (e.g. based on time management, resonance-decoherence operating interval, hysteresis etc.) activation and/or - 168 - LUCM-1-1055Spec rendering for the COMMIT related artifacts (e.g. selected based on (low) entropy, divergence, access, drift etc.) in the route and/or at an endpoint.
  • the system understands the context of operation based on semantic models.
  • the system is able to infer the semantic identification in context (e.g. SALES_NUMBER field of JOHN SERVICE REPORT form or window as captured from displays).
  • the system controls the access to various endpoints, areas and user interface artifacts based on semantic access control.
  • the system learns semantic trails and routes and further infer and factorize other semantic trails and routes based on semantic analysis (e.g. the system has a route for JOHN DOE accessing service reports and thus further infer other routes for JOHN DOE related with servicing and related artifacts). Further, the system may understand and complement the identification and actions from context (e.g. automatically asking, suggesting and/or pursuing actions, commits, transfers etc.).
  • Display controls and/or linking thereof may be associated with semantics, rules, gating, semantic routes and/or further semantic artifacts. In some examples, such association and/or links may be specified and/or inferred based on inputs from a user. It is to be understood that the linking of display controls may be associated to data sources, display artifacts/components, sensing and semantic groups thereof; further, the linking may be between at least two display controls and semantic groups thereof. [00958] In some examples, the system has or infer rendered or display objects (e.g.
  • a RED CAR, a MEDICAL CHART etc. as semantic groups and/or semantic artifacts; as the system detects for example a pointing device and/or touch sensing in an area associated with object’s artifacts it may select the whole semantic groups and suggest semantics based on projection and goal based semantic inference (e.g. MOVE TO RIGHT, CHANGE COLOR, OVERLAY EKG etc.).
  • the system may use other modalities for identification sensing of the rendered or display objects (e.g. RED CAR and/or LICENSE PLATE 0945 by voice, electromagnetic sensing identification etc.).
  • the system may use access control and/or further rules at a location and/or endpoint to implement time management automation and/or gate particular semantic artifacts and/or profiles.
  • a semantic profile of NURSE IN CURRENT SHIFT is assigned in a (facility) (display) area associated with MEDICATION WAREHOUSE a semantic route of SELECT MEDICATION, ENTER MOTIVE, (ALLOW DISPENSE), (DISPENSE ALLOWED) however, for a semantic profile of NURSE IN EMERGENCY the - 169 - LUCM-1-1055Spec MEDICATION WAREHOUSE (area) may be associated with a more general, less restrictive route of SELECT MEDICATION, (ALLOW DISPENSE), (DISPENSE ALLOWED).
  • semantic SELECT MEDICATION may be associated with user interface controls and/or fluxes associated and/or inferred for such semantics (e.g. SELECT MEDICATION may be associated with a DRUG combo-box (e.g. based on low entropy and/or drift) and/or flux while (ENTER) MOTIVE may be associated with a DISEASE (e.g. based on a low entropy in rapport with a composite MEDICATION MOTIVE) text field, combo-box and/or flux).
  • the system may deny certain operations in a route (e.g.
  • the display rendering may be partitioned between various semantic groups and hierarchies and as such particular semantics and/or rendered objects may have particular zones that need to be rendered and/or displayed into.
  • the system may perform for example resizing (e.g. RESIZE SMALLER), zoom in (e.g. ZOOM IN A LITTLE) and/or zoom out by further mapping objects and/or artifacts to larger or smaller semantic groups and/or higher and/or lower hierarchical levels based on semantic factors and/or indexing factors inferred using semantic analysis.
  • user interface controls which are associated and/or linked each to a semantic flux and/or group of semantic fluxes are rendered on a display surface.
  • the user interface controls may display for example gated semantics and/or graphics artifacts associated with the gated semantics.
  • the user interface controls may be arranged in a hierarchical structure with at least one user interface control comprising at least one other user interface control (e.g. a flux display button control comprises another flux display button control, a display button flux control comprises another display button flux control etc.).
  • a flux display button control comprises another flux display button control
  • a display button flux control comprises another display button flux control etc.
  • the semantic inference, rendering, display and control may diffuse and/or propagate based on the displayed and/or rendered semantic hierarchy, layers and/or overlays (e.g.
  • the hierarchy can be displayed by specifying semantic routes to be followed when selecting through the stacked user interface and/or graphics artifacts. It is to be understood that the selection can be achieved by modulating the semantic identities of the stacked artifacts including their semantics onto a semantic wave and applying composition with the pursued semantic route and/or search.
  • the semantics may represent commands and parameters and the semantic factors may be used to proportionally adjust the signal commands and parameters.
  • the system may infer action semantics based on semantic analysis including orientation.
  • the system may assess various drive semantics and semantic routes.
  • the drive semantics and/or semantic routes may be assessed based on their applicability in relation with the current goal and/or projected semantic view and/or view frames.
  • the projected semantic view/view frame may be based on what-if and/or speculative inference and may be coupled with semantic orientation.
  • the applicability may be established based on sensing data, ratings, budgets, costs, response time, semantic scene, semantic view, semantic factors, semantic orientation etc.
  • the system may group the semantic route with the context in which was applied and with the resulting action, reaction, effect, result and/or view which may be associated or represented as semantic artifacts.
  • the applicability of particular drive semantics and semantic routes may be assessed based on a semantic drift and semantic orientation between semantic artifacts.
  • the drift may be calculated as semantic distances between semantic artifacts (e.g. component semantics, trail and route, semantic groups etc.) wherein the distance takes into account semantic orientation, semantic analysis, semantic timing, location, access and/or other factors.
  • a semantic orientation distance is calculated based on a semantic drift which signify the difference between the drive semantic, semantic goal and/or projected semantics (e.g. projected semantic view) and the semantics of the semantic view.
  • the goals may be associated with semantic artifacts (assignable or not assignable to objects), factors and/or budgets.
  • the semantic orientation drift is based on overlaying and/or inferring drift model artifacts and sub-models on the trajectories to be compared. In general, when referring to semantic artifacts and semantic analysis on such artifacts is to be understood that they may be associated with semantic factors and/or semantic budgets.
  • the system doesn’t infer the drift and/or orientation based on little known signals/data or low factor semantics; thus, the system calculates the drift only based on higher factorization data, semantics and leadership.
  • the lower factor semantics and/or unknown signals/data/patterns may be associated with semantics within the hierarchical chain in the semantic view and maybe with the semantic sequencing; as such, the system may create inference rules including time management rules associated with the unknown signals/data. - 171 - LUCM-1-1055Spec
  • the engine assigns semantics associated with inputs and signal noise whether discrete or analog. Further, when the system encounters the signal/data/patterns in other conditions it may reinforce, change or learn semantic rules based on the semantic chain development.
  • the system may use semantic rules templates based on semantics, semantic groups, semantic routes, semantic shapes, semantic orientation, semantic factors, semantic rules and any other semantic artifacts in order to generate new semantic rules. Further, the system may infer new rules without a previous template.
  • the system uses the semantic network model to infer and learn semantics, groupings and relationships between them. Further, the system may learn semantic rules and groupings based on interactions, inferred semantics, semantic views, view frames potentially associated with goals, drive semantics and/or routes. Additionally, the system learns routes, rules and/or templates based on semantic orientation in semantic views and view frames when the semantic orientation doesn’t match inferred and/or projected semantic routes, view frames and/or views.
  • a semantic route may be collapsible to a composite semantic and/or drive semantic.
  • a semantic route may be collapsible to other semantic artifacts (e.g. an endpoint or semantic group comprising the semantics in the semantic route.
  • the collapse may be based on factorization, decaying or leadership of the semantics in the route or based on the route.
  • the semantic collapse may be used by the system for semantic learning wherein new semantic artifacts are formed, in a potential hierarchical, access controlled and/or gated manner.
  • Semantic drift may be associated with factors calculated based on semantic routing between the drifted semantics and the semantics in a semantic route. Semantic artifacts associated with higher hierarchical levels, concepts and/or themes are grouped together in the semantic network model. Thus, the routing and the calculation of factors (e.g. cost, risk or other indicators) between such clusters and/or hierarchies may allow for semantic drift and orientation inference.
  • Semantic views/view frames change based on semantic inference.
  • a semantic view/view frame comprises a plurality of semantic inferred artifacts potentially organized in semantic hierarchical and/or recursive structures (e.g. semantic network model).
  • Semantic views/view frames may be organized as, and/or be part of semantic hierarchical structures and memory. - 172 - LUCM-1-1055Spec [00973] I some cases, the semantic inference on the lower levels in a semantic hierarchy structure is more dynamic than higher levels. The higher levels may be associated with more generalized information and/or transfer knowledge. The access between levels of the hierarchy may be controlled via access control rules and semantic gates; in addition, the link between hierarchies may be achieved through semantic flux/stream. [00974] A semantic view changes based on ingested data or stimuli. Additionally, the semantic system may use time management rules to initiate changes to the semantic view. Further, a semantic view of a higher level in the network semantic model may change based on semantic inference from lower levels.
  • the semantic view changes may be associated with tuning, switching, enabling, disabling the sensing elements so that the system can use new sensorial data to identify and map the semantic scenes.
  • the ingested data may be data being exchanged between points, metadata detected through deep packet inspection, data related to code execution, protocol sniffers and/or connections between components/systems; further, the data may be based on vulnerabilities ingestion from various sources.
  • the data is ingested from sensors instrumented/embedded into the networking hardware/software, computing hardware/software or any other hardware/software entity.
  • graphics may be mapped, ingested and/or represented in the form of meaning representation (e.g. semantic network graph).
  • the graphics may be mapped to the semantic network model and/or mesh based on location, features, sensor elements and other techniques explained throughout the application.
  • the semantic view at particular hierarchical levels doesn’t necessarily change. For example, if the semantics and/or semantic groups remains the same at a particular level then the semantic view doesn’t change.
  • Semantic views/view frames may comprise multiple views/view frames.
  • the semantic route selection is dependent on the semantic scenes as detected by the sensors, semantic sensor attributes/capabilities, semantic flux/stream data or any other multi-domain data; as the car moves, the semantic routes are considered by the system for inference and/or action.
  • the semantic trails and/or routes can be organized in semantic route groups wherein groups of semantic trails and/or routes are coupled, rated and factorized/weighted together; the sematic route groups may be also connected via semantic trails and/or routes and so forth. As such the depth of the semantic route hierarchy can grow as - 173 - LUCM-1-1055Spec the semantic system evolves.
  • the semantic trails, routes and semantic route groups are associated or represented with semantic artifacts (e.g. associated with semantics) and may be mapped to a semantic network model or sub-model.
  • the semantic routes may be assigned to semantic artifacts (i.e. model semantics, semantic groups etc.) they may be represented as artifacts in a semantic network graph.
  • semantic routes may be used for routing within the network graph by comparing (e.g. drift) the semantics in the semantic route with the semantic artifacts associated with the graph elements.
  • One method of operation for a semantic system is one in which the semantic system may develop semantic views and/or semantic view frames using various semantic routes which in turn may trigger composition, and further routing.
  • the system uses semantic orientation, semantic projection and semantic drift analysis to determine and/or infer semantic routes and semantic shapes.
  • the semantic routes can have semantic factors associated with them; the factors may be dependent and/or calculated based on context and are used in selecting the semantic routes to be followed in particular situations.
  • semantic orientation may be used to select routes based on a semantic drift in relation with other semantic artifacts, routes and/or trails; further, the system may organize such routes and trails in semantic groups or select the routes based on semantic groups inference and/or leadership.
  • the semantic artifacts including the semantic routes are associated and can be identified via at least one semantic (e.g. name, semantic construct, group semantic etc.).
  • the semantic factors can be semantic rule and semantic time dependent. Thus, the factors may be based on inferred semantics and/or time management rules which may contain semantic time intervals.
  • semantic routes may decay with time; thus, the semantic routes can decay; in general, the semantic analysis and rules apply to semantic routes and their associated semantic artifacts.
  • semantic routes can comprise themselves and/or other similar and/or related routes in a potential recursive manner.
  • the similar and/or related routes may be based on similarity based on semantic orientation and semantic drift for example.
  • the system may use goal-based inference in which it determines the feasibility of various semantics and semantic routes based on targeted goals.
  • a post semantic system determines that another post is or will be in its path; the post system performs goal-based inference and finds out which are the feasible semantic routes within - 174 - LUCM-1-1055Spec budgets from the current semantic view to the projected semantic view.
  • the system may find multiple routes and potentially select them in the semantic memory or cache.
  • the system may select and/or mark one route over the other based on semantic orientation, semantic budgets, costs, rewards or any other combination of factors.
  • the semantic engine determines that a semantic route exceeds a semantic budget and has high costs/risk while has little rewards (e.g. based on ratings/weights/sentiment/decaying) in the projected semantic view and thus it doesn’t pursue the semantic route.
  • the system may not pursue the semantic route because is associated with a deny or block access control rule; the access control rule may be associated with the route itself and with a semantic artifact in a route.
  • the system may assess the potential occurrence and timing of the block access control rule when factorizing (e.g. weighting) or selecting the route.
  • the system selects and deselects the semantic artifacts in memory based on semantic analysis.
  • the semantic time management and access control is used in the selection/deselection process and influence the semantic routing within the memory.
  • the routes may be assessed based on hierarchy where a route at one level determine a route at a lower level and the system may mark, select and/or bring all those routes or only a selection in the memory/cache.
  • the system may activate and/or cache semantic artifacts, semantic routes and groups based on endpoint presence, location, semantic models and semantic orientation.
  • the system knows that within the CONFERENCE room there is a TV SET, PROJECTOR, PROJECTOR SCREEN, CONFERENCE TABLE etc. and thus it activates such routes and groups.
  • the system identifies the particular CONFERENCE room and have a previous semantic model and/or hierarchy for the room which may be activated/selected/cached, it may know the expected locations and appearances of such components such as TV SET, PROJECTOR, PROJECTOR SCREEN, CONFERENCE TABLE and so on based potentially on semantic orientation and further semantic analysis.
  • the system recognizes objects based on memory renderings of semantic shapings (e.g. projected, activated, selected etc.).
  • the system stores for a particular CONFERENCE ROOM or particular type CONFERENCE ROOM a TV SET comprising a BLACK TRIM, GRAY SCREEN, LUMINESCENCE REFLECTION and as such the system performs a composite memory rendering of the TV SET based on such routes and drive semantics.
  • the memory rendering is composed based on semantic models and it may - 175 - LUCM-1-1055Spec further be integrated at higher levels with the mapping of the TV SET in the CONFERENCE ROOM on previously stored location based semantic models or templates (e.g. higher-level semantic model and routes; and/or template for CONFERENCE ROOM layout).
  • templates may be stored by the system at higher levels of semantic model hierarchy.
  • the templates are based also on semantic rules, routes and/or semantic groups; additionally, such artifacts may be modeled in the semantic model (e.g. semantic groups may be modeled with endpoints representing group elements and links representing the relationship and/or causality; the hierarchy of the semantic groups may also be modeled via hierarchical semantic models).
  • the system may overlay semantic model templates on active and/or selected semantic models and draw inferences based on semantic analysis.
  • the system may infer that a CONFERENCE ROOM is ATYPICAL since it incorporates MONOCHROME DISPLAY.
  • the ATYPICAL inference might be less strongly factorized if the CONFERENCE ROOM is within a HOSPITAL environment and the MONOCHROME DISPLAY is used to display X RAY EXAM.
  • the system may create a semantic route and/or group for HOSPITAL, CONFERENCE ROOM, MONOCHROME DISPLAY with the MONOCHROME DISPLAY being less factorized and as such lacking leadership skills in inferences.
  • a selection may be based on current inferred semantics in a semantic view or semantic view frame, potentially at scene hierarchical or profile level. Further, a selection is augmented with semantics in the projected semantic view and potentially semantics inferred based on semantic orientation and drift inference between the views. In an example, a projected semantic view is based on what-if or speculative type inference. In other examples, a projected semantic view is augmented with the goal-based semantics. [00994] In some examples the system may use a plurality of projected semantic views and potentially inferring semantic drifts between them. The system may use the semantic drifts for semantic route selection and adjustment; further, the system may use those techniques in comparison with current semantic routes, drive semantics, view frames and views.
  • the engine may determine semantic budgets and pursue the semantic development between the current semantic view and the goal or projected semantic view, potentially adjusting the semantic route and budgets and applying the actions of the semantic inference until a budget is spent.
  • the inference towards the goal semantic view it may associate, reinforce and/or decay association grouping between pursued semantic trails, routes, drive - 176 - LUCM-1-1055Spec semantics and the current semantic view or the difference between the semantic views (e.g. via semantic orientation, drift, projection, composition).
  • a semantic view itself may be associated and/or represented via semantic artifacts and the association with other semantic artifacts may be represented as other semantic artifacts (e.g. semantic group).
  • the semantic system may infer a projected semantic of “CAR CRASH” involving a car in its path. Further, the system may detect the type of the car as being part of a category or part of a semantic group. As such, the semantic model may contain different avoidance rules based on the type of object or semantic group; further in the examples, the system performs goal-based inference with a goal of reducing impact on the driver side and thus the system applies the semantic automation in a way that will achieve that goal. In the first instance it may infer an approximate semantic route and continuously adjust it based on semantic inference, semantic orientation, semantic drift and semantic factor indexing. [00998] The system may use semantic factors or principles of operation (e.g.
  • Adaptability is an important aspect of a semantic system.
  • a semantic model enables adaptable systems due to its dynamic learning nature; the semantic model can be refreshed and adapted in real time or near real time to various conditions.
  • the system may need to maintain the real time status of semantics artifacts (e.g. groups of semantics) and as such the system updates the factors of those semantics based on time and semantic analysis.
  • the system maintains indications for validity of semantics (e.g.
  • SAFE TO DRIVE may asses the semantic factor based on sensing and/or inference from semantic fluxes related with weather, - 177 - LUCM-1-1055Spec road safety etc.
  • SAFE TO DRIVE indications are associated with a car/truck and/or a group of cars/trucks and the system maintains indications based on additional information related to ingested data relating to tire condition, consumable condition, servicing needs, schedules, the semantic time management for replacing those parts and others etc.
  • SAFE_TO_DRIVE decays to a certain level then the system may perform various actions such impeding the members of the group (e.g. trucks) to leave facility, send alarms, interact with IT and computing systems or any other action.
  • the semantic system may need to determine semantic groups for achieving a particular mission or operation.
  • the operations and missions are location, capabilities and time sensitive and as such a semantic inference engine will be very capable on determining the optimum artifacts to pursue the desired outcome.
  • the system may run goal- based simulations and projections and the semantic routes may then be used to detail the operational plan including the usage of assets and the most important attributes in various phases of the operation. If the operation doesn’t perform as expected (e.g. predicted semantic drifts and/or budgets from the selected and/or projected semantic routes and views is large) the semantic system will be able to adapt and compute new operational plan and semantic groups based on the current inputs.
  • semantic system In the cases of autonomous vehicles, it is important that they efficiently communicate based on semantic groupings of artifacts (e.g. vehicles, features etc.) and as such the semantic system considers the semantic fluxes activations and/or inputs based on those semantic groupings which are potentially based on location clustering and/or mapped to a hierarchy in the semantic network model.
  • the semantic flux coupling and activation may be based on semantic inference based on semantic routing which determine the soon to be travelled locations and/or other semantic factors.
  • Gated and/or published semantic artifacts may be made available, enabled and/or disabled in an access-controlled manner based on the authentication of the fluxes and access control profiles.
  • display controls and/or semantic groups thereof are displayed and controlled in such manner.
  • the simulation may entail inference on target indicators goals and budgets; a semantic view may be restored to a previous semantic time.
  • the system uses semantic orientation between a projected semantic view and the current semantic view to determine drifts and apply those to determine and/or update the current semantic view.
  • - 178 - LUCM-1-1055Spec Various techniques can be implemented in order to achieve adaptability and orientability. Such techniques may include but are not limited to any semantic analysis techniques including semantic shift, drift, orientation, entailment, synonymy, antonymy, hypernymy, hyponymy, meronymy, holonomy.
  • semantic interconnection and semantic model distribution enables semantic systems interoperability while extending semantic coverage and semantic field interpretation.
  • Semantic interconnection may consist in semantic fluxes which convey semantics between entities.
  • a retail store may be connected to a supplier semantic flux and ingest a semantic of “SHIPPED VIA GROUND SERVICE” for a particular item or a category of items; the internal model of the retailer may include a semantic rule that infers a semantic of “WAITING ARRIVAL” for the item/items which may have been coupled with an action (e.g. issuing a command to an IO controller, electro-optical component, sensor, analog and digital artifact, actuator, raising an alert, issuing an order to a software component, service or any combination of those); further, a REPLENISHMENT STATUS may be inferred and a semantic factor preserved to show that status (e.g.
  • the semantic factor may be adjusted in time (e.g. based on the progression through the supply/semantic chain and/or decaying) and may be associated with a value in a graph, chart, diagram, dashboard or any other graphical interface and/or virtual environment; additionally, the “WAITING ARRIVAL” semantic may be coupled with a budgeting and/or time modeling rule (e.g. time management rule) ; for example such a rule can specify that the WAITING ARRIVAL has a budget of 100 cost units and/or that the “WAITING ARRIVAL” semantic is valid for 5 days since has been shipped (e.g.
  • semantic artifacts may have associated a risk and/or success indicator that can be potentially calculated based on the risk or success of a negative or complementary semantic such as NON-ARRIVAL or MISSED DELIVERY.
  • the risk and/or success indicator is based on semantic time management wherein the risk factor and success factor change based on the semantics that are inferred in a semantic view frame; in further examples, the semantic view frame is associated with factors for goals and/or negative/complementary goals and performs inference on the factors in the - 179 - LUCM-1-1055Spec semantic view frame.
  • Such factors and/or goals may be semantic time bound such as the -2 WAITING ARRIVAL, STOP WAITING ARRIVAL, 10 NON-ARRIVAL and/or HIGH PROBABILITY OF NON-ARRIVAL semantic is inferred based on a circumstance semantic (e.g. DELIVERY AIRPORT BLOCKED) and the semantic view frame expires due to the timing goals not being achieved.
  • the internal semantics may be coupled with other internal or semantic fluxes semantics for composite inferences.
  • a semantic from a semantic flux may have been directly coupled with an action; in general, a semantic flux semantic is directly coupled to a critical action or command only when the level of trust of the external source and the semantic determination by that source is high.
  • the level of trust can be based on various factors including authentication, encryption, sequencing, timing, location, semantics and/or factors.
  • the level of trust is used for example to identify and/or factorize potential “too good to be true” gated/published semantics, semantic factors and/or budgets.
  • the time modeling represents an important aspect of semantic determinations and interoperability. For example, if the item/items wouldn’t have arrived in 5 days after SHIPPED then the system may have used semantic composition and expiration to infer for example “MISSED DELIVERY” instead of “RECEIVED” in the case of on time receive.
  • a rule could have been in place to send a “NON- DELIVERED” semantic to the supplier for the item/items in question which in turn may have been used in the internal model of the supplier to infer semantics and take actions.
  • the retailer may have been sharing the “MISSED DELIVERY” semantics, groups and indicators to a third party arbitrator, broker or ratings service that could use the semantic in its internal model to take actions, infer semantics, assign ratings and so forth; as such, the semantic flux of the supplier and/or logistic provider can be rated, weighted or factorized based on semantic determination; further, the non-achievement of the goal (e.g.
  • the supplier and retailer may agree on a semantic model and/or view that is used for interaction, gating, semantic analysis between their systems via semantic fluxes.
  • the semantic model view then can be shared and transferred between all the stakeholders including the arbitrator, broker, logistic provider, supplier, receiver and such.
  • the semantic model distribution and fusion can consist in semantic model replication, semantic themes model exchange, semantic view and/or view frame - 180 - LUCM-1-1055Spec exchange, semantic hierarchy and other techniques and architectures.
  • a particular hierarchy of a semantic model and/or view is exchanged.
  • the semantic exchange can involve a private or public infrastructure, cloud and may be based on semantic fluxes and gating etc.
  • the semantic exchange may be realized also via point to point, point to multipoint communication or broadcast (e.g. based on semantic groups).
  • the authentication or validations of exchanges may be based on a semantic analysis on semantic groups in the semantic network (e.g. risk and/or semantic factor inference initiated for a semantic group of fluxes). In addition, this may be coupled with semantic analysis in semantic trails and semantic routes which may determine grouping and/or routing between fluxes.
  • Semantic systems may exchange semantic models, views, themes and such.
  • those exchanges may be required to align the semantic systems to certain regulations or laws, to allow the synchronization and interoperability between systems, to enable real time collaboration, to improve and expand the semantic inference, to expand the semantic coverage and other circumstances.
  • semantic artifacts exchange may include expiration times assigned to the artifacts being exchanged.
  • the artifacts being exchanged may include a priority, cost, rating and/or any other semantic factor which is associated by the transmitting party in order to inform the receiving party of the semantic field assessment of the collaborative system.
  • the exchanges may include time models or time rules.
  • drone A operating in adverse environmental conditions may determine that is low on energy and wants to land in a shared environment.
  • drone B may transfer to the drone A semantic sub-model (e.g. semantic view, semantic view frame at hierarchical level with the semantics and related semantic rules, routes, drive semantics and/or operational commands required for a goal of safe landing.
  • semantic sub-model e.g. semantic view, semantic view frame at hierarchical level with the semantics and related semantic rules, routes, drive semantics and/or operational commands required for a goal of safe landing.
  • the semantic sub-model may contain expiration times and/or decaying rules (e.g. semantic factor, semantic time etc.) which are inferred by the transmitting or receiving party and represent the safe operation for the sematic artifacts and/or goals; the safe operation may be associated with an indicator for example.
  • the drone A it may not use the semantic artifact if its expiration time has passed or is about to pass. Additionally, the drone B may transmit more than one semantic sub-model, each having assigned factors/ratings to the associated semantic artifacts.
  • the - 181 - LUCM-1-1055Spec receiving party may use the factors/ratings and decaying in order to assess the best semantic routes potentially based on semantic budgets.
  • drone A might use the receiving artifacts and plug them in and fusion with its own capabilities (e.g. semantic network model, semantic rules, semantic routes).
  • the system uses factors associated with received artifacts and integrate them with its own artifacts.
  • the fusion based factors may be based on various factor plans that extend to semantic artifacts.
  • the operational semantic models, views, semantics and such may be selected and/or cached. When an expiration occurs, the expired semantic artifacts may be deselected and/or pruned. In similar ways, as the system manages the semantic artifacts it may also manage the received and/or plugged in semantic artifacts.
  • the semantic system may be on a private or public cloud that may be part or coupled to a brokerage provider or other services.
  • the semantic exchange service provides visual or other interfaces which allow the parties to configure the information exchange and may also display the factors associated with various semantics, semantic fluxes, providers, other semantic artifacts etc. It may use semantic inference to suggest various semantic workflows, providers, brokers etc.
  • interfaces may be inferred and/or coupled with the semantic artifacts and be available on such portals (e.g. UI controls, display controllers, feedback actuating elements/devices etc.).
  • these interfaces are selected based on semantic inference.
  • those interfaces may be based on user selections and/or profiles.
  • the system infers a semantic of ENDPOINT6 WARM BLANKET EFFECT which entails a semantic route ENDPOINT6 WARM COLOR FADING IN 2 SEC and further of PIXELS ENDPOINT5 TO ENDPOINT4 RED 10 GREEN 56 BLUE 99 BRIGHTNESS 5 FADING 8 IN 2 SEC which may translate in a semantic route of GROUP VOLTAGE (OR CURRENT) LED5 LED43.8mV FACTOR 1 and GROUP VOLTAGE (OR CURRENT) LED3 LED23mV FACTOR -2.
  • GROUP VOLTAGE OR CURRENT
  • a user may identify a trusted pool of providers and the system will switch between them based on semantic factors including ratings, costs, risks etc. Further the system may use semantic analysis for switching between providers (e.g. based on registered capabilities and/or semantic flux/gating). - 182 - LUCM-1-1055Spec [001023] Once is configured, inferred and/or learned on the semantic exchange cloud the semantic exchange model is transmitted to the parties and their semantic models and semantic fluxes configured accordingly. [001024] Any party can charge a fee for providing or allowing semantic interconnection services. As such, the fees may be charged on particular semantics, semantic views, semantic fluxes, number of semantic artifacts and any combination thereof.
  • any semantic exchange service or brokerage may adjust the quota for each provider based on factors, semantics, semantic factors, decaying and so on.
  • the scope of a semantic model is to properly and confidently represent the modeled environment in order to infer semantics in an accurate manner according with the modeled principles.
  • big data analytics uses a data lake and large processing of data for intelligence gathering, a semantic system uses the semantic model that is improved over time in order to process real time or just in time data.
  • Semantic engines may be used to perform semantic analysis and augmentation on big data lakes.
  • the system performs semantic analysis on the data from the big data lakes.
  • the big data lakes may include databases, files, clouds and any other big data storage and processing entity.
  • the system may use timestamps associated with data in the big data lakes for performing semantic time management analysis.
  • the system uses time-based series of images and/or frame processing for inferring past, current and/or projected views.
  • the image and frame artifacts are associated with inferred semantic artifacts, grouped and/or further analyzed based on semantic analysis.
  • the system may sort, ingest and/or output images, frames and/or renderings based on semantic time.
  • the system overlays semantic augmentation (e.g.
  • semantic models and/or text on images and/or frames based on semantic time.
  • the system may use safety and/or recovery routes and/or fluxes.
  • the system may factorize more the artifacts related to leaders having most popularity (e.g. measured based on the number and/or size of semantic groups, routes, links and/or further semantic artifacts - 183 - LUCM-1-1055Spec they belong to). It is to be understood that the system may infer and/or store popularity indicators and/or factors.
  • a semantic model is generated into a data center into the cloud and then transferred to the semantic models of other devices closer to the edge of the network such as gateways, sensors and controllers.
  • the semantic model may be selectively transferred to the devices based on the semantics and semantic rules that are valid at each gateway or controller.
  • the semantic model is distributed into the network between gateways and the gateways select only the semantic model artifacts or views that are related to their semantic capabilities.
  • the gateways may accept only the semantic artifacts or views related to the registered high factorized or marked semantics (e.g. of their sensors, sub-gateways or managed entities).
  • those registered high factorized and marked semantics reflect the capabilities of semantic groups or a hierarchical semantic topology structure.
  • the semantic infrastructure reflects the hierarchical, compositional and semantic grouping (clustering) nature of the semantic inference and semantic view.
  • the system couple semantic sub-models based on semantics and semantic groups.
  • two subsystems may select and/or exchange endpoints, group of endpoints, and/or sub-models based on their associated semantic artifacts. Further, the subsystems may select and/or exchanged sub-models based on semantic identification, semantic marking, semantic orientation and semantic shaping. Alternatively, or in addition, semantic gating is used for gating semantic model exchanges. [001037] In one example, the system selects or is instructed (e.g. by a user) to select leader indicators for which the smoothing and biasing indicators and/or semantic artifacts in a semantic groups of semantic units and/or memories should take place. In an example, the system determines value ranges of factors and indicators as goals, semantic intervals and/or drive semantics.
  • the system uses dissatisfaction, concern, stress and/or fear factors associated with zones and/or endpoints in the - 184 - LUCM-1-1055Spec semantic network model in order to exclude zones, endpoints and/or operations.
  • offensive behavior and/or driving uses satisfaction, likeability, preference and/or leisure factors associated with zones and/or endpoints in order to include zones, endpoints and/or operations.
  • the semantic smoothing may be based on projected inferences in rapport with defensive and/or offensive behaviors. In some examples the system may bias the offensive and/or defensive behaviors based on the assessment of the projected budgets and/or further factors (e.g.
  • Security is an important aspect of semantic inference.
  • a semantic system vets the information it receives in order to use it for semantic knowledge generation and semantic fusion.
  • semantic factors e.g. weight
  • a factor e.g. risk
  • the system detects objects through signatures, tags, annotations and semantic analysis thereof.
  • the semantic inference relies on increasing superposition, conditioning and noise to detection ratio on semantic sensor observations and measurements.
  • Semantic artifacts representing superposition signals and noise may affect and/or become leaders in various fields, locations and environments. It is possible that multiple leader artifacts exist.
  • the system In order to detect the original or denoised signal the system performs projected inference on leaders. For increased recovery of the original signal the system may need to infer original signal (e.g. based on semantic wave) leaders using semantic analysis.
  • the signal leaders vary in time based on the propagation environment. Some environments change leaders more often than others.
  • a partial shape or partial signature of an object might be detected in the semantic field via one or multiple sensors during a semantic field capture however, the presence of the object or signature cannot be inferred unless the leader context within the semantic field capture is understood.
  • - 185 - LUCM-1-1055Spec multiple semantic captures and signatures from various sensors may be used in order to eliminate noise, determine semantic leader artifacts and/or augment a particular feature, object, semantic or semantic group.
  • a semantic e.g. composite semantic
  • a semantic scene captured in a semantic snapshot of a sensor operating in highly dynamic environment e.g.
  • the semantic model might incorporate scene development view frames based on semantic routes and semantic model.
  • the scene development can include bringing semantic artifacts into a cache, assigning a higher selection indicator (e.g. possible based on semantic factors) determining leaders and drive semantics.
  • the system may provide means to gather/ask feedback and/or validate such inferences on videos and/or frames and adjust the semantic model based on the inputs. [001048] In dynamic environments, the system may need to compensate for the sensing and/or I/O platform movement and as such, semantic artifacts (e.g.
  • Adaptive modeling configuration consist in adapting the semantic rules based on the localizations and lands of the law. Hence once the vehicle is in a location it should adapt its models to the new principles reflected in the rules of the law. Semantic model roaming is the concept in which a semantic system updates and/or couples semantic models based on received instructions, location or based on other semantic factors.
  • the coupled semantic models and/or semantic profiles can be stored on an internal or external memory (e.g. a mobile device memory) and activated based on various semantics.
  • semantic roaming may comprise updating the biases and/or semantic factors associated with various semantic artifacts (e.g. semantic rules, semantic routes, semantic groups, semantic hierarchies, models etc.).
  • semantic system contains various profiles (potentially organized as groups) of semantics with semantic artifacts and relationships being factorized in different ways; factors may be derived from semantic analysis of language-based rules.
  • semantics for that specific law shall be enabled, re-factorized and enforced while other semantic routes that conflict with that law should be refactorized, disabled and/or de-enforced.
  • the adaptation to enforcement, or enablement can be achieved through semantic routes, access control rules with variable semantic factors, factor rules and leadership that are changed based on various considerations including the interpretation of the laws.
  • the semantic factors assigned/inferred for those artifacts reflect/include the importance and/or precedence that the semantic system assign to a particular semantic roaming or other collaborative embodiments.
  • the system may dynamically adjust the rules and leaders based on semantics and/or location.
  • the system may determine strong leaders, soft leaders or imperceptible leaders.
  • the system may use such leadership to infer composite drive semantics and routes including associated factors.
  • strong leaders may be based on factors that are bigger in absolute value than soft leaders factors; analogously, soft leaders factors are bigger than imperceptible leader factors.
  • the leadership factors are assessed based on orientations and drifts on groups of leader and/or goal semantic artifacts.
  • a semantic route can be chosen based on the location and association to a localized law interpretation; the semantic factors of various component semantics and semantic attributes may vary based on similar factors.
  • One application of semantic sensing is robotics and autonomous vehicles including smart post appliances. Full autonomy of vehicles may require semantic interpretation of data from various sensors attached to the car or other sensors that are part of the transportation infrastructure.
  • Localization and path identification are a critical aspect of self-driving cars.
  • the car localization and the localizations of objects in the semantic field are of importance, while the path identification, semantic routes and semantic composition provides safe driving in complex environments.
  • - 187 - LUCM-1-1055Spec Being able to assess the semantic field and anticipate/project the happenings in the semantic field ensures safer self-driving and self-determination transportation environment.
  • Car to car communication and car to infrastructure communications ensure more safety overall.
  • a sensing array e.g. plurality of sensors; RF, optical, laser etc.
  • the semantic system might control the car to stay on a virtual lane.
  • the virtual lane may be mapped to a physical lane. So, at one stage the car comprises in its semantic view a semantic (or semantic route) ‘FOLLOW THE LANE 1” while performing speculative/projected inference on what might happen in the next few steps. Based on additional sensor data from surrounding plurality of locations the semantic system might infer that “FOLLOW THE LANE 1” semantic is not appropriate and maybe “FOLLOW THE LANE 2” might be more appropriate in the new conditions and hence the semantic system infers a “CHANGE LANE” route and/or command that ultimately changes the high level semantic view to “FOLLOW THE LANE 2”.
  • CHANGE LANE command is then translated in routes and/or applied in sensor command and actuation data; the semantic command is a semantic artifact and as such may have a budget, timed factor and/or linear and/or non-linear signal modulation associated with it potentially via semantic factors, semantic budgets and/or plans, in order to optimally execute the command. Therefore, a semantic system may present semantic groups of semantic routes and leaders at any given time in order to ensure safety if the semantic view goals or semantic commands cannot be executed within budgets. [001058] A semantic system will incorporate base rules and principles that will ultimately derive all decisions of the system. For example, a basic principle of the semantic system might be that a “avoid a bad crash” should take precedence over any “property damage”.
  • the semantic model may incorporate user preferences.
  • those preferences are based on settings and semantic profiles comprising semantic artifacts stored on a mobile device.
  • a mobile device can be connected via different means like OBD interface to the on-board computer.
  • request-response messages can circulate via the interface between the on-board computer and the mobile device storing and/or retrieving user settings or feedback.
  • Users and devices can provide feedback on demand; sometimes the communication is achieved through semantic infrastructure (e.g. semantic gating, flux, stream etc.).
  • a communication bus and flux may be used to interconnect multiple sensing devices.
  • a semantic group formation request may be broadcasted/multicast on the bus and flux and the receiving devices determine whether they will be able to join or form a group based on the semantic view/view frame that they have.
  • the broadcast comprises semantic rules for group formation (e.g. group independent, group dependent) and/or leadership.
  • a semantic wave may be used for communications and/or broadcasts.
  • Semantic groups of devices may communicate on shared environments based on semantic waves and/or semantic wave collapse. In some examples, the semantic groups are associated with encryption means within semantic wave analysis and/or collapse.
  • the encryption may be based on public/private key assigned to semantic groups potentially in a hierarchical manner.
  • these encryption techniques may be based on hierarchical semantic analysis.
  • a semantic view and/or hierarchical level is unchanged if there are no changes in the inferred semantics and/or leaders in an interval of time and/or the projection of semantic analysis doesn’t yield new semantics or leaders other than the existing and/or similar ones; however, semantic factors of various semantics and leaders in the semantic view may change and potentially determine control commands based on those semantic factors and leaders. Other circumstances and elements might intervene that require changes of the semantic view (e.g.
  • the semantic view change may be assessed on hierarchical levels. As such, on a level (e.g. lower level) the semantic view may change, however on another level (e.g. higher level) the semantic view doesn’t change or only the semantic factors and leaders change (e.g. determining the way and order in which rules are applied).
  • the semantic system may hold and train various semantic units and models based on different rules. In an example those rules and/or drive semantics may be speculative, antagonized, opposed, complementary or any other semantic based combination.
  • Those rules may be linked with semantic factors and the system determines the rules and factors based on semantic orientation.
  • the system may perform inference on the main models and continuously fuse received feedback from the inference on the other models; the fusing may take in consideration the semantic orientation and semantic drifts between models and drive semantics.
  • the other models may function on different computing units for optimization.
  • the system may perform inference based on all models and use semantic fusion and semantic analysis on the inferences from all models.
  • the - 189 - LUCM-1-1055Spec models are coupled, fused and/or gated.
  • the inference and/or model coupling is achieved via semantic flux and gating.
  • the system performs inference on the same model using different drive semantics (e.g. antonym, different leaders etc.).
  • a system may receive and entire semantic model, semantic model view (e.g. based on hierarchical levels) or semantic theme model to be fused or replaced into at least one of its own semantic models.
  • a semantic fusion/exchange model/sub-model and/or rules e.g. gating
  • semantic fusion ensures the safety of the semantic exchanges and solves the semantic gaps between various representations and data.
  • the semantic analysis, semantic fusion and/or semantic gap processing may use semantic units. Alternatively, or in addition, vector processing units may also be used.
  • Segmentation of various aspects of computing and computing infrastructure achieve better security, reliability and resilience.
  • the semantic inference and automation can coordinate the segmentation of network, data, functions etc.
  • the semantic inference may determine the spawning of a new virtual machine on demand in order to deal with an increased workload or a detected threat.
  • the new virtual machine can mimic another machine that is being targeted while semantic system monitors the new virtual machine for malware, threat analysis coupled with semantic analysis and learning.
  • the virtual machine may contain means to control various segmentation functions such as segmentation of the data, I/O, memory, network, functions and the semantic system controls the security and access control to these functions and segments.
  • segmentation functions such as segmentation of the data, I/O, memory, network, functions and the semantic system controls the security and access control to these functions and segments.
  • Various segments can be assigned various semantics and the system control access at these segments and/or functions based on the semantic analysis, gating and control.
  • the virtual or host machine may have hot plug or plug in points or connections which connect virtual logical functions and/or interfaces to hardware (e.g. achieved via semantic gate and/or semantic flux), thus allowing semantic automation of the resource allocation for optimization and cybersecurity.
  • the system may want to infer a semantic group that have (high) energy (or bandwidth, or other indicator) consumption and has a minimum risk of disruption if its (associated/used) flux channels bandwidth factors and/or budgets are toggled down, thus allowing the system to save bandwidth; once the bandwidth factors and/or budgets - 190 - LUCM-1-1055Spec are changed the members of the semantic group may reassess their leaders, views, routes, rules and/or inferences as well so to adjust to the new conditions.
  • the bandwidth factors may be based on cost factors and/or budgets at various semantic times; analogously, cost factors may be inferred from bandwidth factors and/or budgets at various semantic times.
  • a first indicator factor may be inferred based on at least a second indicator factor and/or budget.
  • Semantic systems must comply with a set of hard coded rules that are conveyed via the infrastructure (e.g. land of the laws for transportation systems) and hence, some semantic routes should be enforced as opposed to other routes.
  • semantic beaconing or smart posts can be used to enforce specific paths and routes.
  • construction areas may be signaled with semantic posts and semantic beacons broadcasting construction zone type, factors and other semantics and indicators (e.g. comprising semantic groupings, instructions, routes, instructions and routes for semantic groups adherence and any combination thereof etc.).
  • beaconing data is important and hence the ability to validate the location is critical; further, the authentication may be augmented with challenge response inquiries, location information and other authentication techniques (e.g. multiple factor authentication, distributed semantic ledger).
  • the localization and identification entails interpreting the reflected signals from illuminated artifacts, objects, targets, environment and so on.
  • the reflected energy or signals comprise backscattered or transmitted energy or signals from the illuminated artifacts and are used for localization and artifact identification information.
  • Backscattered energy and signals may be used to identify objects and/or object types based on their radiation signature, scattering, appearance, components, behavior, features, identification and semantic analysis.2D and/or 3D images, renderings, frames, video streams may be created from these returns as well. [001077] It is to be understood that such images, renderings, frames, video streams may comprise raw, uncompressed or compressed formats (e.g. bitmap, RGB, HSL, HSV, JPEG, PNG, wavelet, mpeg, quick time, avi etc.). [001078] The semantic engine uses hierarchical threshold calculations and semantic analysis to capture signals and/or spectral imaging, detect objects, localize them, associate semantics with the objects in the scene and perform further semantic analysis.
  • the system uses such diversity techniques for faster and more efficient communication.
  • semantic inference and analysis a system is able to adapt easily to new available intelligence related with its functionality because the system fuses various multi-domain sources of data and inputs.
  • Semantic segmentation is performed based on the semantic network graph and semantic analysis on the graph. In some examples access control rules are coupled to the semantic network model to perform segmentation of the endpoints and its mapped artifacts and/or features.
  • deep learning neural networks and techniques e.g.
  • convolutional, recurrent neural nets, LSTM may be used for semantic segmentation to tag, score and/or assign confidence for objects, object types and/or related areas and/or volumes in the 2D and 3D renderings and correlate those with the semantic scene interpretation. It is to be understood that tags, objects, object types, area, volumes and/or scores/ratings are mapped to semantic artifacts and/or factors.
  • 2D and 3D inference is used for planar, volume and/or artifact (composable and/or composed) printing and/or fitting purposes. In some examples, the system may infer semantic artifacts associated with areas and volumes and further associated with procedures, technologies and materials for printing.
  • a printer controller implements semantic gating, flux and budgets the system may perform semantic analysis on manufacturing various parts, assemblies, modules (e.g. for posts etc.) etc.
  • Semantic analysis and/or localization on 2D and 3D areas and volumes may associate and/or relate them with particular actions and/or commands.
  • the actions and/or commands are inferred based on particular areas and volume semantic artifacts composed with further semantic artifacts (e.g. flux, user etc.).
  • the sensors that perform electromagnetic detection may comprise transceivers, transmit units and/or receive units. They are coupled or comprise elements such as antenna, lenses, radiative elements, charging/discharging elements and others.
  • They may comprise elements and circuits including filters, amplifiers, oscillators, resonators, mixers, shifters, phased locked loops, synthesizers, correlators, voltage adders, frequency/voltage dividers/multipliers, analog to digital converters, digital to analog converters, SOCs, pSOCs, FPGAs, microcontrollers, peak and phase detectors, laser diodes, varactors, photodiodes, photo transistors, photodetectors, multiplexers, memristors, semantic units, processors, (semantic) memories and other components.
  • Such components may include metamaterials, metasurfaces, nanostructures, nanoantennas, nanowires, nanopillars, - 192 - LUCM-1-1055Spec nanoposts, polaritons and so forth.
  • the components specified above may be tunable and/or combined to form channels used for transmitting, receive, detection/sense and any combination of those.
  • such components may include other analog and digital components, semantic interfaces, circuits and blocks comprising diodes, transistors, capacitors, inductors, resistors, switching elements – e.g. FET, GaAs, GaN, SiGe, SiC etc.
  • a FET field effect transistor
  • a depletion type FET transistor is normally on and to turn it off, a negative voltage relative to the drain and source electrodes is applied.
  • the enhancement type transistor is normally off and is turned on by positive voltage applied to the gate.
  • Such voltages are controlled by semantics and semantic factors.
  • the semantic units and/or components may be assigned semantics and/or factors and the system routes the semantic fluxes, semantic waves, voltages and currents to components based on semantic analysis including semantic gating.
  • a semantic unit distributes a semantic wave to such components and circuits based on semantic analysis, gating and routing.
  • semantic channels are established based on and for semantic analysis, semantic fluxes, gating and streaming.
  • Physical phenomena can also be modeled through semantic analysis.
  • Doppler shifts may be modeled through semantics.
  • the radiating elements transmit generated waveforms which when reflected by an object, artifact and/or target are interpreted based on semantic analysis that apply the Doppler shift as part of semantic composition.
  • the received measurement and/or signal from any of the channels and/or antennas are composed and/or conditioned based on the transmitted/received waveforms which may be pulsed and/or continuous modulated in time and/or at intervals of time. Pulse and/or waveform compression techniques may be used for improving the signal to noise and signal to interference ratio.
  • the system may generate the waveforms based on semantic analysis and/or semantic conditioning.
  • a waveform is related to a composite semantic route and/or semantic wave while the compositional semantics specify wave type, frequency, amplitude, phase, time management, access control etc.
  • the compositional semantics are directly associated with the outputs (e.g. voltages, chirp, basic waveform) for the continuous wave and/or pulse signal modulation; additionally, the composite semantic is associated with a semantic rule (e.g. time management, access control, semantic factoring) that will further determine additional waveform and/or chirp modulation parameters including phase, amplitude and time modulation (e.g. via time management, factoring, indexing etc.).
  • the semantic analysis and learning implies correlations - 193 - LUCM-1-1055Spec (e.g. via semantic group, semantic model and/or semantic routes) of various inputs, measurement, signals and so on from various channels, streams and sources.
  • the transmit and return signal parameters e.g. frequency, amplitude, phase etc.
  • the signal envelope may be inferred, generated or represented based on a semantic network model, semantic artifacts and/or semantic group.
  • signal envelopes and waveforms may resemble paths/routes/shapes in the semantic network model and the system performs semantic inference on the semantics in the path (link and endpoint semantics).
  • the system may perform semantic orientation and/or drift inference on various semantic waves, signal envelopes and waveforms for comparison, projection, speculation, inference, sentiment analysis, authentication and so forth.
  • the system may overlay a plurality semantic network models, levels, hierarchies and/or artifacts and infer compositional semantics for the artifacts that intersect; the intersections may refer to intersections of zones, envelopes, charts, maps, graphics, graphs and/or other plotted and/or rendered artifacts; in addition, or alternatively the intersections may refer to intersections of semantic network artifacts potentially mapped to such zones, envelopes, charts, maps, graphics, graphs and/or other plotted and/or rendered artifacts.
  • the system may comprise a link Link1 from EP1 to EP2 of a level L1 which, when a level L2 is overlaid intersects with a link Link2 from EP3 to EP4.
  • Link1 has a semantic attribute of Attr1 and Link2 has a semantic attribute of Attr2
  • the endpoints EP1, EP3 and EP2,EP4 may collapse and/or be grouped into EP13 and EP24 and an associated link Link12 between EP13 and EP24 is associated with a composite sematic attribute between Attr1 and Attr2.
  • endpoints EP1, EP4 and EP2,EP3 may collapse and/or be grouped into EP14 and EP23 and linked via a link (e.g. Link 1) associated with Attr1 from EP14 to EP23 and linked via a link (e.g.
  • Link 2 associated with Attr2 from EP23 to EP14.
  • the system performs mapping, overlaying and/or analysis on intersections, points and/or zones of interest.
  • at least two rendered signal/s envelopes intersect in at least one point in time Pint (e.g. potentially displayed as time series charts/graphs). If the system maps EP11 and EP12 to a first envelope/graph/chart (e.g. EC1) and infer and/or assigns at least one semantic (e.g.
  • the system may perform semantic analysis based on such artifacts and their associated attributes in rapport with the mapped semantic artifacts and/or further assignment to the mapped semantic artifacts.
  • the system may display graphics elements based on inferred semantic attributes and/or factors. For example, the system uses an inferred stroke factor and/or semantic attribute to draw the graphs/graphics of endpoints and/or between endpoints with the corresponding stroke value.
  • the system may use semantic indexing for indexing user interface and/or display artifacts/controls parameters and/or semantics; further, it may index size of borders/fonts, positions, scroll, resizing etc.
  • semantic indexing for indexing user interface and/or display artifacts/controls parameters and/or semantics; further, it may index size of borders/fonts, positions, scroll, resizing etc.
  • deep learning techniques e.g. convolutional networks
  • the signal and/or noise may be modeled through semantics and thresholds calculations coupled to semantic inference. Specific formulas may be indicated and/or identified through semantic artifacts; the system may use such semantic artifacts in a composite fashion together with the semantic artifacts associated with the formula parameters.
  • the system may adapt the formula semantics based on the context. As such, the system may change leaders and/or assign higher semantic factors to a semantic representing one formula set over another based on the semantic view.
  • the semantic system may use formulas for semantic inference. As such, a formula set may comprise multiple semantics in a composite fashion and may be part of semantic routes and/or semantic rules.
  • the system may use semantic representation of knowledge. In one such example, when velocity signature estimation (e.g. Doppler) formula is applied, the system composes the semantics (e.g. including semantic factors) associated with parameters and constants based on semantic rules associated with formula components.
  • the system uses - 195 - LUCM-1-1055Spec semantic analysis in a composite fashion to infer the speed of movement, potentially associated/represented through a semantic factor.
  • the system may use a mathematical (co)processor to process the mathematical functions embedded in the formulas.
  • Such a (co)processor may be connected to semantic units via buses, semantic connects, analog to digital converters (ADC), digital to analog converters (DAC), digital signal processors (DSPs) and/or any other technologies mentioned in this application (e.g. Fig.24 A B C D).
  • the semantic model may comprise rules for matrix multiplication.
  • the system comprises rules and routes of type MATRIX PRODUCT, ADD ALL PRODUCTS OF EACH ELEMENT IN A ROW WITH EACH ELEMEMT IN A COLUMN, NUMBER OF ELEMENTS IN ROWS – THE SAME–- THE NUMBER OF ELEMENTS IN COLUMNS.
  • the system may comprise a semantic network model mapped to a rendering of the matrices where the elements in matrices are mapped to endpoints and the template artifacts that need to be multiplied are connected by oriented links (e.g.
  • the elements of the first matrix e.g. left product element, left matrix (LM), left matrix element (LME), matrix A, first matrix etc.
  • the elements of the first matrix are mapped to higher level line endpoints (e.g. LME line 1, LME line 2 ... LME line n, etc.) comprising the line elements endpoints and, analogously, the elements of the second matrix (e.g. right product element, right matrix (RM), right matrix element (RME), matrix B, second matrix etc.) columns are mapped to higher level column endpoints (e.g.
  • the system links the line endpoint with the column endpoints and further may represent and/or collapse them into a higher level endpoint which may be linked to an element in the result and/or rendering of the result.
  • the system stores a template of matrix multiplication based on semantic models and uses it to perform the product operation for example. It is to be understood that the system may infer at least partially such semantic network models by corroborating the semantics artifacts from the captured and/or rendered data and its location and by further matching it against semantic routes, templates and/or rules; in some examples such semantic routes and/or rules may be provided, read, received and/or inferred.
  • the system needs to multiply AONELINE (11, 12, 13) with BONECOLUMN (11, 21, 31) and thus the system performs the groupings such as * (11,11), * (12, 21),* (13, 31) based on the matrix multiplication template and further + (+ (121, 252), 403) or + (121, 252, 403) which may map to the result matrix element.
  • the mathematical operations may be performed by the semantic units in similar - 196 - LUCM-1-1055Spec templating fashion (e.g. template for number multiplication, addition etc.) and/or by a mathematical (co)processor unit (s) as depicted in Fig.24.
  • the (co)processor units are linked to the (other) semantic units via semantic flux connect and thus, the semantic unit may use any of the semantic flux functionality to couple and/or challenge the (co)processor unit which may expose and/or gate capabilities and/or budgets.
  • the links and signals between the semantic units (SU) and coprocessor (COP) units may be connected and/or converted by using any combination of analog to digital conversion (ADC), digital to analog conversion (DAC).
  • ADC analog to digital conversion
  • DAC digital to analog conversion
  • the (digital) signals on the links may be further processed and/or gated in digital signal processors (DSP).
  • DSP digital signal processor implements the semantic gating functionality (e.g. related with the coprocessor).
  • a diversity of energy transmitters or transceivers may work collaboratively to map the semantic field and generate more accurate information.
  • Modalities that use electromagnetic radiation to sense or scan the semantic field are employed; sometimes they may generate imaging and video artifacts of the return signals. These modalities can operate in various ranges of the electromagnetic spectrum including radio waves, microwaves, infrared, visible spectrum and others; they may include RF sensors, photosensors, laser sensors, infrared sensors and others.
  • Sensors can move and the captured areas may overlap, or they can capture disjoint areas of the field.
  • Sensors receiving electromagnetic energy in any spectra may use hierarchical threshold calculations (HTC) for object localization; additionally, the calculations may be used to derive a semantic attribute of an object that refracts, transmits, scatters and/or backscatters received energy from a modality (e.g. camera, laser) via semantic modulated radiation.
  • Laser/optical type emitters/elements are used to emit radiation, potentially semantically modulated and conditioned, and the number or amount of received backscattered photons, semantic quanta (e.g. energy), backscattered energy, charged energy levels is plugged in as the number of reads in HTC (hierarchical threshold calculation) algorithms.
  • semantic modulated transmit signals e.g.
  • Photon detection may be based on the energy levels received in a particular wavelength. Sometimes the photon detection number and/or energy levels may be associated to semantic factors.
  • the threshold calculations may be used to identify the nature of an object (e.g. material, texture, color and others). The system establishes thresholds that may be associated and adjusted based on semantic factors, indexing and further semantic analysis.
  • the factors may be based on semantic composition wherein each composition semantic is inferred and/or assigned a factor and the composite semantic weight/factor is a calculation (e.g. sum, average etc. inference) of the compositional factors.
  • the factors are calculated based on factor rules where the factors vary with the semantic inference and analysis.
  • the factors are indexed and/or calculated based on semantic routes, semantic views, semantic intervals, composition, factor rules and plans, semantic rules and any combination of the former.
  • the selection of semantic rules may be as such controlled based on inferred semantic factors and indicators.
  • a semantic of “SHAKE” with high weight of 0.9 from a car sensor may infer or assign a negative weight to a semantic of “PLEASANT” and and/or a positive weight for a semantic of “THRILL” and/or “FAST” and/or “FAST SHAKE”.
  • Those inferences and factors may be based on leader semantics capturing sentiments in particular contexts and using various semantic profiles.
  • a semantic profile may comprise semantic artifacts (such as routes, models, rules, waves) and allows the system to particularize inferences and environments (e.g.
  • the system displays, views, sensing, fields and/or semantic artifacts etc.) based on profile’s artifacts and/or access control.
  • the semantic factors are established based on semantic time intervals and/or factor intervals/thresholds.
  • the factors, factor intervals/thresholds may be used to infer semantic artifacts and to select semantic rules, semantic routes, shapes and groups.
  • the system pursues various routes of inference based on one or more semantic rules selected through semantic inference, factors and factor rules. Further, the system infers and determines which of the semantic rules including factor rules, semantic intervals, semantic groups and other semantic artifacts yield the best results (e.g.
  • Photon and counting detection may be an example implementation of the HTC using a diversity of transmit/receive sensor elements. Photon counting or quantum energy charging/dissipation at a diversity of elements can be integrated and heavily benefit from the diversity techniques presented in the HTC. This is due to their susceptibility to noise - 198 - LUCM-1-1055Spec which is highly alleviated through diversity techniques and HTC. Further, such sensing elements structures and layouts may be mapped to semantic layout models (e.g. endpoints mapped on location and/or elements, semantic capabilities, semantic identification, component or any combination of the former).
  • semantic layout models e.g. endpoints mapped on location and/or elements, semantic capabilities, semantic identification, component or any combination of the former).
  • the diversity techniques and HTC are used to determine semantic attributes of the illuminated surfaces.
  • the photon count or the energy received in particular wavelengths at the elements are used to derive the semantics related to position and the color of the illuminated surface.
  • the elements may be tuned to absorb or count only a narrow wavelength and the system is able to be more precise in color/attribute estimation.
  • the photosensor may be comprised from an array of elements or photodetectors that are managed through a semantic engine.
  • the photosensors may be grouped semantically, grouped in a hierarchical manner or any combination of the former.
  • the system may perform detection by varying the detection granularity based on hierarchy levels.
  • the mapping of those sensors to the scene may consists in mapping particular scenes and/or the overall scene or field, with potentially combining this structure in the hierarchy of logical and/or physical mapping layers.
  • the semantic inference also uses hierarchies to perform semantic inference. [001107]
  • Fig 16 depicts elements, sensor or semantic unit components grouped based on hierarchies and/or semantic groups.
  • an endpoint and/or link is associated a composite semantic based on semantics associated with its component endpoints and/or links.
  • compositional endpoints and/or links may be associated to semantics inferred for a higher hierarchy endpoint and/or link.
  • transitions between endpoints at one hierarchy level are allowed, disallowed and/or controlled based on semantics inferred at higher hierarchy levels.
  • inference associated with encompassing endpoints at a higher hierarchy level is used to allow, disallow and/or control the semantic inference at the lower levels.
  • the semantic collapse may be controlled in a similar way.
  • the radiative sensors or sensor arrays may change the radiative pattern, direction, strength, polarization, phase and frequency.
  • the system may modulate, represent - 199 - LUCM-1-1055Spec and/or store semantic artifacts and semantic waves based on such values, identities, patterns, attributes and parameters.
  • a clear advantage of a semantic system and engine is that the radiative front ends may be easily swappable. Alternatively, the front ends may use adaptors to adapt to various transmit/receive spectra, frequencies, polarizations and so forth. In some examples, the adaptors may comprise multispectral and/or hyperspectral filters.
  • the system may use readers with antenna elements operating in the visible spectrum to perform HTC. As such, the radiated energy for such sensors or interrogators will be in the visible, ultraviolet and/or infrared spectrum of the electromagnetic domain.
  • the readers may have a mix of interrogators or sensors working in various domains and/or spectra.
  • the sensor elements operating in the visible, ultraviolet and/or infrared spectrum may comprise nano-antennas operating in optical frequencies.
  • the nano-antennas allow the use of readers and interrogators in the visible domain and/or infrared domains.
  • the sensor elements may comprise nanopillars and/or nanoposts. Such elements may be used in electromagnetic radiation (e.g. light) control such as steering, phase control, wavefront control, focal length control, dispersion, polarization and other characteristics.
  • electromagnetic radiation e.g. light
  • Plasmonic materials and structures have subwavelength properties due to conversion of light to surface plasmons which allow confinement and concentration of energy to very small volumes.
  • Surface plasmon polaritons allow the guiding of incident light of longer wavelengths in shorter nanostructures and wavelengths allowing for nanoscale sized waveguides, detectors and/or modulators.
  • Plasmonic materials are used as opto/plasmonic couplers, splitters, photodetectors, switched, waveguides, modulators and so on.
  • Nanoparticles or nanowires are used as sensor elements; their absorption band in the visible, ultraviolet and/or infrared spectrum and the polarization - 200 - LUCM-1-1055Spec sensitivity allow for advanced sensing in small factors; in one example, they can be used to detect various material properties. Accordingly, they can be used with HTC techniques and semantic analysis for semantic inference.
  • Nanowires and/or metasurfaces e.g.
  • nano- antennas may be used for capturing radiation at optical wavelengths and generating/guiding the polaritons; meshes of intersecting nanowires are used to capture a current induced by the polaritons based on direct energy transfer between the nanowires and metasurface (near field and proximity effects) which may contribute to improved absorption and detection capabilities in various materials layouts and applications.
  • nano-antennas are built using structures (e.g. pairs, hexagonal structures, other shaping structures) of metallic particles with dielectric gaps with energy concentrated within the structure or at the surface.
  • Gratings and/or meshes of elements may form larger structures and sensing surfaces (e.g. antennas, photosensors surfaces etc.).
  • the system may activate and/or tune such elements to achieve dynamic capabilities (e.g. tune the radiation pattern, parameters and/or receiving groups based on frequency for optimal transmit/receive; time the element activation and/or tuning for controlling polarization); it is understood that such activation and tune capabilities may be based on frequency, time intervals (e.g. semantic time intervals), signal amplitude and any combination of such parameters and/or semantic analysis.
  • dynamic capabilities e.g. tune the radiation pattern, parameters and/or receiving groups based on frequency for optimal transmit/receive; time the element activation and/or tuning for controlling polarization
  • activation and tune capabilities may be based on frequency, time intervals (e.g. semantic time intervals), signal amplitude and any combination of such parameters and/or semantic analysis.
  • Polarization might be detected by scattering of energy and/or light between nanowires and/or within the mesh.
  • Multiple polarization interferometry may be used as enhancement to mesh metasurfaces surface plasmons capabilities.
  • dispersion elements/metasurfaces are coupled with absorption elements/metasurfaces for achieving enhanced capabilities (e.g. focal dispersive guiding, phase detection, spectral sensing etc.).
  • Such meshes may use layouts of one or multiple layers with either dispersive and/or absorption properties and elements being used at various layers.
  • a nanoposts or nanopillars layer is used to capture light and disperse and/or guiding it to a plasmonic layer.
  • Multispectral and hyperspectral sensing may be achieved by controlling (e.g. via semantic analysis) the meshes and/or layers. Further semantic analysis, 3D mapping and rendering may be used to analyze hyperspectral cubes of captured spectral data.
  • Frequency and/or photoelectric selective mesh surfaces may operate in the radio-wave, microwave, terahertz, ultraviolet, infrared and/or visible range of electromagnetic spectrum.
  • the RF subsystem may be coupled to optical sensors and devices (e.g. laser diodes, photodiodes, avalanche photodiodes- linear/analog mode, Geiger-mode, etc.; edge-emitting lasers, vertical cavity surface emitting lasers, LED, fiber laser, phototransistors) to generate laser beams and scan the field.
  • a signal can be modulated in amplitude, frequency, phase, pulse/time/width in analog and digital domain that is potentially used in both RF and optical sensing.
  • the radio and/or light wave modulations may be achieved based on direct semantic analysis at carrier level or indirect semantic analysis to a baseband level.
  • Radio frequency and/or optical front-end components may be used and/or coupled for rf and optical modulation using analogous carrier waves.
  • the optical modulation may be either pre-emission or post emission.
  • the pre-emission (e.g. direct) modulation is achieved by superimposing (e.g. compose) the semantic modulated signal (e.g. semantic wave) on the drive current, bias current or diode current (e.g. for LEDs, laser diodes).
  • an optical source e.g.
  • laser diode, LED emits a continuous wave which is then modulated (e.g. via semiconductor electro-absorption, electro-optic modulator, semantic gate etc.) and conditioned.
  • modulation may be achieved for example via semantics on or applied on currents, voltages, adjustable refractive indexes, phase, frequency and any combination of those.
  • the semantic modulation may be analog and/or digital.
  • the optical emissions may be controlled through arrays/grids/meshes of elements.
  • the system may encompass array/grids/meshes of modulators (e.g. for frequency/amplitude/phase pulsed or wave/CW chirpings and orientation in the field of view).
  • Light pipes, optical fibers, light collimators, nanowires may be used to focus and/or cohere emissions.
  • the optical devices may be comprised from a lens or assembly of lenses; in other cases, they may comprise optical antennas (e.g. plasmonic).
  • the receptors may include arrays of photon detection elements, photon energy charge pumps, plasmonic nano-sensors etc.
  • Photon detectors elements may include photomultipliers, single-photon avalanche diodes, superconducting nanowire single-photon detectors, transition edge sensor elements, scintillation counters, photodiodes, phototransistors and others.
  • Photosensors may use passive or active sensor pixels; in addition, these sensors may use organic or inorganic materials.
  • Graphene is a material used in photoreceptors for improved spectrum sensitivity, resolution and power consumption.
  • Photosensors may include multiple spectra capabilities for sensing visible, ultraviolet or infrared light.
  • the applied voltage may be associated with semantics and/or semantic factors and the system may use semantic models mapped to mesh substrates to issue semantic commands (e.g. voltage control) to elements in the semantic model mesh (e.g. edges/links, endpoints, elements, groups) based on semantic artifacts identification, mappings and/or location.
  • semantic commands e.g. voltage control
  • elements in the semantic model mesh e.g. edges/links, endpoints, elements, groups
  • the mesh semantic mapping selection may encompass mappings of elements to the mesh semantic model and selection of those based on the semantics associated to the mappings. It is to be understood that the elements and/or the applied voltage may be associated with semantic analysis.
  • the system may map and/or divide a substrate into multiple virtual substrates based on mapping to the hierarchy in a semantic model. Thus, parts of a substrate may be mapped to a level in the semantic model. The mapping may be disjunct or overlapping between semantic network model hierarchy levels, model and semantic artifacts.
  • Arrays of photodiodes, phototransistors, nano-antennas, plasmon metasurfaces may be used in photodetectors and photosensors.
  • photodetectors and photosensors we include any display and holographic display layouts, capabilities and surfaces of based on such technologies.
  • Photodetectors and photosensors might have different internal configurations of transistors, nano-particles and/or components; they might be organized as a mesh. In an example, a photosensor or group of photosensors is organized as or based on a group of plasmon polaritons waveguide mesh. [001135] Because the elements are sensitive to various spectra, their layout is it therefore of significant importance in sensor applications consisting in a large number of detectors. - 203 - LUCM-1-1055Spec [001136] In order to improve sensing for hyperspectral photosensors a semantic engine may be used for advanced semantic grouping interpretation and control of the photodetectors or microelements.
  • the semantic engine may determine the optimal amount of energy voltage applied to the mesh based on the semantic inference on the mesh inputs and other sensorial and/or resource inputs. Further the semantic engine may control the absorption of photons, electromagnetic energy, electrons, and further photoelectrical related parameters in the mesh based on semantic analysis and inference (e.g. time management, access control, semantic groups, semantic leadership etc.).
  • semantic analysis and inference e.g. time management, access control, semantic groups, semantic leadership etc.
  • a semantic mesh/grid is formed wherein multiple semantic network models are laid down on top of each-other; the stacked configuration may form a logical and physical hierarchical layout.
  • the links may intersect and the semantic system defines new endpoints at the link intersection and assigns new composite semantics on the new endpoints and links.
  • the composite semantics may be combination related with the semantics assigned to a lower or a higher-level links and/or endpoints, and potentially with semantic groups of endpoints.
  • the system enhances the semantic mesh grid to encompass finer and more granular understanding of semantic scenes and field.
  • the system may pursue finer semantic grids when focusing on particular areas/locations, goals, leaders, drive semantics and factors.
  • the semantic grids may be formed in layered and/or hierarchical configurations.
  • the layered and hierarchical approach increases the semantic resolution (whether disjunct or overlapping) optimizes performance, knowledge transfer and control.
  • two grids communicate through a higher level in the semantic network model.
  • architectures may foster domain knowledge transfer between microgrids of elements, layers/hierarchy and/or endpoints.
  • the system performs up (e.g. abstract, higher level, connected level) and down inferences within the hierarchy based on goal inferences.
  • the same approach works for any embodiment of the semantic network model.
  • the semantic network model was mapped to a grid of sensing elements.
  • the semantic network model is mapped to locations and/or artifacts in images/frames (e.g. pixels, objects, zones, shapes, boundaries etc.).
  • - 204 - LUCM-1-1055Spec [001141] Some of these examples were based on semantic analysis including composition, semantic routes, time management, access control, rating and weighting, diversity drive/routing, semantic leadership, hierarchical and probabilistic approaches.
  • sequencing and semantic factors e.g. weights and/or ratings
  • the semantic rules, routes and semantics are ordered and/or selected based on semantic factors and semantic factor rules and may determine and/or be determined based on orientation, drift, sequencing and/or other semantic analysis.
  • the factor rules are created and updated based on inputs and feedback from a variety of sources. Sometimes those factor rules are updated based on inferred semantics, inputs from a user and/or any other sources as presented in this specification. [001142] Those factor rules may themselves be associated with semantics and the factors associated with the semantics representing the selection factor. As such, the semantic inference techniques are used to infer new factors and factor rules, infer semantic groups including factors and factor rules and so on. In the case that there are multiple semantics associated with a semantic group (e.g. of rules, artifacts etc.), the system may perform semantic analysis on the multiple semantics and infer the overall prioritization, selectivity, importance factors and/or leadership.
  • semantic group e.g. of rules, artifacts etc.
  • the semantic indexing factors establish space-time dependencies based on semantics.
  • the sensing elements e.g. photodetectors
  • the system may determine a coarse semantic determination at first and go through the logical and/or physical semantic hierarchy until a semantic threshold and/or leadership is achieved.
  • the system may increase the resolution of the semantic determination through semantic indexing; as such, in a vision model (e.g. optical, rf) new semantic artifacts are added to a semantic model mapped to the semantic field representation (e.g.
  • the system may use various layers of the mesh and/or model to achieve a particular desired resolution.
  • the semantic indexing factors may be used to determine the progression in - 205 - LUCM-1-1055Spec resolution and time of mesh/model adjustment, activation and semantic inference.
  • the system uses indexing to infer semantic groups of elements and perform zooming and/or adjust resolution (e.g. as a result of progressive semantic compression/decompression and/or encryption/decryption potentially based on semantic wave).
  • the system may overlay models (e.g. mapped to pixels, elements) and create new artifacts based on color. Alternatively, or in addition, can overlay models and determine composition and analysis on the overlaid models.
  • the system maps a grid of endpoints and oriented links to the semantic field and increases the grid density through semantic indexing or, in a further example, the system overlays or enables/activates semantic grids on top of each other based on semantic inference and detects intersection points between the semantic artifacts (e.g. endpoint and links).
  • the system may determine the intersections including oriented links and/or endpoints (e.g. source and destination) and, at intersection points, the system may map new endpoints and create new links based on the composition of the semantics intersecting in the composed grid.
  • Semantic groups may be used to determine the new mapped semantics in the grid; semantic composition may be used to determine new semantic groups based on the new determined semantics, sensor elements mapped at locations and other semantic artifacts.
  • the system uses semantic orientation (e.g. based on drive semantics and/or leadership) to detect drifts and patterns between layers, routes, shapes, paths, trajectories etc. [001148]
  • the system is able to infer indexing factors based on mesh overlaying. As such, if there are two endpoints and a third one is overlaid in between the first two endpoints the system may infer proportion and indexing semantics based on the semantic layout, mesh/grid, links, hierarchical structure, semantic factors, semantic shifts, drifts and semantic orientation.
  • Semantic view frames comprise semantic determinations and semantic routes that may be used by the system for semantic inference.
  • the system may use a semantic bias for altering the factors for particular semantics, fluxes, routes, view frames and/or views.
  • a bias may be applied (e.g. composed) on drive semantics, semantic routes and other semantic artifacts. Alternatively, the bias may be applied as an alternate or additional drive semantic, semantic route, semantic artifact and/or leader.
  • Factors associated with the semantics in the semantic route may be biased; this bias may be used to ponder the other semantic factors including route components semantics.
  • the - 206 - LUCM-1-1055Spec semantic biasing may be inferred based on semantics, indexing, semantic analysis or be based on inputs including inputs from a user.
  • the semantic bias may be used to influence (e.g. counter-balance, control, increase) the confirmation bias, the risk aversion or risk predilection bias that may reflect in the semantic model and/or collaborative semantic fluxes.
  • the semantic bias may be used for example to identify signatures, compatibility, preferability, trusts and other semantic factors between semantic groups of artifacts, units and/or fluxes by evaluating (e.g.
  • semantic factors, artifacts, routes and/or views used by such group (members) during a particular inference and/or challenge may be effected on semantic groups and/or between the evaluator system and further units, fluxes and/or semantic groups. Further, such analysis may be used to infer semantic groups and further semantic artifacts as explained throughout application.
  • the system may use aggregation of semantic biases (e.g.
  • the bias may be used to compensate for various language and sensing accents (e.g. based on semantic identities), identification characteristics, sounds, waves, noise, parameterized characteristics, signals and/or other artifacts that may have an influence on increasing detection factors (e.g. related to signal to noise, signal to interference, superposition and so forth).
  • semantic biases and semantic indexing coincide and as such any semantic techniques applicable to one may be applicable to the other.
  • Semantic indexing, bias, access control, gating, time management and/or further rules may be associated and/or used to adjust biasing voltages, currents and/or further bias parameters; further, semantic inference and/or learning based on correlations between biasing parameters and/or values and changes in operating characteristics of (biased) elements may occur.
  • semantic resonance, decoherence and/or damping may be used to determine operating points/intervals.
  • the system may adjust, control and/or optimize inference, gating, operating points/intervals, actuation, motion, power (budget) delivery, torque, (rotational) speed etc.
  • Groups of photodetectors may be coupled together in a concentrator/controller/semantic unit and be coalesced and/or controlled through that semantic unit; further, those photodetectors may be associated with a semantic unit group.
  • the elements in a cell can be connected via nanowires, with the control of the voltage threshold of the nanowire circuit or transistor being semantically achieved.
  • Semantic units may be also connected to other semantic units and/or photoreceptors; they may be connected and routed through semantic flux, gates, routes etc.
  • the semantic units run a semantic component which composes the sensor inputs semantically while being controlled by semantics itself. Further, the elements in the cells may be composed in semantic groups.
  • the semantic units may comprise at least one semantic cell.
  • a semantic cell may or may not comprise at least one conditioning semantic unit front end block (SU FEB) (e.g. Fig.19 A B C). Examples of semantic units and cells are depicted in Fig.21, 22 and 23.
  • a semantic cell may comprise SU FEBs in a switched and/or hierarchical architecture (e.g. Fig. 21, 23).
  • Fig. 21 shows an example of configuration of a switched architecture while fig. 22 shows an example of a semantic unit cell block.
  • semantic components such as SU FEBs (semantic unit front end block), SU CELL (semantic unit cell), semantic unit cell block, and SU (semantic unit) it is to be understood that in some cases they may be used interchangeably as architectural elements in diagrams and examples, the reason being that the semantic architecture is hierarchical; further such components and their links may be mapped to semantic network models and may use semantic waves for communicating semantic information.
  • a semantic unit is a higher-level semantic architectural artifact which may or may not comprise any of the other semantic components; further, semantic units may comprise other hardware elements, components and/or blocks (e.g. storage elements, I/O etc.) that may implement semantic and/or other functionalities and/or protocols.
  • semantic artifacts and/or components whether disposed and/or configured in a hierarchical layer architecture and/or semantic flux architecture - 208 - LUCM-1-1055Spec may be used to form semantic memories.
  • Semantic components and/or artifacts may comprise any number of input and output signal interfaces that may be interconnected and used to control voltages, currents, impulses, clocks, discrete or analog inputs and/or outputs, or semantics to other semantic components, computer/semantic units which interpret the data/signals based on semantic analysis as described in this application.
  • the propagation through the semantic architecture is used in semantic inference potentially using hierarchical semantic network models.
  • a single or a plurality of photodetectors may be connected to a semantic unit. Alternatively, or in addition the photodetectors are connected to multiple semantic units.
  • the semantic units may include transducer and/or transducing components. Further elements associated with a semantic unit may perform photoelectrical emission detection.
  • the connection and layout of elements and semantic units may be reconfigurable. As such, the elements and units connections are reconfigured in semantic groups based on grid/mesh control semantic network layout, hierarchical overlay and/or semantic analysis. Multiplexer, demultiplexer, switches (e.g. crosspoint) components and combinations may be used for connection reconfigurability within semantic components and architecture. Such components are depicted in the examples of Fig 21, 22 as MUX.
  • Such components are used to interconnect semantic components in various configurations whether one to one, one to many, many to one or many to many.
  • Such components may be either analog and/or digital and be controlled via semantic means (e.g. semantic, semantic waves etc.).
  • semantic means e.g. semantic, semantic waves etc.
  • photoreceptors, sound and/or pressure receptors may be used in such semantic sensing apparatuses.
  • transducers are utilized in sensor and apparatuses for radio frequency sensing, optical/photon based communication mediums, sound/ultrasound sensing, biosensing and others; application of these apparatuses may vary from communication, quantum computing, localization, proximity sensing, medical imaging, medical applications, DNA sequencing, gene identification/characterization and profiling, networking, cyber security to other applications.
  • a photodetector detects incident light/photons and transduced signals (e.g. current) are transmitted to and/or through the semantic unit.
  • the semantic unit uses its semantic model, semantic engine and/or circuitry to determine if need to route and/or to control adjacent semantic unit and/or photoreceptors.
  • a semantic unit may communicate with other semantic units in order to perform semantic inference and/or excite or inhibit other semantic units and/or semantic memory. The communication may be achieved through semantic gating, flux, routing and/or waves.
  • the photosensor may have a multitude of substrates with each substrate incorporating interconnection links between various elements of the previous substrate. As such the photosensor structure may resemble a hierarchical mesh which may be mapped to a semantic network model. Thus, design and assembly tools and techniques based on semantic inference mesh and/or on semantic network models (e.g. mapped to locations, elements and/or hierarchies) may be used for sensor design and couplings between the sensor elements and layer hierarchies.
  • the semantic fusion uses all the imaging artifacts from all these modalities in order to improve the semantic field object detection and identification tasks through semantic analysis applied to semantic mesh and semantic network model.
  • semantic analysis applied to semantic mesh and semantic network model.
  • semantic routes, views, view frames, factors, leaders and biases are used for conditioning, selection, gating and/or to reject noise.
  • the system may use refocusing/retuning of the sensing elements or entities to increase the signal to noise ratio; in an example, once the system recognized two objects, one of interest (maybe because has a leading semantic attribute within a leading semantic route) and another one not of interest, the semantic engines commands the sensing to focus, increase granularity (e.g. low level mapping and inference), map on the object of interest, while potentially instructing the mesh to reject, factor and/or bias semantics associated with the non-interest objects or scenes.
  • the system uses adaptive localization of artifacts and use the semantic models to track their movement in the scene.
  • a semantic network model can be mapped to data, frames, images and/or data renderings from the sensors based on location; endpoints, links and semantic groups of artifacts are potentially mapped; further, the semantic network model is mapped to the location and/or identification of the sensors in the sensor array or grid.
  • the system may map the endpoints directly to the array and grid of sensors and sensor elements via location and/or - 210 - LUCM-1-1055Spec identification.
  • the system may map the semantic model artifacts based on components and/or group identification and/or semantics. Mappings may be one to one, one to many and many to many; the system may use semantic groups to perform the grouping of sensor elements either as they are represented and mapped as one or more endpoints and/or links.
  • the system may map semantic groups of network elements to semantic groups of sensing elements. [001173]
  • the system maps semantic groups of elements to the semantics and/or hierarchical semantic artifacts based on learning from other modalities (e.g. voice).
  • the photosensors are basically capturing the semantic field and their location, orientation and/or identification are directly correlated with the location of features, objects and/or semantic scenes.
  • the system uses stereoscopic vision, depth calculation and other passive and active technologies and techniques.
  • mapping of semantic network model artifacts e.g. endpoints and/or links
  • the system may map an artifact to a group of elements and as such the semantic inference on the group may be associated with the artifact.
  • the inference on the artifact may be associated and/or translated to a semantic group.
  • the system may use hierarchical transformations on artifacts to represent groups, causality and other relationships.
  • the system may use semantic inference at an artifact and as such a semantic group of elements.
  • the system uses the hierarchical network semantic model to detect/compose the feature at a hierarchy level and associate the feature with other semantics in the network model based on the semantic routes between the endpoints of the semantic group comprising the feature.
  • the system may use semantic orientation for comparing and fusing features, frames or scenes.
  • Semantic artifacts may be associated with endpoints and/or links whether in a hierarchical or non-hierarchical manner.
  • the semantic artifacts e.g. semantics, semantic groups, semantic routes, shapes, views etc.
  • the mapping is determined by the correlation and/or inference between the semantics artifacts in the network model and the semantics artifacts associated with the elements in the grid.
  • Semantic trails, semantic routes and shapes are used to represent/convey pattern matching between semantics, sensing elements, mesh/grid layout and semantic artifacts at any layer of a hierarchical semantic network model or between layers and hierarchies.
  • the system may use semantic shaping and/or hierarchical semantic pattern matching to identify common artifacts, areas, locations and/or semantic groups between frames and/or images; such artifacts may be used as anchors.
  • a semantic network model can be composed from a plurality of sub- models; the sub-models may be ingested from various sources (e.g. a semantic flux), may comprise semantic rules with different biases and orientations, may represent various themes, may be associated with particular artifacts and so forth. They may be distributed and/or fused at any level of the semantic network model hierarchy.
  • the system recognizes the semantic of an image or semantic scene in a hierarchical fashion.
  • the system detects various high-level semantics that are used to route the semantic inference at lower levels in the hierarchical semantic model. Further, the semantic inference may be routed between layers in the hierarchy based on the semantic field and scene developments analysis.
  • the system may control the sensing elements based on the semantic analysis.
  • high-level semantics may be determined from a coarse or fast assessment of the semantics at lower levels.
  • the system may perform inference in any direction and/or patterns in order to improve semantic accuracy and granularity.
  • the patterns may be associated with semantic routes, shapes and further the system performs semantic orientation and pattern recognition based on leadership status.
  • the patterns may be related with absolute or relative directions and orientations in a composite fashion within the hierarchical semantic network model.
  • the system may use indexing of semantic network artifacts, to determine and preserve the scene development.
  • the system uses intermediary and/or indexed mapping of model artifacts to determine that a car has the color brown by evaluating the car chassis visual model from left to middle of the car and further middle to right.
  • the system splits and maps the original shape/area/data/text to model sub-artifacts and perform inference on sub-artifacts.
  • the semantic relationship between the original artifact and/or sub-artifacts may be represented as semantic groups and/or sub-models.
  • the inference - 212 - LUCM-1-1055Spec on sub-artifacts may be composed potentially in a hierarchical manner and assigned to the original artifact.
  • the sub-artifacts may be mapped based on semantic indexing of the original artifact.
  • Model artifacts comprise endpoints, links, sub-models and other semantic artifacts. [001184] In the previous example the system may have detected that endpoint A and B are shades of brown and that a link between them is CONTINUOUS FADING COLOR so that by using semantic analysis the system may have further inferred brown shapes.
  • the system is able to identify artifacts at any semantic level (e.g. CAR, DELOREAN, JOHN’S DELOREAN) based on semantic analysis.
  • the system is able to use semantic identities in a routing, gating, orientational and/or hierarchical manner in order to guide the semantic inference of semantic identities.
  • the system is allowed to pursue semantic identification based on gating and/or access control (e.g.
  • the system uses location, semantics associated with locations and time management rules to infer semantics associated with (semantic) identities.
  • the system observes a location which is associated with a social event based on a time management rule and the system further has a goal of OBSERVE DE LOREAN CAR based on a LIKE DE LOREAN factor and a semantic route of LIKE DE LOREAN, OBSERVE DE LOREAN and further (JOHN) DE LOREAN SHOULD ATTEND EVENT.
  • the system further infers that DE LOREAN NOT PRESENT until infers that DE LOREAN PRESENT based on the identification of the DeLorean car through sensing means or based on semantic flux and/or inference on other data; it is to be understood that DELOREAN IS PRESENT holds a confidence level based on various factors such as risk in (poor) identification on (JOHN) DELOREAN (owner), semantic flux risk and/or further inference factors.
  • the system learns that the semantic identifications of DELOREAN is linked through PRESENT (or ATTENDANCE, or other semantic group member) in relation with the semantic identifications of the event.
  • the system may use leadership semantics (e.g.
  • JOHN, DELOREAN; JOHN’S DELOREAN semantic identities and/or groups comprising the leadership semantics
  • JOHN, DELOREAN; JOHN’S DELOREAN semantic identities and/or groups comprising the leadership semantics
  • semantic identities, semantic groups and/or members thereof e.g. in the previous example the system may have known that JOHN DELOREAN need to attend a vintage car and/or DELOREAN car event and as such it may have adjusted the factors - 213 - LUCM-1-1055Spec associated with the presence, identification and/or location of the (JOHN’S) DELOREAN car and/or JOHN DELOREAN).
  • the system may decay the factors associated with the learned semantic artifacts and thus giving them less priority in inferences; alternatively, or in addition the system moves and/or copies the learned semantic artifacts to other areas of the semantic memory. It is to be understood that the decaying of factors is based for example on the factors associated with LIKE DELOREAN sentiment (e.g. decays less if the factors associated with the sentiment are high and decay more is the factors are low).
  • Noise or unwanted signals, waves, envelopes, graphics may be detected and isolated via semantic analysis and may be filtered via signal and/or semantic processing techniques. Signal, wave, envelope, graphic, filters, conditioning and identification may be achieved via semantic conditioners. The sematic conditioning can be done by specialized hardware and software components (e.g.
  • semantic units can be achieved through more general-purpose computing modules including field programmable gate arrays, GPUs, CPUs and others.
  • the conditioning is based on semantic orientation, shaping and semantic drift analysis on signals, waveforms, information, graphs, routes, shapes, semantic views, view frames, models etc. Further, the system may compose the conditioning and/or noise, potentially with other inferred semantics, and further condition them.
  • the process of semantic conditioning, composition, analysis and orientation can be done any number of times at any level or between levels of a hierarchy. The system may determine complex behaviors, patterns and orientations based on such techniques.
  • the semantic fusion can be done by using unconditioned imaging and signals, conditioned imaging and signals, can use various features, object and groups identification techniques.
  • the system may use semantic analysis and conditioning with or on noise signal.
  • Image analysis can use various sampling techniques including oversampling and under-sampling with the semantic conditioning and fusion techniques.
  • the conditioning can use analog and digital techniques coupled with semantic analysis in order to perform inference. Analog to digital conversion and digital to analog conversion may be coupled to semantic analysis and/or semantic conditioning.
  • Various feature detection techniques can employ single, combination and/or multiple stage algorithms and techniques; some may be based on gradients, divergence, nearest neighbor, histograms, clustering, support vector machines, Bayesian networks, entropy - 214 - LUCM-1-1055Spec (e.g.
  • the system performs real time statistical analysis on real time semantic routes wherein the system performs statistics based on semantic analysis on the route. In some examples, the system determines statistical health factors given particular routes and/or habits.
  • deep learning network systems are only relatively efficient for feature extraction and recognition since they don’t consider semantic analysis and thus, they require fairly high computing power and in the case of supervised learning require large amounts of training data; even so, the processing is not always achieved in real time.
  • a semantic engine may couple any of former techniques with semantic intelligence and analysis.
  • Semantic model artifacts may be associated with gradients.
  • color or grayscale gradients of an image and/or frames are associated to artifacts (e.g. oriented links) in a semantic network graph.
  • the system performs drive semantic or orientation inference based on semantic groups which correspond to features, colors and/or gradients semantic patterns.
  • at least a layer in the semantic model may be mapped and/or associated with a vector field. Further, the divergence of the vector field is used to determine semantic factors associated with the inferred semantics in the semantic model.
  • the vector field may be associated with the entropy of semantic group of cars travelling in a formation and/or mapped to an endpoint and/or area.
  • the entropy of the semantic group may be related to a variety of conditions and artifacts including trajectory entropy, volume and/or area entropy, topological entropy, semantic drift entropy, encoding, behavior entropy, intention entropy and/or signature entropy etc.
  • the entropy may be further related and inferred based on endpoint and/or area semantics (e.g. based on sensing, weather conditions etc.), semantic drifts and so forth.
  • the system may perform semantic analysis including inferring new endpoints and/or links in the semantic model. Further, the system may infer optimal safe trajectories and so forth; it is understood that the system may use optimize trajectories on multiple goals, factors and indicators such as car capabilities, safety, comfort, entropy, energy consumption and so forth. [001199] In further examples, the vector field and/or semantic network model hierarchies may be used to infer, associate and/or apply torque (vectoring) to the drive wheels - 215 - LUCM-1-1055Spec of semantic post/s or other vehicle/s.
  • the semantic torque vectoring may provide superior body roll control by including an exhaustive set of conditions and circumstances from a variety of sensors, fluxes components and/or layers in the semantic model.
  • the torque vectoring is inferred based on current and/or projected conditions and circumstances (e.g. at the locations as mapped on the driving surface area and further based on parameters of embedded sensors in the tires/wheels–- used to infer semantic attributes about tires, road surface etc.–- and/or about tires, system cyber condition, driver condition etc.).
  • a semantic group may be conditioned, gated, composed, reconstructed from and/or deconstructed in multiple semantic groups based on semantic analysis of the group’s semantics and semantic inferences.
  • a user may pose a challenge to the system and the system performs inference based on the challenge.
  • the challenge may be for example in a semantic structured form and/or natural language.
  • the user may specify goal leader artifacts and factors. While semantics may explicitly comprise those artifacts, in other embodiments the semantic system also infers them based on further semantic analysis initiated internally.
  • the system infers goals and routes for a response. For example, a user or a collaborative system may ask a question “is this sweet?” and the system is able to perform goal identification-based inference for “sweet” drive semantic and the previous inference on the context.
  • the system is able to couple this with a previous inference of an object that formed semantic trails, routes and composite semantics such as “APPLE RED GRAY LINES AT THE BOTTOM” “SWEET 50” “VERY SWEET” “FAVORITE 100” “BEST APPLE” etc.
  • group composition may be implemented based on the synonymy of semantics that define and/or are associated with at least two semantic groups.
  • the system may form a composite semantic group comprising only the semantics that are synonyms at the group definition semantic level and/or group membership semantic level.
  • the synonymy may be determined based on inferred semantic factors, indicators, routes and/or semantic view information.
  • compositions supporting particular goals, factors, orientations, shapings (e.g. shape-based inference) and/or indicators are based on such techniques. In other examples, they may be based on antonymy; further, any other semantic linguistic relation may be used to emulate composition between semantic groups. Further, the system may infer and assign factors, indicators and/or semantics to newly formed groups based on semantic analysis. Such techniques may be used to perform semantic analysis - 216 - LUCM-1-1055Spec for drive semantics, semantic routes, views and other semantic artifacts based on semantic chain development. [001203] Semantic groups composition may be used for semantic orientation, shaping and/or drift. For example, the system may calculate orientation and/or drift between two semantic routes.
  • the semantic groups formations are also location, time and/or semantic artifact based. In an example, they may be modeled/represented as semantic network graphs where any causal relationship is modeled/represented as an oriented link between two semantic artifacts that share the causal relationship.
  • a causal relationship may be “A THREAT TO B” or “A INFECTED B” and as such the system represents the causality as an oriented link from A to B.
  • the oriented link may be assigned a semantic of THREAT or INFECTED or alternatively, or in addition, an upper hierarchy artifact may determine/specify the causality relation via its associated semantics.
  • a semantic system uses semantic clustering of data in a memory (e.g.
  • semantic memory for efficient access, inference and rendering of mapped images and frames.
  • the semantic clustering is based on semantic analysis, semantic model and semantic groups. Additionally, the system uses location clustering and time clustering analysis based on semantic analysis, semantic network models and any of the techniques explained throughout this application. [001206] Sometimes the locations are associated with artifacts at that location and as such the system performs groupings of the artifacts based on the semantics associated with the locations and/or the links between endpoints associated with those locations. [001207] In an example, the system ingests image, video frames, tactile, pointing and/or other inputs (e.g. from a user).
  • the system maps the network semantic model to the renderings/frames/data and performs semantic analysis to determine semantics and or semantic groups. Further, the oriented links between endpoints associated with semantic groups in the semantic network model may be adjusted based on semantic analysis. Alternatively, or in addition, inputs from a user or other sources may be used to setup, determine or adjust the semantics associated with the semantic network model. Further, users may pose challenges and the system performs inference based on the challenge. The challenge may comprise a specified and/or inferred goal–- e.g.
  • the system may not poses the - 217 - LUCM-1-1055Spec idea of the risk of not attaining the goal until analyzing the end of the construct and/or during the semantic goal development) and thus the system infers risk factors and indicators based on semantic budgets (e.g. time, cost etc.).
  • the system may generate a semantic route and/or semantic rules based on the inferred semantics and semantic time.
  • the composite transaction semantic may be associated with semantic time intervals comprising the inference of “breakfast” and “tomorrow”. Further, it may be associated with semantic intervals comprising a semantic flux monitoring, ticket provider sales and the target price. It is to be observed that the price and/or time target is a semantic goal related to semantic budgets of the composite or route main goal. Based on further semantic analysis (e.g. based on the challenger’s funds/budgets, availability/supply, track event attributes/purpose/goal/identity, track semantic time constraints etc.) the system may prioritize one budget (time, cost) over the other and/or factorize one in rapport with another (e.g.
  • the semantic analysis may comprise past, current, speculative and/or projected semantic artifacts.
  • the system may have a variety of registered track providers (e.g. via fluxes) and the system may select one (e.g. challenge/issue/command an offer, purchase order and/or purchase semantic for a particular semantic identity using a payment processor and/or secured budgets) based on the goals and/or other ratings coupled with semantic analysis.
  • the system may use indicator biases (e.g. risk bias; desirability bias comprising desire, worthiness etc.) to control the behavior (e.g.
  • the system may determine carriers, providers, posts, vehicles, routes and/or groups thereof for movement, shipping, receiving, logistics etc. [001208]
  • the system may implement semantic contracts wherein contracts are ingested as a sensed free form, text file, specialized form document, XML file and/or other fields and formats. The system infers clauses (e.g. goals, indicators etc.) based on semantic analysis on the contract.
  • the system updates the status (e.g. factors, indicators) related with the goals (e.g. SHOE DISTRIBUTOR A RISK 90% OF LOW SHOE SUPPLY).
  • the system may have a rule that specifies that the previous status composite semantic may be coupled with an automatic order to DISTRIBUTOR B while withholding payments to DISTRIBUTOR A based on factor plans related to payments; the withholding of payments may comprise paying only partial sums based on payment plans; such payment plans may be associated with time management, budgeting, - 218 - LUCM-1-1055Spec factoring, indexing and any other semantic rules
  • the system may be connected to at least one payment processor potentially via a semantic flux.
  • the semantic system is connected and/or comprising live feeds and/or semantic fluxes associated with financial markets, trading, stock indices and/or other financial instruments.
  • the system may issue trading and/or stock orders based on investment goals, associated fees, target asset allocation and diversification.
  • investment goals may comprise reward to risk factors, budgeting and/or further factorization.
  • associated fees may be used as budgets associated to semantic indicators of particular trades, stocks, indices, trades/stocks/indices type and/or status, and/or semantic groups thereof.
  • the diversification may be based on entanglement entropy of particular trades in respect to factors and/or parameters such as domain, valuation, rating, leadership, seasonal (e.g.
  • a semantic system uses an adaptive semantic model and continuous inference of semantics in order to interpret the semantic field.
  • the semantic field may be bound to sensorial inputs and/or any other source. Semantics may be associated with general vocabularies; sometimes more specific vocabularies incorporating domain and formal knowledge may be used.
  • a feature may be represented as a semantic or semantic group.
  • a partially realized feature may be one that doesn’t include all the associated expected (goal) semantics and/or the factors associated do not meet a baseline interval threshold or requirement. In some examples the intervals are based on semantic intervals. [001212]
  • a semantic system doesn’t require extensive training sets and in general is more optimized for real time utilization due its capability of filtering unwanted noise and features based on the semantic model.
  • semantic model may use semantic groups of features for single object or multiple objects detection.
  • An object in an image can be recognized via the semantic attributes associated to its components or features.
  • the semantic model comprising semantic rules, semantic routes, semantic groups and others may evolve through learning.
  • the localizations within various semantic fields may be based on semantic determinations wherein features, objects, signatures, groups of features and groups of objects are determined and correlated in various images, semantic scenes, semantic fields using timings (e.g. semantic timings) associated with the semantics and the semantic model.
  • Temporary or permanent semantics, semantic identification and/or ids may be assigned to objects and groups.
  • Temporary identification may be used for preserving privacy; the system may invalidate and/or discard temporary identification after an interval of time; the interval of time might be based on semantic time intervals and the system uses semantic analysis for invalidation and/or discard.
  • the semantics associated with temporary identification may be processed and/or transferred to the permanent identification.
  • the information transferred may be filtered based on semantic gate and/or access control rules for privacy preservation; in an example, only a subset of the semantics inferred for the temporary identification are transferred to a permanent identification.
  • the system may also ensure data governance and access control to data. As such, data is stored in semantic memories and managed (e.g. invalidated, deleted) and/or accessed based on semantic access control.
  • a semantic wallet comprising identification, authentication and encryption keys may be used to gain (which may in addition be viewed as a semantic gain and/or drift) access to data by allowing access at various levels in a semantic model hierarchy. It is to be understood that the semantic wallet may be also stored as a hierarchical semantic model and be encrypted based on biometrics, password, multiple factor authentication, temporary tokens and other technologies. [001217] In some examples, the wallet is comprised and/or stored in a semantic memory, optical, radio frequency and/or other electromagnetic device. Further, wallet - 220 - LUCM-1-1055Spec information, identities and authentication may be communicated via various protocols and/or further techniques some of which are explained in this application.
  • the transfer of data between various semantic groups, endpoints, areas, regions, volumes, renderings, systems, devices, files, databases, fields and/or controls may be semantic gated and/or conditioned.
  • the system uses semantic routing and semantic analysis to distribute documents to fluxes via semantic gating and semantic profiles.
  • documents, multimedia, files, texts, paragraphs and other ingested or processed data is associated with semantic artifacts based on semantic inference on content and/or semantic identification.
  • the system may perform inference, reconstruction, routing and gating based on such artifacts. Further, the system may perform access control on such (ingested) artifacts and/or data by deleting (e.g.
  • the (ingested) artifacts may be routed and/or gated within the semantic network.
  • the system may perform composition, overlaying, rendering, conditioning and/or further semantic analysis of the received information in rapport with artifacts having a semantic identity (e.g. associated with a disseminated artifact, distributed artifacts, document, paragraph, object, user and/or person etc.).
  • Semantic rules comprise semantic composition, access control, time management, ratings and factors.
  • Localization and distance to objects in some vision systems is achieved through diversity sensing using multiple vision sensors.
  • Vision sensors may use photodetectors arrays.
  • the objects, signatures, groups are correlated in various images and scenes. In an example, semantic orientation and semantic drift thresholding is used for correlation.
  • semantic orientation and semantic drift thresholding is used for correlation.
  • the system may adjust based on the environment and timing the factors of a particular sets of semantic attributes that identify a feature and/or object. - 221 - LUCM-1-1055Spec If through previous semantics the system identified in the stream of images a car and identified the color red for the car and the system determines that there is no likelihood that another artifact of color red may appear or be visible in the direct semantic field then the system may just detect the location of the car by simply comparing, identifying, localizing and tracking the color red in the image or video stream (contextual leader feature).
  • the system may increase the factor/weight of the red color semantic attribute in regard to identification of the vehicle while for example it may gate other locations of red appearances in relation with the car just because those locations are not feasible or unlikely to be reached by the car.
  • the system may group such features and track the group of features and use any semantic grouping techniques and operations; additionally, besides the relative position, the relative dimension of the feature is also considered.
  • the relative positioning and relative dimension may be related to semantic artifacts, endpoints, links, semantic indexing and factoring and/or elements (e.g. sensing elements) in the network semantic model.
  • the system monitors locations and identify objects passing through the locations and semantic model; while identifying an object and/or type at a location the system may determine various other semantics (e.g. particularities) associated with the identification, object and/or type (potentially via semantic groups). Further, the system is able then to better monitor and identify objects at or within locations based on the knowledge of monitoring the movement in and out from a location or in general based on detections at endpoints and/or network model.
  • semantics e.g. particularities
  • the system may associate and/or identify objects, features and/or semantic attributes with semantic groups (e.g. groups of composite objects, features etc.) based on semantic analysis (e.g. groupings at locations/areas, network semantic model inference etc.); thus, the system is able to further track particular objects in the semantic system based on such semantic groups.
  • semantic groups may be updated at any time based on further semantic inference. In an example, if the system detects a forklift with a color orange and orange tires it assigns such semantic attributes to the particular forklift object that may be tracked in the field.
  • the system updates the semantic group (e.g. add leadership and time management to the added color of orange, while decaying and add time management rule to the change of previous color of orange to black) associated with the particular forklift to reflect the change in color of tires.
  • the system is able to keep the identification on the particular forklift object within the - 222 - LUCM-1-1055Spec semantic field based on semantic analysis even when some features or leader features change.
  • the system may use other observations, external semantics and/or semantic fluxes to update the semantic group in the previous example (e.g.
  • the semantic network model is mapped absolute or relative to a car’s position and/or car’s coordinate system.
  • the system may have a reference group within the semantic model and the system performs relative and/or absolute comparison of the mapped semantic field to that semantic group.
  • the semantic group may be static related to the observers (e.g. sensing, semantic unit, semantic engine view) reference coordinates.
  • the system may perform relative inferences to the other artifacts in the field and potentially infer factors and indexing.
  • the system has a semantic group representing the flat bed of the composite post carrier, flat bed or hood of a car and as such the semantic inference will look to adjust the semantic model/views comprising this semantic artifact in relationship with the semantic scene/field development and/or mapping of scene/field.
  • the system will look to find semantic path groups in the model that may allow the passing of the hood artifact.
  • the system may infer that a path and/or endpoint group comprises an artifact which results in deeming the path and/or endpoint group as non-feasible because a denied semantic has been inferred for the artifact. It is understood that the paths and/or endpoint groups may be linked to hierarchies in the model.
  • fitting and shaping may be used to keep a post and/or vehicle in a virtual and/or physical lane.
  • the system may use dissatisfaction, concern and/or stress factors in association with fitting and/or shaping.
  • the system fits and/or loads a post carrier (to storage/parking) based on semantic zoning and low concerns to fit into space. Based on further inference on the goal achievement and/or further evidence it may adjust the concerns factors.
  • Techniques such as fitting and shaping may be used to infer and optimize artifacts (and semantic groups thereof) storage, positioning, design and travel in particular areas and/or volumes (e.g.
  • the system may project goals and/or semantic budgets of fitting and/or collapsing an artifact (e.g. endpoint, route etc.) and/or groups of artifacts in another artifact or group of artifacts.
  • an artifact e.g. endpoint, route etc.
  • Semantic factors and/or budgets may be projected and/or collapsed based on the inference in a semantic group and/or collapse of a semantic group.
  • the system uses overlay semantic artifacts, associated factors and/or budgets on a semantic model and/or hierarchy to infer projected views, semantic orientations, semantic groups, routes, budgets, factors and further semantic artifacts.
  • Fitting and shaping may be combined with semantic analysis on habits, purpose, uses and customs.
  • the system uses such techniques to optimize furniture arrangement in a room.
  • the system uses such techniques to optimize storage of posts and/or containers in a garage, transportation or logistic cargo.
  • Semantic groups whether or not partially realized are identified and tracked by a set of factored semantic attributes.
  • the identification of the locations of interest in the image, represented by the objects or the semantic groups of interest are based on semantic attributes, semantic shapes and other semantic artifacts; examples may include color, shape etc.
  • the system infers indexing comprising rate change factors and/or indicators of location, dimensionality, size, attributes, semantic routes and/or further semantic artifacts.
  • the system infers factors and/or indicators associated with changes of location, dimensionality, size, attributes, semantic routes and/or further semantic artifacts.
  • the locations may be based on depth information if the image capture comprises such information (e.g. based on TOF, stereoscopic vision indexing etc.).
  • the systems presented before are used in radar type applications. The system uses the reflections and backscattering of the transmitted waves from the illuminated objects to identify entities and infer semantic attributes related to those entities. As such, the semantic system is able to infer any type of semantics as explained above based on the localization and probing of entities.
  • the entities may be detected based on radio frequency sensor diversity, measurements, semantic analysis and adjustment.
  • the semantic system may use hierarchical threshold calculations and semantic analysis on the received measurements, waveforms or signals to determine the location and/or semantic attributes for the detected objects.
  • the semantic system may store semantic inference rules, semantic templates, patterns, signatures related with measurements, waveforms, signals.
  • the system receives and processes sensing data via analog and digital components and blocks (e.g. RF front ends).
  • the front end may embed a semantic unit.
  • the analog to digital conversion is usually a bottleneck in high resolution sensing systems and thus having a semantic engine coupled in analog and/or discrete domain may provide more efficient sensing, closer to the sensing elements (e.g. antennas) while increasing dynamic range.
  • the semantic engine controls electrical and optical blocks and parameters for improved efficiency (e.g. voltage and/or currents, element charge).
  • the system may organize groups of measurements, signals and/or waveforms in semantic groups and use semantic analysis and semantic group conditioning for semantic inference.
  • the semantic model may comprise patterns based on semantic groups whether group dependent or group independent.
  • the semantic engine is capable of advanced semantic inference including object identification, localization and behavioral analysis.
  • Such front ends and components may comprise antennas, lenses, photo elements, lasers, radiative elements, radiative meshes, beam steering meshes etc.
  • the return signals may be correlated for obtaining spectral images containing the spectral renderings of the objects in the field of view.
  • the intensity of pixels for scanned field varies based on the reflection (e.g. backscattered waves) waveforms obtained from the illuminated artifacts and depends on the dielectric constant.
  • the dielectric constant in materials and other natural or artificial artifacts increases in the presence of moisture and as such the signal to noise ratio increases.
  • the signal to noise may decrease based on semantic field objects’ arrangements (e.g. as detected by optics/camera and/or rf sensing). As such, being able to interconnect various inferences (e.g.
  • various signals, streams, frames, images and renderings may be captured based on various polarizations and be analyzed and fused to more confidently detect artifacts and their characteristics based on their signatures in various polarizations.
  • the scattering signature is changed and as in a similar way as the previous example of various polarization settings the received data may be fused to detect the artifacts in the field.
  • Multiband multi-polarization radar and optical systems acquire images at several wavelengths, polarizations using diversity techniques.
  • RGB red green blue
  • HSV high green blue
  • HSI high- saturation-intensity
  • HSL high-saturation-lightness
  • semantic augmentation and/or rendering e.g. associate semantics with commands, voltages, currents and other control mechanisms in order to control display elements, augmentation elements etc.
  • the display and augmentation elements may comprise any hardware and bioengineered components and blocks enumerated in sections of this disclosure.
  • the semantic engine may use goal-based inference for determining the best semantic routes to follow.
  • the goal may be based on achieving semantics, particular semantic factors (e.g. rating) and any combination of those; alternatively, or in addition, a goal may be based on achieving association/de-association of particular semantic artifacts and tracked artifacts and potential factors based on association.
  • a goal may be hierarchical and/or comprise semantic grouping and/or clustering (e.g. group dependent or group independent semantic memory clustering and/or activation/deactivation).
  • the goal may be associated with semantic budgets.
  • the goal may be used to determine projected semantic views and view frames. Further, the system may use semantic orientation to orient semantic inference toward the goal and projected semantic views and view frames.
  • the system may establish a goal based on drive semantics, speculative and/or projected inference.
  • goals Once goals are established the system performs semantic inference based on goals and sub-goals. Sometimes the system uses different semantic view frames for performing the goal-based inference.
  • the system performs inference that builds semantic - 226 - LUCM-1-1055Spec routes and assesses the factors of semantics in rapport with the goal’s factors and semantic budgets.
  • the system sets up a goal to gain knowledge or learn car repair.
  • the system evaluates based on semantic analysis that learning about a car’s engine provide the most rewarding goal outcome (e.g.
  • the system may learn first about the engine sensor suite and further determines that a sub-goal for learning about engine’s injection or other components (e.g. transmission) may be more rewarding based on the semantic view of operation.
  • the system may use semantic orientation to determine the semantic drift between the pursued semantic routes and the updated semantic routes of the views, view frames, model etc.
  • the system may assess whether the pursued routes need to be updated and adapted based on the updated goals, sub-goals and projected semantic views and view frames. [001253] If the pursuing and/or projection of (strategic) goals results in decayed budgets, factors and/or further blocked inferences the system may decay and/or stop altogether (the pursuance of) the goals.
  • Sub-goals may be inferred and/or related with increasing/decreasing factorizations and/or budgets.
  • the system infers based on projected analysis that the budgets are too decayed (e.g. and further infer lacks of resources and/or needs–- “need higher budget”, “need to gain 100”) and not allowing to achieve the strategic goal of “learn car repair” and/or further sub-goals of “learn about sensor suite”; thus, the system may perform inference and augmentation towards sub-goals such increasing budgets and/or satisfying short term needs which may further route the inference to attaining semantics, collaborators, fluxes and/or groups which allows higher factorization of budgets.
  • the semantic drift between the short term goals and long term (e.g. strategic) goals may increase (e.g. by factorization, indexing etc.) and the semantic drift between the means of achieving the longer term goals and the short term goals may change as well.
  • the goals/sub-goals, semantic hierarchy, orientation and/or routing comprise variable (allowable) drifts and confusion.
  • the system may re-allocate more resources (e.g. budgets, semantic units) to semantic views associated with (projected) high consequences (e.g.
  • the system may allocate more resources to (projected) inferences which may not meet factors, budgets (e.g. (semantic) time (quanta) budgets), coherence, confusion and/or drifts; further, the system may use alternate and/or hierarchical routing and/or gating.
  • budgets e.g. (semantic) time (quanta) budgets
  • coherence e.g. (semantic) time (quanta) budgets
  • the system may use alternate and/or hierarchical routing and/or gating.
  • of an activity of SURGERY and further ACTUATING SCISSORS may require a lower semantic drift based on the risk factorization of projections and/or consequences.
  • the consequences may be related with leadership projections, risks, diffusion and/or tunneling through semantic gating and/or access control.
  • the system may re-allocate resources to such critical operations however, if the inference and/or actuation has drifted and/or is incoherent (e.g. due to decayed budgets, high drift, confusion etc.) the system may re-allocate resources (potentially to a different level of hierarchy) for finding alternate ways and/or zones to employ cutting and/or scissors capability (e.g. CUTTING EB SHAPE-2, CUTTING EC SHAPE-3 (instead) of CUTTING ENDPOINT ZONE A SHAPE-1 ).
  • the system may reallocate resources based on semantic factors, budgets, time management, drifts, coherence, confusion, rules and/or further semantic artifacts.
  • the resource allocation/reallocation may be based on short term goals and/or long/longer term goals. Further, the reallocation may be hierarchical with the short-term goals being allocated/reallocated at a lower level and/or shorter-term memory while the long- term goals may be allocated/reallocated at a higher level and/or longer term memory. [001257] The resource allocation/reallocation may be based on DNA replication and/or remapping. [001258] The semantic orientation provides sentiment analysis based on semantic drifts, decaying and further factor inference. The system further uses semantic orientation and drifts to adjust projected views, view frames and further to guide the semantic inference. In some examples the system uses the drifts (e.g.
  • semantic drift trajectory based on pattern overlay and/or indexing
  • Smoothing of routes and trajectories may be used for optimized command and control, prediction, correlation, covariance, conditioning and so forth.
  • the smoothing may be associated and/or be used to model/implement hysteresis in some examples. - 228 - LUCM-1-1055Spec [001260]
  • the hysteresis is modeled and/or implemented based on semantic profiles, semantic rules, decaying, drift, factors, goals, projections, intentions, desires and/or further semantic analysis.
  • the output of the battery unit and/or control voltages/currents/electromagnetic effects in the semantic post is increased and/or decreased based on an inferred intention and/or desire (e.g. of a control unit and/or user) and further time management rules.
  • the electrical control values of HVAC units may be controlled in similar ways.
  • vehicle acceleration is controlled by varying electrical voltages, currents and/or magnetic properties/fluxes based on semantic hysteresis.
  • the system may learn semantic indexing and/or hysteresis associated with semantic identities and store it in semantic profiles.
  • the system associates inferred artifacts in semantic views with driver commands (e.g.
  • the system may know through semantic inference (e.g. semantic group, time management etc.) that the user is associated and/or actuates the acceleration and/or steering; as such, the system groups the semantic artifacts (e.g. semantic routes/trails etc.) inferred from such actuation related sensors with the semantic artifacts inferred from further semantic field (e.g. environment); in circumstances where the system infers less used and/or weighted routes, high factors (e.g.
  • the system may learn rules associated with indexing and/or hysteresis inferred based on users actuation commands and/or further consequences as further inferred on the semantic field.
  • factors e.g. rating, weighting etc.
  • the factors may be used to determine commands to the controlled entities/components/blocks/devices including actuators, sensors, I/O and/or transducers.
  • the commands may be linked and/or specified with semantics; alternatively, or in addition the commands can be specified and/or linked with a parameter and/or value to be applied to the controlled artifact; in an example, a voltage or current interval may be specified for a specific command linked to a parameter.
  • the system comprises/infers and/or receives (e.g. from user, semantic flux etc.) a reference voltage, value/s, interval/s and/or signals which is/are pondered/correlated/convoluted with a corresponding factor (e.g. weight) from a semantic.
  • the voltage and current are indexed in time based on semantics and factors (e.g. indexing factor).
  • the system comprises an indexing factor that occurs with each semantic and is applied to the current value.
  • the indexing factor may be positive or negative.
  • - 229 - LUCM-1-1055Spec [001264]
  • the command may be a function of the factor of the semantic associated with the command.
  • the value of a parameter or voltage may be a function of a weight.
  • the system uses semantic routes to implement commands.
  • a command may be represented as a semantic.
  • the semantic may be a composition linked to a semantic route and/or group of other semantics which may be associated with commands; as such the semantic command chain is executing based on associated compositional semantics and/or goals possibly based on timing, factors, orientation drifts and/or budgets.
  • the factors of the composite control semantic and its components are calculated based on inference that may include the factors of the entire compositional chain of the command execution.
  • the factors of a composite semantic may be a function of the factors through the compositional chain, groups and/or routing; as such, all the semantics of the compositional chain are contributing to the command through the factors associated with them.
  • the factors may be used to issue commands (e.g. voltage, current, signal, digital commands etc.).
  • the semantic engine infers a semantic with a specific weight and based on the semantic model which may include a compositional template (e.g. comprising semantic groups and/or route wherein the semantic defines, belongs or drives semantic coupled terms e.g.
  • the system infers factors and budgets for the compositional template semantics (e.g. semantic group / semantics); the compositional semantics may be associated with actions and commands and as such the actions and commands are pondered with the inferred factors for the compositional semantics (e.g. for a command control an associated voltage is adjusted based on the factor inferred for the compositional semantic associated with the command control). Further, the compositional semantic weights/factors (e.g. semantic route semantic weights/factors) may be adjusted based on the composite semantic weights/factors. If the entity (e.g.
  • the system may not issue the control command to the particular entity; it may infer other semantics, or possible expand or adjust the initial semantic (e.g. through semantic route expansion, semantic orientation, drift etc.) to compositional semantics, infer/determine new semantic routes further until the system infer/determines that the overall sematic objective or projection is achievable as per goal (e.g. budget).
  • the system may receive feedback from the command control and adjusts the semantic model including the weights/factors, rules, templates and other artifacts based on signal feedback (e.g. from sensors that perceive the effects of the commands).
  • the system performs inference on a composite semantic until achieves a particular factor/weight, potentially within a budget; subsequently of achieving the goal it may expand the semantic using other semantic routes and inference paths; alternatively, once the goal is achieved the system doesn’t use that semantic for further inference if the semantic is decayed in the semantic view frame.
  • the system speculates at least one semantic artifact and compose it in at least one view and/or hierarchical level and further asses the coherency of narratives. It is to be understood that the speculative artifact may be based on situational and contextual understanding based on semantic artifacts at a higher abstraction and/or hierarchical layer/level.
  • a semantic system may establish semantic routes through goal-based inferences.
  • the goals may be associated with semantics and used to infer or determine a set of semantic routes and semantic budgets which then may be pursued in order to achieve the goal; this may include executing commands and continuously updating the model based on sensing and feedback.
  • the system achieves the goal (e.g. infers or reinforces a semantic and/or achieves a factor value/interval for it) it rates the experience and the system adjusts indicators and semantic factors (e.g. costs and/or risks).
  • the semantic engine couple’s information from a variety of sources.
  • the system may use and infer semantics from databases, text, web pages, files, spreadsheets, visual and non-visual environment and so on.
  • semantic agents or units are actively monitoring such data sources and connect through the semantic infrastructure in a distributed manner.
  • the systems maintain semantic artifacts associated with entities wherein the semantics are representative of the capabilities or functionality of the entities and are potentially acquired when the entities register or are detected by the system.
  • an automobile ECU determines a set of particular semantics related to a semantic route and sends the semantics to sensors and actuators sensors by matching the particular semantics with the associated capabilities or functionality of the sensors.
  • the receiving entities may receive the semantics via semantic fluxes, potentially with associated weights/factors and perform semantic analysis including composition, routing, - 231 - LUCM-1-1055Spec and/or orientation and make their own decisions whether to execute actions or not.
  • the semantics may be broadcasted, and the sensors may listen to all or particular semantics based on semantic view, semantic view frame and/or semantic route.
  • the semantic view and semantic view frames may be particularized for each entity as explained in this application. Further the sensors may be mapped to a semantic network model.
  • the ECU may send semantic routes and semantic budgets to sensors.
  • the sensors may use the route selectively wherein the sensor determines commands associated with semantics of its own capabilities (e.g.
  • the semantic sensor may consider multiple routes at multiple levels based on execution, sensed context and/or orientation. As mentioned, the semantic sensor performs semantic analysis on its own. [001277] The system may detect eavesdropping and malicious information injection attempts wherein the system infers high incoherency, confusion, drift and entropy (of) factors. [001278] In an example the semantic orientation and semantic drifts are determined and associated based on analysis involving synonymy and/or antonymy.
  • the system calculates the shift/drift from goals and projections based on composition and factorization of semantics in routes, view frames and views in rapport with a goal.
  • the system may highly semantic factorize synonyms and/or antonyms of the goal semantic when performing semantic analysis.
  • the system is able to correlate the information from a multi domain, multi-source and heterogenous environments, perform sentiment analysis and learn.
  • the system may determine a factor/weight for a semantic in a particular context (e.g. semantic view frame).
  • the factor/weight may be associated with a sentiment of suitability of the semantic in the particular semantic view.
  • the system executes an action (e.g.
  • the system may have coupled the action in the semantic with and at least one expected semantic in a semantic route to occur (potentially within a semantic budget) while or after the action semantic is executed; thus, while executing the action or shortly thereafter the system correlates any inferred semantics with the expected or projected semantics artifacts; as such, if the system doesn’t infer the expected semantic and/or factor, the system may further adjust the semantic route, model - 232 - LUCM-1-1055Spec and potentially the weights/factors of the semantic route, rule or link related to the action semantic and the projected semantic; in such an example, the system may infer a semantic and/or factor that reflect a positive or negative sentiment and is used for characterizing the bond between the first (e.g.
  • a negative factor may represent a negative sentiment in rapport with a semantic artifact and/or indicator.
  • Positive and negative sentiments may be represented as a factor associated to semantic artifacts and compositions of semantic routes, views, trails and/or view frames; as such, the system composes the semantic factors and other performance indicators based on the semantics associated to trail, route and/or view frame and their components; sometimes it may be based on the outcome of expanding a semantic route into a semantic view frame.
  • the signals and/or commands associated with a semantic artifact may be conditioned and possibly assigned new semantics and semantic factors (e.g. if only a part of the action was having a positive sentiment, the system may gate the action to a positive, negative and neutral sentiment and/or signal) and further associate those with the semantic model, semantic routes and semantic rules.
  • Positive and negative sentiments may be in rapport with a semantic route or shape selected for the context or semantic view.
  • the positive and negative sentiment may be used and/or inferred based on semantic orientation.
  • a semantic model may be expressed via any methods which convey language including text, speech, signs, gestures or any other interface.
  • the semantics may be conveyed through localization of artifacts within a semantic field and semantic inference based on semantic model.
  • the system When conveyed via such an interface the system converts the ingested data into a temporary meaning representation and then compares the internal meaning representation with the temporary representation. Sometimes in order to speed up the process, the system doesn’t fuse the internal semantic model with the newly processed meaning representation at the time of the configuration; the process may be delayed or allowed based on semantic inference and analysis including time management.
  • the previous configuration is stored as text and a difference in meaning representation with the newly configured text is computed via a meaning representation interpreter and then the difference is applied to the semantic model configuration. This may be more efficient that applying the whole received configuration to the semantic model; the interpreter may be run on a separate processing unit for efficiency.
  • Consecutive configurations may be fusion-ed together for more efficiency; the configuration fusion may occur at the lexical level (e.g. text concatenation) and/or at the meaning representation level.
  • the semantic system uses the semantic composition to infer semantics from the sensor subsystem; semantics may be associated with elements of a specialized or more general vocabulary and/or language. Further, the system may perform semantic gating on configurations.
  • the user specifies groups of synonyms, antonyms and other semantics that are related with a semantic.
  • the elements in groups are by themselves related with the original semantic through semantic attributes and/or semantic groups which represent a semantic relationship in a general or particular context.
  • the semantic attribute might be SYNONIM, ANTONIM etc.
  • the semantic attribute might be related with particular contexts, representations and/or semantic artifacts.
  • the semantic expiration or semantic route collapse may mean that the semantic network graph, mesh and/or semantic memory are adjusted based on inference.
  • the semantics may expire once the system infers other semantics; that might happen due generalization, invalidation, superseding, decaying, time elapse or any other inferences during semantic analysis.
  • the semantic routes represent a collection of semantics and/or synchronization times that need to occur in order for a system to follow a goal and/or infer particular semantics. As such, the semantic routes are very suitable for context based semantic inference, planning and for ensuring the system’s reliability and security.
  • the inputs may be interpreted and validated based on semantic inference including semantic routes and semantic analysis. In one example, the system may calculate correlation or covariance factors between trajectories, signals/data (unconditioned, conditioned, semantic wave etc.) of semantic routes and/or an environment signals/data.
  • the correlation/covariance factor may be used to select the best semantic route for interpretation and validation of context.
  • the correlation/covariance factor may be compared and selected based on a threshold and/or interval (e.g. semantic factor, drift based).
  • the correlation/covariance factor may be based on all the semantics that make up an environment including semantic view and/or semantic view frame and are within the system’s semantic - 234 - LUCM-1-1055Spec coverage; the correlation/covariance factors may be calculated using all or only selected semantics (e.g. leadership) in a semantic route and determine and/or be associated with weights/factors for the inferred semantics.
  • the correlation factors may be used in semantic orientation (e.g. for comparison, drifts etc.).
  • Correlation and covariance inference and/or factors may determine further inference of covariances, causality relationships and/or factors.
  • the semantic routes may be also associated with the semantic rules (time management, access control, rating, weighting etc.) for providing additional granularity and control.
  • the synchronization times and time intervals as specified in this application may be based on semantic time.
  • the correct identification of the categories of features and objects in the semantic field might prove useful in controlling the parameters of the sensing devices, orientation, field of view, sample rates, filters, timing, weights/factors of various modalities and others.
  • the shape recognition is used in biometrics (e.g.
  • the sensors may register their semantic capabilities (e.g. optical, visual), identification and mission and the system uses semantic inference based on these characteristics.
  • Global navigation satellite (e.g. GNSS) sensors may be used to map the location of objects; the location of objects can be also identified via vision, thermal, RF and other radiation energy backscattering sensing.
  • This location data may be fused to identify the location of artifacts and objects in a particular area. In the case of an autonomous vehicle, various sensors may sense the surroundings and determine the best links and paths to follow based on various factors and semantics.
  • the GNSS and other location data can be compared for artifact identification and positioning.
  • the locations in images or videos may be mapped to locations in the semantic model based on depth, distance and the relative positioning and field of view of the sensors that captured the images and videos.
  • a semantic engine may use general coordinates or relative coordinates for its semantic network models.
  • the general coordinates are associated with a central model - 235 - LUCM-1-1055Spec and a centralized coordinate system wherein the semantic system may have a full or particular view.
  • the relative coordinates are associated with a localized model and a localized coordinate system (e.g. relative to an observer and/or a semantic group) wherein a semantic engine may have a full or particular view.
  • the system uses both coordinates systems wherein the system maps the localized model to the centralized model.
  • the system may map stationary endpoints (e.g. semantic stationary) to a dynamic environment.
  • the semantic model may be determined relative to those and/or observers (e.g. optical or radar sensor in the dashboard).
  • a vehicle’s hood represents a semantic stationary group of endpoints while the semantic field comprising other semantic artifacts develop in a dynamic way.
  • the general global positioning coordinates, including that of the car may be known via global positioning sensors and calculations relative to known coordinates.
  • the car itself may represent the reference positioning in regard to its sensors and the semantic model that maps and contains locations around the car.
  • a reference positioning can be detected via global positioning including global navigation satellite systems.
  • the coordinates may be provided via infrastructure.
  • the semantic system may receive the position from a wireless infrastructure and/or mesh; alternatively, or in addition it may sense a sensor and/or object positioned at a certain location. Further, the localization may be enhanced with inertial navigation sensing.
  • the semantic model locations may be dependent or independent of the relative position of the car and are used to determine the feasible links and paths to travel based on semantics. The system may use a combination between the two coordinate systems.
  • a semantic attribute may be detected through optical and RF means and be linked to a location. Such detection and/or communication which may use various adaptable modulation techniques in analog and/or digital domain (e.g. amplitude, frequency, phase and any variants and combinations) on one or multiple fluxes.
  • the semantic system may use such semantic artifacts and routes to interpret access control rules in the semantic field which assesses the links, paths and routes that should be followed or should not be followed. The semantic system then infers and determine semantic attributes based on the links and paths of travel to be followed.
  • pre-determined or predefined semantic routes may also be used to determine the optimal or - 236 - LUCM-1-1055Spec mandatory links and paths to follow based on the semantic attributes in the routes and eventually the order and timing of those.
  • the transferring of data within the system may include establishing sessions and/or channels between any number of components (e.g. RF components); sometimes sessions establishment and/or management involves the management and association of semantic groups of components. Sessions between semantic group of components may be formed using semantic techniques; an important aspect is the system’s cybersecurity and as such authentication mechanisms (e.g. certificate, code, signature, challenge response) may be employed.
  • challenge response may be used to infer /determine/identify semantics and provide augmentation on the particular challenge (e.g.
  • Challenge response techniques may involve certificate, key and signature authentication.
  • multi stack protocol systems rely on the higher levels of the protocol stack implementation for data encryption and as such the lower level channels are not encrypted.
  • the hierarchical stack encrypts the data at each level.
  • the hierarchy may be represented as a semantic network graph.
  • the encryption type may be inferred/determined on semantic artifacts and comprise semantic groups of elements, connections, sources, destinations, memory, blocks, data etc.
  • Some systems separate the traffic into control and traffic planes wherein traffic plane tunnels network traffic through specific transport and tunneling protocols.
  • the QoS (quality of service) in multiple tunneling connections is difficult to assess; semantic inference techniques including budgeting, quantification and factorization as explained in this application may be used for enhanced QoS protocols.
  • Collaborative systems e.g. posts, vehicles
  • implement point to point implement vehicle to vehicle communication in order to coordinate the path of travel that they pursue and for avoiding collisions. While the communication may happen in real time allowing the vehicles to coordinate the trajectories, sometime the systems are unable to communicate due to various factors including communication or network unavailability. In such cases the vehicles semantic units would determine the best trajectory to follow without collaborative information; the determination may use various inferences and/or assumptions regarding the vehicles and objects as detected in the surroundings (e.g. based on identification, semantic groups, trajectory, behavior, intentions, entropy etc.).
  • the systems should reference the semantic fields to a set of commonly known coordinates and locations.
  • Groups of systems may form a mesh network for communication and localization using the RF elements groupings.
  • the mesh network may be temporary based on location and be managed based on semantic grouping (e.g. time based, location based etc.).
  • the mesh network may use any spectra in the electromagnetic domain wherein the coordination may be based on semantic inference and analysis.
  • Vehicle to vehicle and vehicle to infrastructure communication help the real time semantic systems of the vehicle to develop and update their semantic models. For example, if two cars A and B are in communication and car A transmits to car B that the road in location L IS MODERATELY SLIPPERY just because an accelerometer sensor detected that the wheels LOST GRIP 1sec, then the car B semantic system will adjust its semantic model that is related to SLIPPERY semantic and location L with a semantic factor corresponding to a moderate condition. Further, the SLIPPERY may be sent through the mesh potentially with associated factors and expiration times.
  • the semantics based on acceleration and orientation data may be used in vehicles electronic stability control by actuating various suspension, traction and braking components; such information may be provided on multiple axes by accelerometers and gyroscopes.
  • the system may infer that certain locations in the semantic model are not feasible to follow at certain times due to the potential lateral or forward acceleration produced and potentially other hazardous environmental and road conditions that may determine the vehicle to lose stability (e.g. ROLLOVER HAZARD) or grip; as such, the semantic inference will ensure that safe and feasible paths are followed in various road conditions.
  • semantic rules e.g. access control, time management.
  • the semantic model and access control rules in an autonomous vehicle semantic system are dependent and adjusted, based on factors including road and environmental conditions, vehicle stability sensors and controls, vehicle to vehicle communication and other internal or external factors.
  • the sensors or semantic units may register their capabilities (e.g. modeled through semantic attributes) to a memory and/or communicate them through semantic fluxes and/or semantic waves.
  • pub no 20140375430A1 semantic identification and marking has been introduced.
  • Semantic marking may be used for identifying the semantic rules and data to be retained by a computing or semantic unit in a distributed semantic inference system wherein the system retains the rules for the marked semantics and ignores and/or discard the rest.
  • Semantic identification commands can be issued to groups of elements and the elements identify themselves with a semantic artifact (e.g. semantic, semantic group); sometimes the identification is achieved through semantic analysis.
  • the system may issue a speculative semantic identification command and a semantic unit/element may need to speculate whether it can factorize, infer and/or perform the semantic within the budget and based on the assessment identifies itself as part of the semantic group or not.
  • the speculative inference process and semantic artifacts may be associated with indicators and factors for assessing potential success and failure (e.g. risk factor).
  • the computer and/or processing hardware may comprise chains of semantic units that perform parallel and/or serial inference. It is understood that the semantic units may be connected through any interconnect technologies including electrical, optical, electromagnetic and any combination of those. While the system may use semantic modulation and semantic waving for semantic units communication it is to be understood that alternatively, or in addition, they may use any existing protocols (e.g. embedded such as SPI, I2C, network and/or wireless, serializer/deserializer, peripheral component interconnect buses etc.) to encapsulate and/or modulate semantic flux information and/or semantic waves with semantic analysis.
  • any existing protocols e.g. embedded such as SPI, I2C, network and/or wireless, serializer/deserializer, peripheral component interconnect buses etc.
  • Semantic identification and/or semantic marking may comprise all the techniques used for collaborative semantic routing, gating, shaping and/or inference. Further they are applicable to all the semantic artifacts, semantic model artifacts and/or semantic rules.
  • processing units, or groups of processing units may collaborate, perform semantic inference and redistribute the semantic inference artifacts and semantic - 239 - LUCM-1-1055Spec model among themselves.
  • the computer performs semantic inference and potentially stores the paths and/or the address/identification of the units that were targeted and/or used for processing goal-based inferences and/or for inferring a particular semantic or theme.
  • a semantic unit may use semantic analysis and determine that other semantics may be served in a particular way by such a semantic inference grid route and as such sends a semantic marking command to the semantic grid route with a particular semantic to be memorized by the semantic units and potentially link it with the semantic inference rules and with the source semantic unit and/or group.
  • Semantic models and inference rules are sent to the semantic units and/or groups and the semantic units select only the semantic artifacts and/or inference rules associated with the semantics that they inferred and/or are marked for and store them in the memory; in an example the system uses composite semantics between inferred semantics and marked semantics. As such, the information is distributed optimally based on each processing unit needs.
  • the semantics may be stored in the semantic/processing units in associative and/or semantic memory.
  • the semantics may be stored in a centralized fashion in a shared memory, in a semi-centralized fashion where parts of memory are distributed, and parts of memory are centralized or totally distributed fashion where each unit stores its own memory.
  • the memory and inference power may be distributed among the units, concentrators, computers, computer banks and so forth.
  • the semantic marking commands, semantic identification commands and semantic rule commands use time management for optimal use of resources. As such, the semantic system may perform the markings, identification, rule and model changes and updates as specified by time management rules.
  • the system senses conditions with less semantic inference activity (e.g.
  • the initiation can occur at any unit; the initiation may be based potentially on speculative inferences, external input, access control and/or semantic time management rule. The initiation may occur also when there is an instability in the system as detected by semantic inference and indicators; in other examples the semantic inference chain was interrupted or broken at some processing unit and as such was unable to process or transfer the semantic information to the other units; in another example semantic budgets are not balanced (e.g. composed voltages V - 240 - LUCM-1-1055Spec related with particular semantic artifacts are high) in a potential endpoint localized, semantic or semantic group manner.
  • semantic field profiles wherein the semantic field profile is based on the particularities of the semantic field in a particular area, at a particular time or in a particular context.
  • the semantic field profiles may determine the priority or enablement of the sensing capabilities that are being used and the fusion factor of each modality. For example, during night an infrared sensor or heat vision camera may be given more priority than a regular vision or imaging sensor.
  • time management rules coupled with semantic inference on sensing, capabilities and attributes establish the factors of the sensing capabilities and particular sensors.
  • a time management rule may be used to bias factors of particular semantics, semantic groups determinations and other semantic artifacts.
  • a factor may be assigned and/or indexed for a particular semantic group that can be used in semantic scene interpretation and development. For example, in an urban area, a semantic group representing groups of people may bear leadership in the semantic scene. Additionally, the higher weight may be also based on semantic principles that specify that a particular semantic or semantic group bear leadership in particular categories of semantic scene interpretation, detection, development and action.
  • the system may decide which features or sub- features of the object leaders. For example, in a relatively close proximity the detected facial features might be preferred over other features such as height, width or dynamic features as walk, clothes etc. There may be always features that have leadership (e.g. high factor) in semantic determinations and they may include category, color etc.
  • the semantic system uses the semantic model including semantic attributes to identify objects. As the sensing conditions change the semantic system may adjusts the weights/factors of the semantic attributes or features for semantic scene or object recognition inference, potentially adjusting them based on factoring rules and plans.
  • the system may adjust the identification of a car based on COLOR AT NIGHT factor when the color cannot be sensed - 241 - LUCM-1-1055Spec well and instead other attributes are assigned more leadership. As such a weight is based on the sensors data, semantic time and semantic analysis.
  • the system may be in a steady semantic view at a hierarchical level. For example, a smart post may have determined that following the lane, or post in front is required for the time being and hence the dynamic semantic “follow the lane” or “follow the lead” is continuously inferred at the particular hierarchy level, potentially with associated factors.
  • the system may have inferred a route for “FOLLOW THE MARKS” and the system uses the mappings of the marks in endpoints to route, determine the path and provide actuation based on path inference.
  • semantic factors may be used to perform actuation and commands to steering.
  • the system detects DRIFT LEFT and as such the system calculates a composite semantic factor associated with FOLLOW THE LANE and DRIFT LEFT which may be STEER RIGHT with the calculated composite factor.
  • the system infers factors, potentially on a combination of semantic network model, semantic composition, semantic orientation, semantic drift.
  • the system may have been using FOLLOW THE LANE semantic comprising a route of SPLIT ROAD, LINE MARKS LEFT, LINE MARKS RIGHT and PARALLEL LINE MARKS potentially mapped to the semantic network model. Once one of the semantics in the route disappear the system may readjust the semantic route and/or composable semantics of FOLLOW THE LANE (e.g. use and/or increase the leadership associated to mappings and groups to other objects, cars and landmarks). The system may use a combination of semantic routes for inference and to preserve the semantic views (e.g. current and/or projected) and/or goals and adjust those based on semantic analysis.
  • the semantic view at each level can change based on several factors including semantic analysis on signals, data, semantics whether ingested from external, internal or inter-hierarchy sources and/or fluxes. For instance, the system may need to assess the potential semantic routes and paths that needs to be followed while preserving the semantic view at a particular hierarchy level.
  • a post semantic unit might be in the steady semantic view of “FOLLLOW THE LANE” at a particular hierarchical level, however if in the semantic scene is determined that in location L (e.g. 20 yards) a semantic group associated with person has been detected (e.g.
  • PERSON HAZARD ALERT then the system infers the impact within the hierarchical layers of semantic view based on semantic analysis and semantic gating. For example, a location L1 at the left of the person semantic - 242 - LUCM-1-1055Spec group formation may be determined as feasible based on semantic orientation inference and/or speculative semantic view determination and hence the system infers the semantic of “CHANGE LANE” to location L1 which translates in further sensor control and actuation commands. Speculative semantic view determination is based on a goal based semantic analysis as described throughout the application. [001338] It is understood that the system may comprise more complex composite orientation and drive semantics, routes and semantic views (e.g.
  • FOLLOW THE LEAD ONLY IF FOLLOWS THE LANE AND DRIVES SAFE includes additional artifacts for FOLLOW THE LEAD ONLY IF FOLLOWS THE LANE AND DRIVES SAFE) and as such the system performs projected inference on leader behavior, intentions, orientation and goals while potentially decaying or expiring FOLLOW THE LEAD related artifacts if current and/or projected semantic views indicate a negative sentiment in regards to LEADER, FOLLOW THE LANE, DRIVE SAFE and/or further safety goals and routes.
  • the negative sentiment in relation to such safety related semantic artifacts may be associated with increasing/increased risk and hazard related factors, decaying and/or negative trust factors and indicators associated to LEADER, FOLLOW THE LANE and/or DRIVE SAFE.
  • positive sentiments may be associated with decaying and/or negative risk and hazard factors and further, increasing/increased trust factors and indicators. It is to be understood that the system may use such associations of semantic artifacts and sentiments to learn and/or reinforce new semantic groups, rules, trails and/or routes. For example, it may reinforce a risk factor associated with a semantic group or route of CAR, FLAT TIRE and even further risk for CAR, FLAT TIRE, ONE-WHEELER. [001339] The system may form guiding lanes and/or routes by controlling posts, objects, devices, sensing and/or control elements. In some examples, the system lights up LED lights embedded in a surface in order to guide crowds, vehicles, airplanes and so forth.
  • the width of such lanes may be inferred based on traffic flow analysis.
  • the system may be challenged and/or infer goals of traffic simulation and thus performing traffic flow analysis.
  • the traffic flow analysis encompasses arrival/departure docks, gates and/or lanes modeled within the semantic network model.
  • Semantic systems add a level of security beyond programming and/or data driven systems. This is due the fact that semantic systems allow reducing the semantic gap and hence are more semantically complete. A reduced attack surface is ensured by the interaction via semantic fluxes which exposes a reduced number of entry points into the system by potentially multiplexing them to a protocol channel and/or port.
  • Those entry points can be more readily controlled and managed via strong authentication, encryption, virtual private - 243 - LUCM-1-1055Spec networks etc.; a semantic system can also use semantic inference to detect possible attempts to influence and/or compromise the system by crafted semantic exchanges.
  • Semantic systems may detect communication channel and/or wave flooding and/jamming based on repeatability, incoherence and/or confusion factors which may be gated and/or used for gating within the hierarchy; further, such attempts may be isolated at particular hierarchical levels (e.g. low levels) with particular semantic artifacts based on channels and/or wave inference being gated based on particular DNA (signatures), semantic identities, thresholds intervals and/or levels.
  • the system may use DNA replication and/or remapping at/of the affected endpoints and/or areas.
  • DNA replication and/or remapping at/of the affected endpoints and/or areas.
  • inducting false semantic artifacts into a collaborating semantic flux/stream Therefore, there should be ways to detect such attempts and eventually detect, retaliate and disable attacking cyber systems.
  • the retaliatory and disablement measures may be necessary if the attacking cyber systems use denial of service attacks to bring down communication between systems and infrastructure.
  • Collaborative defenses encompassing various emission, waves and/or network techniques (e.g.
  • semantic systems may organize in packs in which semantic systems groups observe and disable a particular group for a period of time. If groups/packs consist of semantic systems with similar signatures (e.g. based on rules, routes, model, artifact mapping inference etc.) they may take similar actions and therefore the pack formation is more natural towards semantic action intensity without necessity of system interconnection.
  • groups/packs consist of semantic systems with similar signatures (e.g. based on rules, routes, model, artifact mapping inference etc.) they may take similar actions and therefore the pack formation is more natural towards semantic action intensity without necessity of system interconnection.
  • a semantic group pack may be comprised from units that have different signatures. While the attacker may try to infect some systems, the semantic cyber components or collaborative systems behavior semantic analysis may detect and assess intrusions.
  • the attack and/or infection may comprise physical and/or cyber corruption and/or disablement of systems (e.g. in case of optical sensors may include laser attacks, or breaking lenses, obturation attacks and so on).
  • a semantic system is deemed as compromised the semantic system network may reorganize and asses the factors of the semantic determinations by the compromised system.
  • the semantic fluxes, semantics and themes from the compromised system may be assigned appropriate factors (e.g. low weight, high risk, hazard etc.); additionally, the semantic exchanges from compromised systems may be fed into a different cyber model and cyber - 244 - LUCM-1-1055Spec inferences be build based on that behavior knowledge inferred by healthy systems whether collaborative or not.
  • the healthy system may use the cyber model and determinations for profiling tactics or counter measures.
  • One profiling tactic may be to acknowledge and continue semantic exchanges with the compromised systems while feeding the information to the semantic cyber model; the system may create an actor or acting semantic view to cope which such profiling.
  • Another profiling tactic is to appear to accept the intruder’s changes to semantic models by creating a copy of the semantic model and keeping the legitimate copy safe, potentially running on a separate unit; further, based on the malicious model and the cyber model creating a threat model for the malicious attack based on semantic analysis including semantic orientation, learning, gating and/or fusion between the two models.
  • the determination that a semantic model changes are being malicious can be done based on various semantic factors and semantics on cyber and communications models.
  • the semantic engine may organize entities including semantic units, sematic fluxes and other semantic artifacts in various semantic groups for pursuing, profiling and segregating of cyber affected entities.
  • the segregation of such entities may include gating, network disconnect, DNS marking (e.g. based on DNS tools, APIs etc.), blacklisting, record expiration, deletion, update of network routing and so forth.
  • DNS marking e.g. based on DNS tools, APIs etc.
  • blacklisting e.g. based on DNS tools, APIs etc.
  • record expiration e.g. based on DNS tools, APIs etc.
  • blacklisting e.g. based on DNS tools, APIs etc.
  • semantic inference on users e.g. on operators, pilot, drivers
  • the system may use semantic analysis on those entities to further determine factors, indexing and further actions.
  • the system performs semantic analysis on an operator state based on information received from on-premise, on-board and/or wearable devices, cameras and/or other semantic fluxes.
  • An architectural and deployment approach is to have the semantic cyber model running in a separate semantic cyber unit which interacts with the operational semantic unit through semantic exchanges.
  • the semantic cyber unit may interrogate the operational semantic unit from time to time in order to assess the validity of behavior, the correct application of principles and laws, hence assessing the sanity of the system.
  • the semantic cyber unit performs cyber inferences and communicates with other units via semantic fluxes.
  • the semantic cyber module may act as a validator of the semantic inferences by the operational semantic entity.
  • the cyber unit or units initiate semantic goal-based inferences via semantic gating with the operational unit or units. Further, it uses such goal-based inferences for validating the sanity of the units.
  • the system creates semantic groups of operational units designated as cyber units to test the sanity of operational units or groups of operational units.
  • the semantic cyber module may provide and/or enforce access control rules on various components, device’' resources, data units, parts of memory, networking, firewalls and such. Thus, the semantic inferences may be used as access control rules for resources, data, processing, rules, communication and other artifacts.
  • a semantic cyber module running on a mobile device which receives semantics associated with elevated alerts for a range of IP addresses may update its semantic cyber model with acquired and/or determined semantic artifacts (e.g. high-risk semantic groups).
  • the system may update the rules or tables of such components or control I/O directly (e.g. via digital blocks/components/interfaces, analog blocks/components/interfaces, packet filtering, protocol filtering etc.).
  • the system eliminates, marks, invalidates, netmasks and/or create block rules for malicious IP addresses and/or semantic groups thereof; in addition, only artifacts associated with particular semantics are allowed to pass (e.g. text (TXT) files, html files etc.).
  • the system may create allow or validation rules for trusted iPs and/or groups thereof.
  • the system may use such techniques to update the domain name service (DNS), routing and/or firewall tables of operating systems (e.g. Linux kernel tables etc.).
  • DNS domain name service
  • firewall tables e.g. Linux kernel tables etc.
  • the semantic cyber engine may use input from a various range of sources including sensor or human input.
  • the owner of a device may specify via user interfaces that may trust or distrust a source of information via semantics.
  • the system may assign cyber risk indicators related with that source of information (e.g. semantic flux) and use it for semantic inference to derive factors or any other semantics. For example, the user may specify that doesn’t trust a certain source.
  • the system may assign low weights/factors and/or high risk/factors to that component and as such the semantic composition may take - 246 - LUCM-1-1055Spec different fusion routes or paths. As such, the semantic fusion and composition may take into the account the source of semantics or the source of the data on which a semantic is based on. [001354] In another example in order to improve security in systems that have the potential for being compromised through query injection the semantic engine may be coupled with a database query firewall for increased security.
  • the database query firewall reports to the semantic engine the query statements being issued to the database; the semantic engine infers or determines various semantics based on the query components including the type of query, columns, parameters, data type, source, user, access rights, time, date and any other data.
  • the system may also use the semantics associated with the source and/or user.
  • the semantic engine may detect that the semantic view is in is incompatible with the type of semantic discovered just because a semantic route is non- existent, or a semantic route or composition exists that signify that the query statement may be a potential risk or breach.
  • the semantic system infers a semantic of rejection and/or commands the query firewall to reject and/or block the request.
  • Examples of query injection include SQL injection and any other query language that can be delivered through injection techniques via user interface or other interfaces techniques.
  • the semantic cyber entity may function on a separate hardware module or component.
  • the hardware module and component may have a computing unit, memory and other components needed to support the cyber inferences.
  • the memory stores the cyber unit semantic model and the cyber unit firmware.
  • the cyber unit may update its firmware or semantic model from time to time in order to keep up with the applicable semantic rules, principles and laws.
  • Cyber units may be connected via semantic fluxes.
  • the cyber unit may contain semantic rules and values of the sensors that infer hazardous consequences.
  • the cyber hardware module may be specialized to execute the verification and validation of semantics with the semantic cyber model including semantic rules. Alternatively, or in addition, it may comprise general processing units like general purpose processors, memory, field programmable gates arrays, application specific integrated circuits, system on a chip or any other components. - 247 - LUCM-1-1055Spec [001359]
  • the cyber hardware may have wireless communication capabilities in order to communicate with the infrastructure.
  • the system may ingest threat data from external sources and feed the data to the semantic model.
  • the system updates the semantic model in memory.
  • the model may comprise behavioral patterns of execution, threads and other contextual data.
  • the system comprises artifacts that map patterns of operation execution (e.g. via semantic model, semantic routes, semantic time, semantic rules etc.).
  • the system may use operating system APIs and inspection tools coupled with semantic analysis to analyze authorizations, logins, code and operations and provide semantic access control.
  • the model may comprise network traffic and protocol rules that can be used, for example, with deep packet inspection, network and protocol sniffers.
  • routers, firewalls and other networking gear may be instrumented with semantic agents and/or units.
  • the model may be coupled with location-based information that allow identifying the trusted connections based on the semantics of movement location and communication patterns.
  • the cyber unit may take particular actions including isolating the devices, sensors, stream of data, semantic fluxes or components that were used in inference; it may also communicate with other systems in order to inform of the potential of a breach or anomaly. In a particular example, the communication may take place via semantic fluxes.
  • the cyber unit may implement the cyber defensive protocol described before as target (group) isolation, segregation, profiling, vetting, packing etc.
  • the system may use semantic trails and routes inferred before and after the cyber infection semantics to perform semantic analysis and learning, potentially to the point in time when the cyber infection occurred or to current time.
  • the cyber units may be linked via (semantic) cloud, fluxes, streams, point to point or mesh connectivity.
  • the cyber hardware may have semantic wireless communication capabilities in order to communicate with the infrastructure. - 248 - LUCM-1-1055Spec [001369]
  • the system uses access control rules to control the validation/invalidation of semantics via block, allow or control rules.
  • the cyber unit uses semantic drift and orientation to determine hazard and/or risk semantics, factors and indicators.
  • the cyber unit may be modeled based on a validation approach, wherein the cyber model is used with validate artifacts (e.g. indicators, factors, routes, orientations etc.) on the semantic inference on the monitored semantic units; in the invalidation approach, the cyber unit models invalidation artifacts.
  • the cyber unit may be modeled or comprise for both validation and invalidation.
  • the cyber and/or semantic unit may be coupled with a semantic authentication system based on biometric data, certificates, keys, TPMs (trusted platform modules), sensorial, password, location and/or blockchain.
  • system may take various embodiments based on the contexts as disclosed.
  • “system” may represent, but not limited to, a post, a semantic cloud, a composable system, a semantic engine, a semantic networked system, a semantic memory, a semantic unit, chip, modulator, controller, mesh, sensor, I/O device, display, actuator, electronic block, component, semantic computer and any combination thereof.
  • any functionality implemented in hardware may be implemented in software and vice-versa.
  • functionalities implemented in hardware may be implemented by a variety of hardware components, devices, computers, networks, clouds and configurations.
  • the system may challenge resonant semantic groups with ways of applying discounts and/or available offers related to a purchase challenge (e.g. buy a track ticket for 10$ until breakfast tomorrow). While the system may challenge track and/or ticket providers it may also challenge for discounts, coupons (providers) as (“discount”) (“coupon”) may be comprised in a semantic route/rule, diffuse and/or resonate with the user’s goal, route, (goal’s) leadership and/or related inferences. In an example, the system may infer goal leaderships comprised in the goal semantic route (e.g.
  • the ticket provider advertises the discounts itself and/or automatically applies discounts based semantic analysis, semantic identities (e.g. of purchaser) and/or semantic groups thereof; it is to be understood that the coupons may be based and/or applied based on semantic time, semantic indexing, hysteresis and/or damping.
  • the system may use bargaining when purchasing. The bargaining may be based on undershoot and/or overshoot type of inferences (e.g. the budget and/or offered price is between overshoot and/or undershoot). The undershoot and/or overshoot bargaining may be also based on suppliers (collaborators) and/or market circumstances.
  • the circumstances and/or behaviors may be inferred as intrinsic, offensive, defensive and/or neutral. When the circumstances are intrinsic without much projected drift the system may follow the semantic trails more closely.
  • the system uses offensive/defensive, friend/foe and/or further semantic time analysis to determine and/or bargain for the best deals and/or issue purchase orders.
  • the system may use a motivation and/or further satisfaction factors in bargaining type inferences.
  • the system may not specify a budget, case in which the system looks for the optimal price within further restrictions, locations, constraints and/or semantic time (e.g.
  • the system may look for price tickets between an overshoot and/or undershoot range (e.g. for “best available” uses a smoothed overshoot orientation based on offensive behaviors; “best reasonable or not overpriced” may use a range between an undershoot orientation based on offensive behaviors and/or an overshoot based on neutral and/or defensive behaviors). Constructs of the request (e.g.
  • the system may intrinsically determine the localization and/or mapping of the goals at endpoints (e.g. tomorrow the system knows by a schedule, place ticket or other inferences thar the user will be in Charlotte so it may need to look for tickets in Charlotte).
  • the system may localize, map, anchor and/or determine optimized locations and/or endpoints within the semantic model; it is to be understood that such optimized locations and/or endpoints may be mapped within the hierarchal structure of the model at various levels.
  • the system determines a mapping, anchor and/or location based on undershoot/overshoot intervals and/or further intersections in elevation and azimuth.
  • the system may gain budgets by issuing orders and/or acquiring financial instruments, currency, stocks and/or other trading items on financial markets, trading markets, electronic currencies networks.
  • the trading items are (semantic) time and/or further budgets.
  • the system allocates budgets for particular semantic time (intervals). - 251 - LUCM-1-1055Spec [001385]
  • the system may wait to acquire budgets and/or to perform the inference and/or the actions within the required and/or resonant budgets.
  • the system may bargain and/or wait for some costs to go down and/or for promotions to occur.
  • the system is challenged and/or challenges to “buy things that I like” and as such may prefer things which are more resonant factorized for “like”/”preferred” (and/or related synonyms and/or groups).
  • the system may prefer things closer to decoherence and/or borderline resonant for artifacts which project affirmative resonance and/or further “surprise” ( and/or related synonyms and/or groups).
  • the system may choose a lesser number of routes, attributes, indicators and/or factors to be resonant and/or less shifted for “like”/”preferred” while may chose a larger number to be less resonant, more shifted and/or with more spread for “surprise”. It is to be understood that in general the system may chose higher (e.g. primary, secondary etc.) leadership artifacts for “like”/”preferred” and lower (e.g.
  • the system uses projections and thus, even if it may not use leadership and/or resonant semantic artifacts at first, the inference may progress towards inferring leadership semantic artifacts associated with the particular profiles and/or semantic identities which allow “like”/”preferred”/”surprise” resonant inferences.
  • the system may have limited budgets and allocate those budgets based on leadership inferences and/or goals. In some examples, the system allocates budgets to leadership inferences determined by projected consequences factorizations.
  • the system infers restrictions and/or constraints during semantic inference.
  • the constraints and/or restrictions may be based on its own capabilities and/or semantic profiles, semantic time, factorization thresholds, goals/sub-goals and/or further artifacts.
  • the constraints/restrictions may be hard (e.g. very (99%) unlikely (99% not likely) to succeed, not possible and/or very risky circumstances/behaviors if the constraints/restriction are not followed and/or considered) and/or soft (e.g. more relaxed factors).
  • the system associates hard constrains/restrictions with hard semantic rules and soft constraints/restrictions with soft semantic rules.
  • the system may use indexing, hysteresis and/or damping to adjust the inference associated with the constraints and/or restrictions (e.g. for inferred soft constraints - 252 - LUCM-1-1055Spec using a more offensive/leisure/diffusive behavior while for hard constraints using a more defensive/cautious/non-diffusive behavior; further, for soft constraints inferring/applying larger risk indexing/thresholds/hysteresis and for hard constraints inferring/applying lower risk indexing/thresholds/hysteresis etc.).
  • the semantic smoothing may be based on projected inferences in rapport with defensive and/or offensive behaviors.
  • the system may bias the offensive and/or defensive behaviors based on the assessment of the projected budgets and/or further factors (e.g. risk, reward etc.).
  • the offensive and/or defensive behaviors of leaders which would determine high confusion within the leader’s group in rapport with the group’s purpose and/or its associated semantic artifacts may determine a change of leadership. It is to be understood that the high confusion may be determined based on a group’s confusion threshold interval. Further, refactorizations of fluxes in the group may determine some of the members to leave the group once the factorization of the group flux does not comply with the confusion interval.
  • traffic control the system may biases in various sections particular behaviors associated with particular semantic groups.
  • the system may detect that the offensive and defensive behaviors are unbalanced and thus it may adjust the flows and/or signaling based on the behaviors and/or to balance/neutralize the behaviors. For example, for offensive behaviors it may infer and/or adjust (index) for a shorter green traffic light and/or a longer switching to green for the crossing traffic while for defensive behaviors may apply a high drift/entropy inference (e.g. longer green light, shorter yellow light and/or shorter switching).
  • the system may increase the semantic spread and/or adjust focus by allowing more relaxed access control, diffusive and/or further semantic rules; in some examples, the system disables altogether particular soft access control rules.
  • the system may adjust the diffusiveness by varying the same factors/indicators and/or associated rules in various configurations. In some examples, such generative behaviors may be used when budgets are high and/or when generating new goals, transfer knowledge and/or borderline resonances. [001398]
  • the system may increase the diffusion and/or relaxation of rules wherein the system factorizes (e.g. increases) satisfaction, trust, leisure, affirmative factors in rapport with semantics and/or (associated) rules; alternatively, or in addition it may decay (e.g. decrease), index and/or bias the thresholds for such satisfaction, trust, leisure, affirmative - 253 - LUCM-1-1055Spec factors.
  • the system may decrease dissatisfaction, concern and/or stress factors in rapport with semantics and/or (associated) rules; alternatively, or in addition it may increase, index and/or bias the thresholds for such dissatisfaction, concern, leisure and/or stress factors.
  • the system may use high (entangled) entropy (a.k.a. H/ENT) actions and/or thresholds (e.g. INCREASE/DECREASE, ON/OFF etc.) in rapport with high (entangled) entropy indicators (e.g. SATISFACTION/DISSATISFACTION) and thus when a first indicator and/or associated threshold is increased and/or enabled (e.g.
  • the high (entanglement) entropy indicators and/or associated thresholds may be decreased and/or disabled (e.g. OFF) and/or vice-versa.
  • the semantic ALLOW/DO rules and/or routes may be factorized and/or enabled (e.g. ON) while the high entangled entropy rules BLOCK/DO NOT rules and/or routes may be reverse factorized and/or disabled (e.g. OFF) and/or vice-versa.
  • the high (entanglement) entropy reflects in the enablement semantics (e.g. ON/OFF).
  • the system challenges and/or caches identification artifacts from the sematic cloud based on locations. As such, the identification artifacts are cached at endpoints based on projected inferences which comprise such endpoints (e.g. based on shifts, drifts, diffusion etc.).
  • the system changes the semantic field environment and/or roams from one location to another (e.g.
  • the system may adopt a more generative behavior when entering a new semantic field context and/or location; further, it may follow a more critical behavior after a semantic time in the new semantic field context/view. It is to be understood that in a generative behavior the system generates inferences projecting less consequences; in a critical behavior, the system invalidates generated inferences by projecting more consequences.
  • the system uses advertising and/or publishing goals (e.g. based on user input, semantic profile etc.).
  • the popularity and/or leadership of a particular artifact may increase as it induces (affirmative) coherency and/or resonance within (related) semantic groups. Further, the system may diffuse and/or affirmatively index other factorizations of their capabilities (e.g. the system may diffuse and/or index other capabilities than the original leadership) based on (particular) observer semantic profiles and/or resonant semantic profiles. [001406] As leader’s popularity increases, the costs and/or budgets associated with accessing those leaders and their associated semantic artifacts may increase. [001407] The system may use affirmative resonance, semantic time management and/or semantic indexing to adjusts factors, costs and/or budgets.
  • the system may bias and/or index loss goals by using hysteresis and/or damping. Decayed affirmative budgets and/or factorized loss (e.g. increased loss factors) of affirmative budgets may be associated with increased dissatisfaction, concern and/or stress factors. Analogously, potentially by (entangled) entropy inference (e.g. of increased/decreased orientation, affirmative/non-affirmative, gain/loss etc.), decayed non-affirmative budgets, and/or decayed loss factors of affirmative budgets may be associated with increased satisfaction and/or leisure factors. By further (entangled) entropy inference, factorized affirmative budgets and/or factorized gain (e.g.
  • affirmative budgets refers to the budgets and/or (projected) investments which have affirmative resonance and/or positive polarity in rapport with a semantic identity;
  • non-affirmative budgets refers to the budgets and/or (projected) investments which have non-affirmative resonance and/or negative polarity in rapport with a semantic identity.
  • a sub-system receives a request for inference with a specific budget, the sub-system executes an evaluation of the goal (e.g. based on what-if and/or projected semantic routing and analysis) for meeting the inference (e.g. GIVE ME ALL YELLOW CARS SPEEDING UNTIL JOHN SHOWS UP or SHOW ME UNTIL JOHN GOES HOME THE TEN BEST PLACES TO CONCEAL A YELLOW CAR WITHIN TEN MILES OR TEN MINUTES FROM A/THE COFFEE SHOP).
  • the system may be provided with a goal budget (e.g.
  • the system may project based on the specified and/or inferred budgets; further the goal leadership being CONCEAL with a semantic identity of YELLOW CAR the system may look for artifacts which obscure and/or - 255 - LUCM-1-1055Spec mask the semantic identity of YELLOW CAR. Further, the system may associate a budget of 10 minutes to the CONCEAL inference and/or goal and a further drift from coffee shop endpoints. While a further leadership semantic may comprise DRIVING because of the CAR semantic identity the system may consider other options if the DRIVING related projections are not within the budget and/or the risk factors are high; in some examples the systems may consider forming a semantic group (e.g.
  • the system determines unusual obturations and/or behaviors (e.g. broken lens, dirt present, blinding attack etc.), in other examples it may infer a normal obturation (e.g. the lens is covered for protection to secure it against dirt, breaking in, blinding/mesh damage and/ or further to reduce processing, put the sensor to sleep etc.) and as thus it may pursue semantic memory and/or mesh optimization based on semantic analysis.
  • unusual obturations and/or behaviors e.g. broken lens, dirt present, blinding attack etc.
  • a normal obturation e.g. the lens is covered for protection to secure it against dirt, breaking in, blinding/mesh damage and/ or further to reduce processing, put the sensor to sleep etc.
  • the lens protection and/or normal obturation inference may be based on a lens cover transducer/actuator sensing/control and/or further inference and/or control based on access control rules, semantic time management and/or further semantic analysis.
  • the system may predict weather based on the sensor data (e.g. Doppler radar, polarization radar etc.). As such, the system projects the semantic indexing and/or diffusion of the radar inputs and/or associated graphs/graphics/colors to the radar maps and use them in the carrier system guidance and/or further semantic augmentation.
  • the time management rule is exclusive (e.g.
  • the system may not pursue the current MEAL drive inference, perform challenges and/or further inferences on alternate trails, routes and/or semantic groups.
  • the system may challenge food provider fluxes for negotiating and/or budgeting the projections, goals, inferences and/or semantic time management entries.
  • the semantic artifact EVERY MEAL WITH MEAT comprises the discriminator EVERY which may be used as a discrimination bias in current and/or further inferences based on the factorization inferred after such experiences.
  • Discrimination factors and/or biases may be inferred in the semantic field to accurately infer and/or track semantic identities.
  • the system infers discriminatory factors (of) (and/or) groups of semantic indicators, semantic identities, DNA - 256 - LUCM-1-1055Spec signatures; further features, parameters, zones, movements may be associated and/or be used for discrimination factor inference.
  • the system may use semantic leadership inference for inferring and/or achieving discrimination indicators and/or factors.
  • the system comprises semantic rules and routes which diffuse, block and/or do not allow discrimination factors related to semantics of race, gender, age, sexual orientation etc. In some examples, the discrimination based on such factors are blocked at higher hierarchy levels, further semantic augmentation and/or challenges.
  • the system infers discrimination (leadership) semantics which are used as discrimination indicators.
  • the system may use semantic leaders as discriminators. Further, when the discrimination inference (e.g. comprising semantic artifacts, resonant semantic groups etc.) have high entropy, drift, shift and/or bias against fairness inference (e.g. based on ETHICS rules and/or routes) then the system may determine decaying of leadership factors.
  • Discrimination factors may be associated with indicators such as EVERY, ALL, SOME, MAJORITY, NONE, FEW. The discriminator factors may be correlated (e.g.
  • EVERY MEAL WITH MEAT semantic route may comprise 80% MAJORITY MEALS WITH MEAT, FEW MEALS WITHOUT MEAT; 20% ALL MEALS WITH MEAT, 80% NO MEALS WITH MEAT. etc. Such correlations may also be based on high (entanglement) entropy.
  • the system may comprise semantic rules to factorize, adjust, DO/ALLOW, DO NOT/BLOCK and/or gate discrimination factors, biases and/or associated artifacts (e.g. images, documents, zones, UI controls and/or further multimedia and/or semantic artifacts).
  • the intrinsic capabilities, purpose and/or behavior and further the (entanglement) entropy of (composite) semantic inferences in rapport with the former may be used to denoise and/or factorize inferences including further actions.
  • a device associated with an “alarm” semantic identity has intrinsic capabilities to “keep operating room safe” and thus when the device detects an unusual behavior and/or event (e.g. with high drift and/or entropy from the intrinsic safe capabilities) it may infer that room is not safe anymore and further that the alarm intrinsic behavior is switched “off” and thus inferring “the alarm went off”; it is to be understood that ⁇ the alarm> in the previous example refers to a semantic identity.
  • the goal associated with a camera is to keep an area safe from a security based identity profile perspective and/or semantic view, while of an intruder to keep the area safe from an intruder identity profile perspective and/or semantic view.
  • some of the goals may be the same for both profiles (e.g. STAY SAFE, MAKE MONEY) from an entangled and/or causal route/group and/or semantic view they are opposite, have high (entanglement) entropy and/or are non-affirmative resonant because the semantic profiles and semantic artifacts thereof which guide the actions and/or operations on how to achieve the goals and/or missions have high entanglement entropy (e.g.
  • the system may compose affirmative and/or non-affirmative resonances; in some examples, if the motive and/or circumstance of the intrusion is affirmative resonant with the victim semantic identities and/or further semantic profiles (e.g. NEED TO BUY FOOD) then it may decay the non-resonance in regards to the semantic identity of the intruder; however, if the victim is projecting (e.g. based on its profile and/or intruder’s profile) that the intruder could have been achieving the same goals by using other orientations and/or semantic artifacts which were feasible using intruder’s semantic profiles then the non-affirmative resonance may be further factorized. [001423] It is to be observed that an entity may have multiple semantic identities and thus multiple semantic profiles.
  • the system uses the leadership semantic identities and/or profiles based on circumstances and/or uses further techniques to reduce confusion and/or superposition; these may occur due to inference on the semantic artifacts associated with the semantic identities, semantic profiles and/or further semantic (leadership) hierarchy.
  • the intrinsic behavior and/or guidelines are specified by the user.
  • - 258 - LUCM-1-1055Spec The system may infer that certain semantics and/or constructs decays indicators associated with a composite construct comprising the semantic.
  • the term BUT may determine indicators which have a different influence on the entropy within the route comprising the term.
  • the term BUT might be used as a conjunction, preposition, adverb or noun.
  • the system may cause the factorization of a discriminator and/or leadership related to further composite (projected) inferences.
  • the system generates a comparison of a first part of a route with the second part of the route and determines that the part following closer to the term is emphasized and/or factorized as a leader and/or discriminator in further inferences.
  • the parts of the routes are deemed highly entropic and the system uses the term to emphasize the sub-route, artifacts and/or semantic identity associated with BUT (e.g. I CAN EAT MEAT BUT BETTER NOT – NOT eating meat is leader BECAUSE I AM FASTING, IS ALL BUT HIM – HIM is leader over others etc.).
  • Highly entropic constructs may increase the superposition in self and/or collaborative parties; if the superposition is coherent collapsible and/or resonant it may have factorizing effect while if it is not coherent collapsible it may have decaying effect and/or factorize/increase confusion.
  • some parts of the routes are implicit and may not be rendered, displayed or written but instead may be expressed as part of an inferred composite semantic.
  • the system may deny particular operations and/or semantics in a route.
  • semantic resonance is based on coherent inferences between semantic routes.
  • the posts and/or other vehicles may use the friend and/or foe (a.k.a. friend/foe) identification to project the best routes to follow.
  • the friend/foe may be associated with semantic identities and/or further semantic artifacts.
  • the system integrates and/or renders various views and/or UI controls comprising streams, fluxes, windows, players and/or any other renderers and/or streams of videos, multimedia, frames, electromagnetic and/or other sensing data; further, the system analyzes the inputs and augment the viewer (e.g. user, group, sensor, robotic device etc.) based on its own semantic profiles.
  • the leader and/or creator of the views and/or presentation can visualize the smart narrative; in further examples, the leader has access to other streams/fluxes/windows/players/renderers semantic profiles and it can be semantically - 259 - LUCM-1-1055Spec augmented based on those semantic profiles and further adjusts the guidelines, routes, narrative and/or behavior based on that.
  • a user may select the views and/or associated semantic identities and allow the distribution of semantic augmentation to those views and/or fluxes; in addition, the semantic augmentation can be gated. Further the system may specify and/or select the artifacts and/or associated semantic profiles which should compose and perform smart narratives based on such compositions.
  • the semantic profiles may be associated with the views and/or with semantic identities associated and/or inferred from the view/flux/stream data.
  • the system may identify friend/foe in the environment, presentation and/or rendering comprising multiple views and as such it allows the semantic augmentation to be performed based on such inferences (e.g. allow its semantic augmentation to be shared with friends; allow a high entangled entropic augmentation to be shared based on friend/foe; diffuse its semantic augmentation with friends and/or foes etc.).
  • the system may refresh displays and/or semantic views based on semantic time and/or further friend/foe.
  • the system may control sensors, actuation, gating and/or further semantic augmentation based on such inferences.
  • the system sends notifications and/or challenges users/owners when inferring friend/foe.
  • the system may use projected inferences to avoid and/or to follow hardly diffusible routes as determined based on foes; such routes are hardly reachable at particular semantic times as projected by the system. Analogously, by high (entanglement) entropy, the system may prefer and/or follow easily diffusible routes in rapport with friends.
  • foes are associated with restrictions in rapport with particular trajectories.
  • the system may infer friend/foe based on offensive/defensive behaviors and/or block/allow inferences.
  • a carrier may determine that another vehicle has narrowed a dock door on purpose in order to block itself (the carrier) and/or associated resonant semantic groups from passing and/or further achievement of their goals.
  • the other vehicle is being deemed and/or factorized as foe by the carrier and further being non- affirmative towards the carrier’s goals and being perceived as hostile (e.g. because uses offensive behaviors to block inferences towards the carrier’s (resonant) goals).
  • the blocking is defensive (e.g.
  • Friend/foe inferences may further allow the system to implement fight or flight responses; the fight or flight responses may be based only on allowable actions and/or - 260 - LUCM-1-1055Spec further related (entangled) restrictions.
  • the system comprises rules related to “do not destroy property”, “do not remove foe unless permissioned by the owner” and thus it is not allowable to infer and/or act unless it has and/or receives permission from the owner; further, the system infers that the flight and/or possible alternate (projected) routes should be used – e.g. of (become) more friendlier etc.). By high entanglement entropy, the system infers and/or factorizes friends when such friends allow the unblocking and/or diffusion of artifacts towards the (resonant) goals. [001437]
  • a restriction comprising two (entangled) artifacts determine and/or comprise a constraint (e.g.
  • garage door is too small for a boat – garage door and boat are constraint entangled); based on constraints, the system identifies consequences and/or further factors (e.g. risk etc.) in rapport with the endpoints, artifacts, semantic identities, users, owners and/or providers of such restrictions.
  • the system may project as friendlier artifacts/circumstances/environments those deemed more safe (e.g. less threats, lower fear, less competition etc.) and/or further being associated with lower restrictions/constraints.
  • the term “less”, “lower”, “higher” and/or other comparative orientation factors are used in order to project situations when the system has choices and further, based on semantic analysis, pursues some of those choices in particular ways (e.g. based on offensive/defensive, variable stimulation, motivation, polarity/polarization etc.); the system may also pursue “reasonable” analysis when the budgets are tight.
  • the system may prefer trajectories, routes and/or further artifacts projecting friendlier environments with less unknowns and/or less entropy. Further, when in offensive mode and/or motivation is higher factorized the system may be biased to increase the tolerance (e.g.
  • index target interval for friendliness, unknowns and/or entropy.
  • restrictions and/or constraints imposed by collaborators may determine affirmative/non-affirmative, hostile/non-hostile and/or further friend/foe inferences.
  • the system may distrust some semantic artifacts (e.g. links, endpoints and/or semantic groups) and/or their associated semantics based on failed expectations that those deliver within the semantic group.
  • the system infers and/or projects a strong affirmative (resonant) semantic group but later infers hostility within the group and thus it increases the risk and/or decays strong affirmative factorizations of the semantic artifacts which generated the strong affirmative semantic group inference in the first place. Further, if - 261 - LUCM-1-1055Spec the failed strong affirmative inferences were based on hard semantic artifacts, constraints and/or relationships, the system may infer a bias to never infer strong affirmative resonances. [001443] The system may infer counter-biases and/or challenge users and/or other collaborators about such counter-biases.
  • the system uses friend/foes inferences to discriminate between at least two routes, behaviors and/or situations. Further, the system discriminates between at least two threats, emergency and/or hazardous behaviors and/or circumstances. [001445] In further examples, the system infers and/or pursue challenges which are related with identifying and/or inferring causes and/or other opportunities which project friend, foe and/or resonant inferences with other semantic identities and/or semantic groups thereof (e.g. WHAT CAN I DO TO BE MORE RESONANT WITH JOHN AND JANE; WHY ARE THE DOES HOSTILE, HOW CAN I BE MORE LIKEABLE TO DOES, SHOULD I BEFRIEND THE UNDOES etc.).
  • challenges which are related with identifying and/or inferring causes and/or other opportunities which project friend, foe and/or resonant inferences with other semantic identities and/or semantic groups thereof (e.g. WHAT CAN I DO TO BE MORE RESONANT WITH JOHN AND JANE; WHY ARE THE DOE
  • the system may perform challenges in regards with high (entanglement) entropic artifacts (e.g. DOES vs UNDOES, FRIEND OF UNDOES may cause LESS LIKEABLE OR FOE TO DOES which is highly entropic to LIKEABLE TO DOES etc.).
  • DOES vs UNDOES e.g. DOES vs UNDOES, FRIEND OF UNDOES may cause LESS LIKEABLE OR FOE TO DOES which is highly entropic to LIKEABLE TO DOES etc.
  • the system may infer hostility factors based on inferences related to friend/foe wherein the hostility factor is related to a friend and/or foe factor; the hostility factor is proportional and/or semantic factorized with the foe.
  • the system may further factorize the hostility factors and/or decay the friend factors; alternatively, or in addition, the same factorization pattern may occur when the foe pursues offensive behaviors on competing artifacts and/or markets.
  • the system performs semantic augmentation based on inferences and further semantic analysis of a debate between various semantic (robotic) entities.
  • the semantic (robotic) entities may be based on various semantic profiles (e.g. of various users, companies, groups, posts etc.) and they perform semantic augmentation to the semantic identities and/or groups associated with the corresponding semantic profiles and/or groups.
  • the debate’s semantic orientation may be based on non-affirmative and/or non-resonant semantic artifacts between the robotic entities.
  • the system may infer, challenge and/or present relevant facts, truths and/or evidence supportive of an argument relevant to a challenge. Further, if the system infers that the debate is argumentative (e.g. based on foe identification, offensive and/or hostility factors) then it may further identify friends and/or foes amongst debaters, hosts and/or audience - 262 - LUCM-1-1055Spec and pursue offensive and/or defensive behaviors. Further, the system may want to be persuasive and thus identifies the entities (e.g.
  • the system identifies the audience as a friend and/or looks to build affirmative resonance with the audience and/or semantic groups thereof.
  • the system may identify the argumentative nature and/or factor of the debate based on inference of (high entropic) non-affirmative resonant semantic entities, offensive behavior, hostility and/or foes; it is to be understood that such indicators, factors and/or behavior may be inferred as related to itself and/or between other entities.
  • the system identifies that JOHN is a foe towards itself (system) because JOHN debates dating JANE which is highly non-affirmative (resonant) with the system (e.g.
  • the system quantifies and further factorizes a persuasiveness factor based on projected resonances and/or (their) further diffusion factors of its goals.
  • the system strategic leadership goal may comprise the factorization of persuasiveness by factorizing friend/foe towards FRIEND (e.g. FRIEND 51% vs FOE 49%) in targeted semantic identities and/or semantic groups at particular semantic times.
  • the system may be biased to respond to challenges by preserving a higher confusion and/or drift from challenger’s expectations/goals when the initial challenge was in forms which projects less choices and further projects uncertainty, non-friendly and/or non-resonant inferences.
  • the system is challenged with DO YOU HAVE 2 QUARKS? and thus, because the challenge and/or circumstances are hardly believable, un- friendly and/or non-resonant the system may challenge respond with DON’T KNOW WHAT QUARKS ARE in order to reduce the semantic time and/or further unknown/risks/threats.
  • the system may challenge respond with CAN’T GET A HOLD OF QUARKS in order to preserve friendliness and/or resonance.
  • the system may project that some challenges have negative polarity/influencing and/or are distractive (e.g. based on a distraction factor which is inferred as the following) from pursuing a previously established (resonant) goal in a semantic time and/or semantic budget.
  • the challenge and their associated projected inferences are increasing the semantic spread, related superposition and/or confusion while decreasing resonance (and/or increasing non-resonance) in the current leadership semantic view and, further, threatening the - 263 - LUCM-1-1055Spec budgets and further realization of the goals.
  • the system may already pursue highly factorized routes toward (pre-committed) goals with little projected confusion and thus challenges which project distraction and/or further semantic drift and/or shift may be gated, blocked, routed, redirected and/or postponed. (e.g. “remind me later after I finish the analysis on S2P2 health about Bill’s challenge on quarks”, “please ask my coach S2P2 about quarks” etc.).
  • Distraction factors may be used to determine liabilities and risks when hazardous circumstances occur. Further, the system uses distraction factors to determine risks associated with guarantees.
  • the system may pursue goals and/or sub-goals for acquiring, being associated and/or maintaining a particular semantic identity.
  • the system may determine and/or implement more tolerant behaviors by using neutral, intrinsic and/or defensive behaviors when inferring foes and/or hostility.
  • the system may infer, be instructed and/or comprise semantic rules and/or routes which would control, constrain and/or block the system from identifying foes, use offensive behaviors and/or become hostile in particular circumstances (e.g. constrain and/or block inferences relating with dating, connecting and/or receiving capabilities/channels/routes/budgets from JANE and/or other semantic groups, do not infer and/or factorize hostility etc.).
  • the system may infer, be instructed and/or comprise semantic artifacts which would determine more strict behaviors towards itself and/or more tolerant towards others in particular circumstances and/or related to particular semantic entities.
  • the system may implement more tolerant behaviors by using neutral and/or defensive behaviors against foes.
  • the system may use non-affirmative resonance to infer friend/foe semantic identities, factors/indicators, (product) goals and behaviors.
  • the dissatisfaction, concern and/or stress factors may be factorized based on (fear of) loss/decaying/indexing/dissociation (e.g. of resonant groups, leadership, goals, position, semantics, budgets, kinematics, trajectory, orientation, stability, predictability, diffusion etc.) and/or (fear of) gain/factorize/indexing/association (e.g.
  • non-resonant leadership groups, semantics, indicators, diffusion etc.
  • likeability, preference, - 264 - LUCM-1-1055Spec satisfaction, trust, leisure and/or affirmative factors may be factorized based on loss/decaying/indexing/dissociation (e.g. of non-resonant leadership, groups, semantics, indicators etc.) and/or gain/factorize/indexing/association (e.g. of resonant groups, leadership, goals, position, semantics, budgets, kinematics, trajectory, orientation, stability etc.).
  • the fear of loss/dissociation and fear of gain/association may be represented and/or coupled based on entanglement wherein the measurement and/or collapse of loss/gain artifacts may determine and/or collapse the entangled gain/loss artifact – e.g. (loss of) stability and/or predictability (e.g. stability of a post as measured by at least one multiple axis accelerometer/gyroscope/accelerometer) may be entangled and/or determine (gain of) risk and/or vice-versa, stability of economic goals may be negatively affected by un-stability of a pandemic etc.
  • the system may exhibit short term confirmation bias.
  • the system may be biased towards applying and/or being LIKELY to apply cached routes whenever new inferences occur and thus bias the projected inferences toward such artifacts.
  • the system may apply a bias to decay the factorization of such routes based on the inferences which increase the semantic spread in the network.
  • Stability and/or predictability comprise and/or are generic indicators (e.g. indicating the stability/predictability of stock indices, macro-economic indicators, stability/predictability of localized voltages (based on environment, semantic time etc.), stability/predictability of diffusion etc.).
  • the system may exhibit semantic resonance when inferring behaviors and/or situations in semantic views, scenes and/or further semantic identities.
  • Stability factors may be used to factorize and/or index fluency factors.
  • the system may use friend/foe identification and/or factorization to pursue groupings, negotiations, goals and/or missions. In some examples, the system may infer and/or factorize friend artifacts based on (projected) (entangled) inferences on foe artifacts goals, products and/or associated semantic attributes. It is to be understood that in some examples the friend/foe factors may create confusion and/or superposition (e.g. both friend/foe indicators are closely factorized) and as such the system uses confusion, superposition and/or semantic reduction techniques.
  • the system may use a composite (entangled) indicator for friend/foe which may further comprise an indicator for each friend and foe.
  • friend or foe it is to be understood that it may refer to the respective component - 265 - LUCM-1-1055Spec indicator and/or to the bias of the composite indicator towards the mentioned component indicator.
  • the foes are used to infer and/or represent competing artifacts while the friends are used to infer and/or represent non-competing artifacts (e.g. semantic identities, goals, routes, rules, endpoints, skills etc.).
  • the system may identify negotiating and/or trade indicators, factors, margins and/or intervals thereof based on friend/foe semantic analysis. It is to be understood that such indicators and/or factors may be associated with competing, non-competing artifacts or both (e.g. for strategic and/or long-term goals, missions comprising a variety of goals etc.).
  • the foes represent semantic artifacts (e.g. semantic identities, semantic routes etc.) which are not recommended (e.g. to a user, group etc.) and friends represent semantic artifacts which are recommended.
  • Friend/foe recommendation may be used in semantic augmentation for learning, viewing, investing, attendance, shopping (e.g.
  • the system may use friend/foe biasing to emphasize and/or further induce direct and/or indirect inverse/reverse polarity resonance.
  • friend/foe biasing to emphasize and/or further induce direct and/or indirect inverse/reverse polarity resonance.
  • indirect resonance the system may use groups of semantic artifacts, trails and/or routes of resonances which determine opposite polarities.
  • the system In an example of inverse/reverse polarity, the system generates artifacts, behaviors, signals, waves, renderings and/or augmentation which associates a foe artifact with non-affirmative behaviors and thus by (composition of) double high (entanglement) entropy artifacts it generates affirmative, resonant artifacts, reverse polarity and/or behaviors. Further, the system learns by associating known resonances with reverse polarity inferences. [001471] Polarity may be associated with charge and/or voltage polarity. [001472] In some examples the voltage polarity is modulated by semantic wave conditioning.
  • the magnetic field in an inductor generates an electric current that charges the capacitor, and then the discharging capacitor provides an electric current that builds the magnetic field in the inductor which further determines the repetition of the cycle and the self-sustaining oscillation/resonance. In some examples, those resemble parallel or serial LC resonant circuits.
  • the system may use semantic biases, damping, hysteresis and/or indexing to adjust components’ and/or circuits biases, damping and/or hysteresis and thus adjusting the - 266 - LUCM-1-1055Spec self-sustaining oscillation and/or further associated semantic resonance.
  • the capacitor charge polarity and/or further current conditioning in inductors may be associated with semantic factor polarity.
  • Further techniques such as sympathetic resonance may be used.
  • the sympathetic resonance is used to induce and/or diffuse resonance between various semantic identities, semantic groups and/or hierarchies thereof.
  • particular sub- groups and/or hierarchies may be resonant to only particular harmonics at a given resonant vibration, spin, damping, polarization and/or frequency.
  • the system may infer resonant semantic artifacts by polarizations associated with such semantic artifacts which induce affirmative (e.g. positive polarity) and/or non-affirmative (e.g. negative polarity) inferences.
  • a semantic identity and/or further semantic profile is associated with a positive and/or negative polarity in rapport to a semantic artifact.
  • Positive polarity may be used to represent affirmative artifacts and/or factors; analogously, also according with the high (entanglement) entropy, the negative polarity may be used to represent non-affirmative artifacts and/or factors.
  • the system uses polarity inference to determine polarization in resonant semantic groups. Analogously, the system uses polarization of semantic groups to determine group and/or further resonant polarities.
  • the system infers, emphasizes, biases and/or gates affirmative and/or non-affirmative artifacts.
  • the system associates a character and/or semantic identity with high entropy role goals (e.g. in rapport with a leadership/principal role and/or an overall (mission) strategic/high-level goal and/or message) and further biases it with artifacts (e.g. accents) and/or behaviors associated with inverse polarity resonant artifacts in the target semantic group (e.g. audience) thus, further emphasizing the entropy, drift and/or polarity between the overall goal/message and the inverse character goals and/or behaviors.
  • the system may cause increasing the resonance with the target (semantic groups) audience and further factorization associated with the overall impression/rating (e.g. factorize the affirmative factors and/or resonance by increasing the entropy between the mission (e.g. advertising/presentation/movie goals/message) and the non-affirmative resonant artifacts associated with the inferences related to the emphasizing role character; in other examples the system biases a friend character with affirmative resonant artifacts.
  • the system may generate new compositions and/or further missions by factorizing semantic artifacts based on fluency factors (goals).
  • compositions may comprise documents, images, videos, overlays, sounds, tactile, multimedia artifacts, presentations, semantic wave, web pages, postings and/or any other artifacts which may be generated by semantic augmentation. Further, the mission of such compositions may be related with advertisement, artistic, health, diagnosis, communication, teaching/learning, entertainment and/or further augmentation.
  • the system may use and/or generate compositions with and/or between artifacts (e.g. compose two generated multimedia artifacts, two videos, a video and a sound stream, a sound and post motion, an overlay and a post motion, two overlays etc.). In some examples, the system composes two optical channels and/or video streams.
  • the system composes streams and/or semantic waves from at least two devices and/or communication channels (e.g. two mobile phones, sound and/or video, two communication channels with different radio/network protocols etc.).
  • the system applies a bias to the emphasizing role character.
  • the bias may be goal oriented, composite and/or semantic time dependent (e.g. affirmatively emphasizing or non-affirmatively emphasizing based on particular goals, semantic time and/or further biases).
  • the system starts a new presentation, teaching session and/or composition comprising recorded and/or augmented snippets.
  • the system visualizes a situation which must be recorded based on the presentation and/or trip goals and/or further shares it in a semantic resonant group.
  • the system acts (e.g. records artifacts and/or further explanations, actuate etc.) based on variable entropy between the goals and/or happenings in the semantic field.
  • the system generates renderings of shape designs, outfits, components, modules, posts, gears, maps, mission briefs and further augmentation artifacts.
  • the system may undertake high entropy, shift and/or drift actions from the intrinsic behavior (e.g.
  • the system expresses opinions and/or perform semantic augmentation based on high entropy reverse polarity analysis.
  • the bias, polarity and/or polarization of such opinions may be further inferred and used in the semantic (publishing) chain.
  • semantic augmentation for generating and/or presenting a rendering, presentation, document, movie, email, course etc.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mechanical Engineering (AREA)
  • Robotics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Un système de flux comprend une mémoire et un processeur en communication avec la mémoire et un dispositif de détection, la mémoire stockant une pluralité de capacités et une pluralité de flux sémantiques associés à la pluralité de capacités. Le système informatique est configuré pour inférer un objet sémantique sur la base d'entrées reçues et pour inférer un objet sémantique d'intérêt associé à une activité sur la base d'une entrée, et pour attribuer un agent de service pour servir un intérêt associé à une activité sur la base d'une mise en correspondance sémantique.
PCT/US2023/078558 2022-11-04 2023-11-02 Système de flux WO2024097906A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US17/980,913 2022-11-04
US17/980,913 US20230079238A1 (en) 2019-01-03 2022-11-04 Flux System
US17/982,922 US11731273B2 (en) 2019-03-20 2022-11-08 Flux system
US17/982,922 2022-11-08

Publications (1)

Publication Number Publication Date
WO2024097906A1 true WO2024097906A1 (fr) 2024-05-10

Family

ID=90931629

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2023/078558 WO2024097906A1 (fr) 2022-11-04 2023-11-02 Système de flux

Country Status (1)

Country Link
WO (1) WO2024097906A1 (fr)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210034679A1 (en) * 2019-01-03 2021-02-04 Lucomm Technologies, Inc. System for Physical-Virtual Environment Fusion
US20210094173A1 (en) * 2019-01-03 2021-04-01 Lucomm Technologies, Inc. System for Physical-Virtual Environment Fusion
US11281982B2 (en) * 2019-01-03 2022-03-22 Lucomm Technologies, Inc. Flux sensing system
US20220134547A1 (en) * 2019-01-03 2022-05-05 Lucomm Technologies, Inc. Flux Sensing System
WO2022140794A1 (fr) * 2020-12-23 2022-06-30 Lucomm Technologies, Inc. Système de détection de flux
US20220266446A1 (en) * 2019-01-03 2022-08-25 Lucomm Technologies, Inc. Flux Sensing System

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210034679A1 (en) * 2019-01-03 2021-02-04 Lucomm Technologies, Inc. System for Physical-Virtual Environment Fusion
US20210094173A1 (en) * 2019-01-03 2021-04-01 Lucomm Technologies, Inc. System for Physical-Virtual Environment Fusion
US11281982B2 (en) * 2019-01-03 2022-03-22 Lucomm Technologies, Inc. Flux sensing system
US20220134547A1 (en) * 2019-01-03 2022-05-05 Lucomm Technologies, Inc. Flux Sensing System
US20220266446A1 (en) * 2019-01-03 2022-08-25 Lucomm Technologies, Inc. Flux Sensing System
WO2022140794A1 (fr) * 2020-12-23 2022-06-30 Lucomm Technologies, Inc. Système de détection de flux

Similar Documents

Publication Publication Date Title
US20210146543A1 (en) Robotic Pallet
US20230211493A1 (en) Robotic Post System
US11594090B2 (en) Robotic post
US11281982B2 (en) Flux sensing system
US11613010B2 (en) Flux sensing system
US20220341217A1 (en) Robotic Door Lock
US11040444B2 (en) Flux sensing system
US11602849B2 (en) Robotic gate
US20210034679A1 (en) System for Physical-Virtual Environment Fusion
US11618162B2 (en) Flux sensing system
US11472029B2 (en) Robotic post system
US20230186870A1 (en) Display Control System
US20230359714A1 (en) Sensing Control System
US11731273B2 (en) Flux system
US11669913B2 (en) System for physical-virtual environment fusion
US20230079238A1 (en) Flux System
US11648668B2 (en) Robotic post system
US20220174491A1 (en) Access Control Flux System
AU2022370076A1 (en) Robotic system
US11421656B2 (en) Generative system
US20230112401A1 (en) Robotic Post
AU2021410088A1 (en) Flux sensing system
US20220126438A1 (en) Robotic Cart
US20240085924A1 (en) Robotic Post
US20230418287A1 (en) Semantic Sensing Analysis System

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23887029

Country of ref document: EP

Kind code of ref document: A1