EP4162766A1 - Système de fusion d'environnement physique-virtuel - Google Patents

Système de fusion d'environnement physique-virtuel

Info

Publication number
EP4162766A1
EP4162766A1 EP20938858.6A EP20938858A EP4162766A1 EP 4162766 A1 EP4162766 A1 EP 4162766A1 EP 20938858 A EP20938858 A EP 20938858A EP 4162766 A1 EP4162766 A1 EP 4162766A1
Authority
EP
European Patent Office
Prior art keywords
semantic
artifacts
semantics
inference
factors
Prior art date
Legal status (The legal status 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 status listed.)
Pending
Application number
EP20938858.6A
Other languages
German (de)
English (en)
Other versions
EP4162766A4 (fr
Inventor
Lucian Cristache
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Lucomm Tech Inc
Lucomm Technologies Inc
Original Assignee
Lucomm Tech Inc
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 US16/891,893 external-priority patent/US20200298401A1/en
Priority claimed from US16/929,680 external-priority patent/US20200356951A1/en
Priority claimed from US16/999,691 external-priority patent/US11562565B2/en
Priority claimed from US17/064,198 external-priority patent/US11604832B2/en
Priority claimed from US17/076,979 external-priority patent/US11669913B2/en
Application filed by Lucomm Tech Inc, Lucomm Technologies Inc filed Critical Lucomm Tech Inc
Publication of EP4162766A1 publication Critical patent/EP4162766A1/fr
Publication of EP4162766A4 publication Critical patent/EP4162766A4/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
    • G05D1/0291—Fleet control
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • 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
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • 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
    • G06N5/025—Extracting rules from data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models

Definitions

  • This invention relates generally to robotic devices, including communicatively coupled devices which use variable semantic coherent inferences to allow the devices to perform semantic augmentation
  • 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 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 In the discussion which follows, for the sake of simplicity each of the foregoing will be referred to as a module; it should be understood, however, that the same description applies to other embodiments in which the module is a portion or section of the smart post, and not necessarily a discrete module. It is to be understood that the post may use any number of modules of any type. In an example, 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.
  • the signal conditioning is based on semantic wave conditioning, preferably based on semantic gating.
  • the system performs video processing.
  • the system performs semantic augmentation on video artifacts.
  • the system may form semantic groups of posts and physically connect them through physical movement of the semantic posts motor components.
  • the system uses concern factors in order to determine coherent inferences.
  • the system forms a semantic group based on semantic resonance.
  • the system invalidates a semantic group based on semantic decoherence.
  • the system performs semantic learning based on the inference of semantic resonance.
  • the system performs semantic learning based on the inference of semantic decoherence.
  • the system leams semantic rules based on semantic resonance.
  • the system leams damping factor rules.
  • the system leams semantic gating rules.
  • the system leams a hysteresis factor based on semantic analysis.
  • the system performs semantic augmentation using a variety of augmentation modalities.
  • the system performs semantic augmentation comprising semantic displaying.
  • the system performs semantic augmentation on particular devices based on ad-hoc semantic coupling.
  • the system performs semantic augmentation based on challenges and/or inputs.
  • the system performs semantic encryption. [0031] In some examples, 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.
  • the devices comprise semantic meshes.
  • the devices comprise biological sensors.
  • the biological sensors comprise at least one medical imaging sensor.
  • Fig. 1 is a front perspective view of a preferred smart post.
  • Fig. 2 A 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. 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.
  • 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.
  • 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. 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. 26A is an illustration of an observer directing attention to a first endpoint within a semantic field of view.
  • Fig. 26A 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.
  • 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 analysis including semantic time management (e g. REDUCED SPEED UNTIL ACCIDENT CLEARS, CONTROLLED SPEED UNTIL TRAFFIC FLOW IS NORMAL etc ).
  • 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.
  • TOF time of flight
  • 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.
  • sensing modalities e.g. microphones
  • 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.
  • the system may use environment profiling and learning based on corroborating radiofrequency energy returns with optical (e.g. camera) sensing wherein both modalities sense conditions in the semantic model (e.g. at various endpoints) and create semantic artifacts (e.g. semantic groups, semantic routes) based on sensed conditions and semantic analysis.
  • optical e.g. camera
  • semantic artifacts e.g. semantic groups, semantic routes
  • 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 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. Also, 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).
  • a copter module e.g. for aerial transportation
  • a display module e.g. for providing semantic augmentation
  • 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.
  • a module In the discussion which follows, for the sake of simplicity each of the foregoing will be referred to as a module; it should be understood, however, that the same description applies to other embodiments in which the module is a portion or section of the smart post, and not necessarily a discrete module. It is to be understood that the post may use any number of modules of any type. In an example, 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. springs, shock absorbers, coils, coil- overs, piezo components etc.) and attachment mechanisms for wheels or for attaching to a structure (e.g. automobile).
  • 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). .
  • mounts may be controlled (e.g. by compression, extension etc.) by 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. in a triangular pattern, rectangular pattern etc.) and thus adapting to conditions, movements and efficiency.
  • 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.
  • a power supply such as one or more Li-Ion batteries
  • 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. Li-Ion), fuel cells, super capacitors and/or other energy storage components.
  • 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.
  • 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 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.
  • control module provides commands to actuators incorporated in the base module for guiding the posts through environment. Further in the example the 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. As mentioned, the control units command actuation based on such commands (e.g.
  • 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. area B and/or via endpoint associated with B) which may entail high rewards in a particular circumstance however, routes and/or accessibility to the area are not available immediately (or maybe too busy) and/or maybe power scarcely available and thus increasing risk and/or lowering the total rewards of evaluating pursuing the goal via location endpoint B.
  • the 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.).
  • 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.
  • other material and further emission, dispersive, diffusive and/or quantum properties may be controlled (e.g. electromagnetic flux, conductivity, photon/photoelectron emission, polarization, etc.).
  • Decaying and semantic factors may be inferred and learned with semantic analysis.
  • the system leams decaying and semantic factors for semantic rules and/or semantic routes.
  • 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. 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.
  • 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 when they need detaching, they may move towards each other in order to detach the band clips at a closer distance in order to avoid band dangling. In other examples the posts detach while at farther distances and the band rolls attenuate the retraction movement through amortization or controlled retraction (e.g. based on springs and/or electrical means). It is to be understood that 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 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. by time management) and/or intensity of the attracting and/or repelling magnetic fields.
  • 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.
  • 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.).
  • the posts may or may not comprise j oining 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.
  • 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.
  • 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.
  • 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 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.
  • the power module can be attached or detached by sliding and/or lifting the assembly (e.g. other modules, trunk) on top of it, potentially using the attached hooks and further lifting the power module and replacing it with another one.
  • assembly e.g. other modules, trunk
  • 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.
  • Figs. 11 and 12 present example of such configurations where smart posts (for example, posts 101a through lOle; for simplicity, not all posts shown in Figs. 11 or 12 are labeled) are used in conjunction to form various configurations of smart carriers. As shown in those examples 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 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. Analogously, 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 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. rules, routes etc.) associated with the semantic artifacts which generated (increase in) confusion (in semantic views).
  • DO NOT semantic artifacts e.g. rules, routes etc.
  • Increases in confusion may be assessed based on thresholds, rate of increase, mapped overlays, indexing, hysteresis etc.
  • 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. While the example specifies the positive bias towards higher Z axis factors it is to be understood that such biases may be configurable or provided as part of semantic profiles (e.g. associated with users, identities, semantic groups, semantic artifacts etc.).
  • the authorization rights and levels may be based or assigned on hierarchy levels and/or artifacts in the semantic model.
  • the right for DRONE SHUTDOWN related artifacts may be assigned to particular semantic groups (e.g. of users, semantic posts, endpoints etc.). While the previous example relates to a more specific application it is to be understood that 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). Further, if the endpoints are associated with semantics and narratives and the endpoints are associated each with various semantic fluxes and/or agreements then the system may infer the intersection endpoint (a third endpoint) as an area associated with an inferred agreement (e.g. based on strong factorization) between the two semantic fluxes and/or agreements based on semantic analysis.
  • an inferred agreement e.g. based on strong factorization
  • 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 ). Further, the system may associate, 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 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.
  • 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. of identities, routes, endpoints, SPINNING PHONE AROUND A WHEEL, SPINNING WHEEL WITH A PHONE etc.) may be factorized differently and have different believability factors.
  • identities, routes, endpoints, SPINNING PHONE AROUND A WHEEL, SPINNING WHEEL WITH A PHONE etc. may be factorized differently and have different believability factors.
  • 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 obj ects 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 El and E2 e.g. governments, companies etc.
  • the system may change ratings of the entity El 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. E2); thus 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 (El) 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.
  • 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.
  • the telescopic capabilities of the posts may allow the realization of enclosed areas within a composed post structure. For example, as illustrated, 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. via telescopic means) for hooking and/or latching to person or transportation wagons thus the composite carrier acting as a driveline for such wagons.
  • the system may select specific wagons based on specific needs inferred via semantic inference and analysis.
  • users select specific wagons and the system assembles carrier beds based on the characteristics of the wagons and potentially the characteristics of the required route.
  • 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 comer 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 101 a-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. Further, when considering the goals and missions the system may use rewards and other factors-based inference.
  • 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. based on a semantic route) where the system detects that particular areas and/or endpoints to be traveled comprise restrictions (e.g. a location comprising a door of 4’ width).
  • such 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.
  • a semantic network e.g. endpoint
  • 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 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. Fig 13 and 14) in order to improve sensing and/or capabilities.
  • 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.
  • the system composes the smart posts to improve coverage and/or reliability of semantic inference.
  • 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.
  • the antenna elements module 7 may comprise panels of multi-array antenna elements 22; the panels may be disposed on the exterior of the trunk in a specific pattern (e.g. hexagonal). While in some embodiments the panels are fixed, in other embodiments the panels are automatically movable and composable and can be moved and organized in various patterns on the exterior of the trunk (e.g. two panels on two sides of the hexagon combine in a larger panel that can be oriented as well in various directions).
  • 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. laser diodes, photodiodes, avalanche photodiodes- linear/analog mode, Geiger-mode, etc., edge-emitting lasers, vertical cavity surface emitting lasers, LED, fiber laser, phototransistors).
  • 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.). It is understood that 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.
  • 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.
  • 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.
  • the semantic model may be associated with hierarchical semantic network models for further management of paths, fluxes/flows, routes and semantic inference.
  • the 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 artifacts e.g. semantics, semantic groups, semantic routes, semantic network model artifacts etc.
  • 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 artifacts e.g. semantic and/or semantic group
  • Semantic routes may be organized in a hierarchical manner with semantic routes comprising other semantic routes. Such hierarchical structure may be recursive.
  • the semantic routes may be grouped in semantic groups and participate in semantic inference.
  • Semantic routes associated with a semantic network model may be used for artifact (e.g. traveler, smart post) routing within modeled environments.
  • artifact e.g. traveler, smart post
  • 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).
  • 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).
  • an area and/or endpoint is associated with semantic artifacts (e.g. HEAT related, etc.) which have high (entanglement) entropy, drifts, shifts and/or factors as related with COOLING then the system may pursue the COOLING leadership and/or capabilities.
  • 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.
  • 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. In further examples such shapes may be associated with areas, endpoints, trajectories and/or sub-models. It is to be understood that 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. Further, the system may use dissatisfaction, concern and/or stress factors in order to assess the fitting of posts within various areas.
  • 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, hypemymy, hyponymy, meronymy, homonymy.
  • 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. (“running”, “runnin”); (“o’leary”, “oleary”, “o leary”) etc.).
  • 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.
  • 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.
  • artifacts e.g. “cat” and “c.a.t”
  • semantic identities e.g. semantic identities
  • 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.
  • 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 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. Further, 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. By using semantic intervals and adaptive semantics for inference a semantic system achieves predictive semantics.
  • a checkpoint for foreign nationals is timed based on the transitions in the semantic network model.
  • at one checkpoint gate it may take a foreign national from country A (Fa) 1 min to be cleared by an officer and a foreign national from country B (Fb) 2 min.
  • Fa foreign national from country A
  • Fb foreign national from country B
  • every time when the systems infers, potentially based on semantic interval contexts (e.g. arrival of a flight and arrival at the checkpoint), that there are foreign nationals from country B at the checkpoint it may index the waiting time accordingly. While the previous time indexing has been based on a single attribute (citizenship), other attributes or categories can be used for indexing the time (e.g.
  • 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.) ⁇
  • 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 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.
  • 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
  • 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. derive the number of people based on the transitions in the semantic network model), for speed of processing (traveler rate in an interval of time), to derive general or personalized sentiment inferences (e.g. VERY FAST, FAST, SLOW), for traveler semantic routing, experience rating, personalization and so forth.
  • type of service e.g. CITIZENS AND PERMANENT RESIDENTS mapped to transition links from the checkpoint inbound to checkpoint outbound
  • counting e.g. derive the number of people based on the transitions in the semantic network model
  • speed of processing traveler rate in an interval of time
  • general or personalized sentiment inferences e.g. VERY FAST, FAST, SLOW
  • 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 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.
  • 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).
  • 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.
  • these deployments are straightforward in control areas and boarding sterile corridors where the flow is guided through lanes and corridors thus allowing for less shadowing and multipath effects.
  • the counting in these areas can be very precise by instrumenting the lanes and/or corridors with smart posts or other sensing artifacts. For example, in a checkpoint lane 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 optimizing power consumption. Also, the relevant detection happens in near field for both optical and microwave and as such the data interpretation would be straightforward. Further, semantic system’s 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.
  • Figs. 8A and 8B 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.
  • Fig. 8A 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.
  • 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.
  • Fig. 8C 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.
  • 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.
  • 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.
  • 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 1, 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. It is to be understood that the grouping of smart posts in various topology, processing and cloud configurations may be based on semantic grouping based on semantic inference on inputs, outputs, sensing etc.
  • 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 with the plurality of distributed devices.
  • 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.
  • 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.
  • 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 initialization of operations may take place based on semantics inferred at any inference capable post.
  • the posts in the high-risk areas 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.
  • 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.
  • 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).
  • 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.
  • compositional semantics e.g. STAND POST UNIT
  • other sematic group hierarchies formed based on semantic composition.
  • the previous exemplified semantics, semantic groups and/or semantic routes may be evaluated and/or inferred by the system on a linguistic relationship basis including semantic shift, entailment, synonymy, antonymy, hypemymy, hyponymy, meronymy, holonomy, polysemy.
  • a HAZARDOUS semantic 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. lower weights) using antonyms and/or related semantic groups (e.g. SAFE).
  • 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, hypemymy, 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.
  • semantic identification of such paragraphs e.g. attributing a news article to its author, newspaper, group etc.
  • semantic profiling and factorization at any level of semantic identification.
  • the 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.
  • inferred factorized 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).
  • semantic model which represents a set of rules, patterns and templates used by a semantic system for semantic inference.
  • 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.
  • 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.
  • 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. SPI, I2C, I/O circuits, buses, analog and/or digital blocks and components, diodes, varactors, transistors etc.), CAN, wireless interfaces, optical interfaces and fibers and so on. Additionally, or alternatively, semantic fluxes connect via semantic sensing units comprising semantic controlled components, including those previously enumerated and others enumerated within this application.
  • interconnect technologies comprising protocols, on-chip/board and off-chip/board interconnects (e.g. SPI, I2C, I/O circuits, buses, analog and/or digital blocks and components, diodes, varactors, transistors etc.), CAN, wireless interfaces, optical interfaces and fibers and so on. Additionally, or alternatively, semantic 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.
  • 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.
  • change of data e.g. via analyzing a rendering, bitmap, user interface control/artifact, window, memory buffer analysis, programming interface, semantic inference etc.
  • the system infers and identifies display semantics artifacts (e.g. of an airport app window, messaging app, geographic information system window, input/output control etc.), activations, locations and a further semantics based on I/O data (e.g. touch/mouse click) on the window and the system maps and creates semantic artifacts (e.g. models, trails, routes etc.) from such inference.
  • display semantics artifacts e.g. of an airport app window, messaging app, geographic information system window, input/output control etc.
  • I/O data e.g. touch/mouse click
  • semantic artifacts e.g. models, trails, routes etc.
  • the mapping may be hierarchical, relative to the activated artifacts in a composable manner.
  • the mapping may be absolute to the display surface whether composed or not (e.g. comprising multiple display artifacts and/or sub-models).
  • time may be represented sometimes as a semantic time or interval where the time boundaries, limits and/or thresholds include semantic 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. a semantic of VERY CLOSE BY might infer a semantic of SUDDEN or SHORT TIME with potentially corresponding factors).
  • a semantic system is able to model the space-time-semantic continuum through semantic inference and semantic analysis.
  • the 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.
  • 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.).
  • 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.).
  • the system maps semantic network models to objects artifacts and so on and performs further inference in the semantic field.
  • 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.).
  • 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.
  • the semantic expiration may control the level of superposition (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 leams 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. simple localized and/or composite localized) semantic models and groups.
  • 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. Thus, when referring to a semantic group it should be understood that it may refer to its leader or leaders as well. In some examples, 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.
  • 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.
  • 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 blocks/gates some sounds and/or factorizes others based on the perceived peripherality and/or importance (e.g. based on 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.
  • 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 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. based on low entanglement entropy) the 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 apply ing/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.
  • 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.
  • semantic groups e.g. of endpoints, components etc.
  • 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.) ⁇
  • the 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. Therefore, in some instances, the semantic inference on the second semantic system may be biased based on the first system semantic interpretation.
  • 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.
  • 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. weight) to the ingested information; the assigned factor may be assigned to fluxes/streams and/or semantics in a flux/stream.
  • a semantic factor e.g. weight
  • 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 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 comprise or be associated with semantic artifacts, semantic budgets, rewards, ratings, costs, risks or any other semantic factor.
  • semantic artifacts e.g. semantic and/or semantic group
  • 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. Such hierarchical structure may be recursive.
  • the semantic rules may be grouped in semantic groups and participate in semantic inference.
  • the semantic rules may be classified as hard or soft.
  • 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 perform semantic orientation, routing and/or gating in order to achieve the publicity and/or influencing goals. It is to be understood that 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.
  • NURSE negative behavior towards
  • JANE negative factors/ratings on
  • the representation of semantic groups may include semantic factors assigned to each group member.
  • 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. In some cases, only the leader semantics are composed.
  • semantic timing and decaying the system may infer new semantic rules (e.g. semantic time rules).
  • 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.
  • 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 ( (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.
  • PRONOUN (1 ST PERSON, ALL GENDERS, “I”), (2 ND PERSON, ALL GENDERS, “YOU”), (3 RD PERSON, MALE, “HE”), (3 RD PERSON, FEMALE, “SHE”)
  • ALL GENDERS ALL GENDERS
  • ALL GENDERS ALL GENDERS
  • 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. null value or 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, 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.
  • 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.
  • a semantic e.g. semantic identity
  • 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. Alternatively, or in addition, 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.
  • an indicator is inferred based on goals.
  • multiple indicators are determined for a particular goal inference.
  • 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.
  • the system may perform recursive inference which is controlled by factor rules, decaying and other semantic techniques.
  • 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. In some examples 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.).
  • routes and paths e.g. based on rewards, goals etc.
  • the system may collapse the semantic artifacts, link and/or factorize them.
  • the system may cache such routes and/or map them at lower or higher level depending on factorization and/or theme.
  • DO and DO NOT semantic artifacts may be associated with time management rules (e.g. it may be allowed to DO a BATTERY DISPOSAL in a HAZARDOUS RECYCLING circumstance while in all other circumstances the DO NOT artifacts apply).
  • the system may adjusts and/or invalidate rules, routes and/or further artifacts which may activate gating based on such rule. If the gating is a block/deny rule the system may decay such artifacts. If the gating is based and/or controlled on interval factor thresholding the system may adjust the semantic rules.
  • 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.
  • the 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).
  • 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.
  • 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).
  • the system may choose to be more pioneering to inferences as they occur and follow semantic trails less closely.
  • 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.
  • Such budgets and/or factors may determine time management and/or indexing rules.
  • the system infers/leams 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.).
  • risk semantic factors
  • 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.
  • 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). Further, authoritative rules may affect such factors as they may determine high consequential risk and/or fear factors.
  • the semantic route may be represented as a semantic artifact (e.g. semantic, semantic group) and participate in semantic analysis and semantic modeling.
  • a semantic artifact e.g. semantic, semantic group
  • 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.
  • 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. endpoints and/or links associated with the semantic route to a lesser number and/or to higher level artifacts).
  • semantic model artifacts e.g. endpoints and/or links associated with the semantic route to a lesser number and/or to higher level artifacts.
  • 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.
  • 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.
  • the threshold frequency is associated with the minimum electromagnetic frequency generating photoelectrons emissions (e.g. by photoelectric effect). It is understood that by tuning the composite, absorptive, dispersive, diffusive and/or semantic artifacts of (nano) meshes the threshold frequency at a location may be tuned and thus allowing fast hyperspectral semantic sensing.
  • 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. [00348] 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 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 and/or other fluxes (e.g. such those initiating/challenging the goal of JUMP THE FENCE and/or consequences) for additional information (e.g. which EDEN?).
  • the system may decay and/or invalidate the semantic artifacts (e.g. routes , rules etc.) which generated confusion.
  • the confusion is low the system may factorize such artifacts.
  • 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.
  • 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 system when the system understands more than a language and symbology, 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 artifacts for each language for optimization (e.g. both languages are used often and the semantic factors for both languages are high) and model artifacts are linked and/or duplicated.
  • the system has a semantic group of associated to CAR comprising GERMAN AUTO, SPANISH COCHE, FRENCH VOITURE.
  • GERMAN GERMAN AUTO
  • SPANISH COCHE SPANISH COCHE
  • FRENCH VOITURE a semantic group of associated to CAR
  • 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) 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).
  • 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 linguistic relations including semantic shift, entailment, synonymy, antonymy, hypemymy, 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 poly seme 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. More so, 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.
  • the system A and B can have one common semantic point where the systems can 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 leam from the ingested data from system A; further, the system B may send his interpretation (e.g.
  • 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. In other instances, the system B will ask/challenge the system A about what the meaning of a signal is. In some cases, 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.
  • decoherence e.g. high incoherence, confusion, dissatisfaction, concern and/or stress between the members of the group
  • Semantic groups resonance determines and/or is associated with low confusion, dissatisfaction, concern and/or stress factors.
  • 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. of resonance, decoherence) comprising semantic routes/trails, rules and/or other semantic artifacts.
  • 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.
  • 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.).
  • 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 artifacts associated with system B e.g. (portions of) semantic trails, routes, rules, drives, goals and/or orientations etc.
  • 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.
  • 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 access control and gating In an example particular semantics and/or associated semantic artifacts are blocked.
  • 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. If the semantic inference doesn’t meet required budgets, then 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.
  • 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.
  • the interfaces may be statically coupled, and the operations and/or functions established a-priori and/or they may be encoded/explained in a dynamic way in the JSON objects (e.g. one field explains another through semantic means such as semantic augmentation, synonym and/or antonym. These interfaces are not very adaptive due to semi rigid implementation of the coupling between the systems.
  • 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 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.
  • 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) 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.
  • Fig. 16 and 20 we multiple elements (e.g. semantic units) coupled through 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., LI through L19.
  • 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/di splay capture, radio frequency, electromagnetic modalities and/or other modalities/techniques.
  • Information flows within and/or between semantic network model artifacts are controlled based on semantic gating.
  • 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 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.
  • the system performs projected compositional semantic analysis on the semantics assigned to the linked artifacts and based on the projected analysis perform the semantic gating.
  • 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. touch interface, screen, pointing device etc.); further, in some examples the system provides feedback on such operations (e.g. deny the operation, inform the user, pop up an image control and so on).
  • 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.
  • 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).
  • causality relationship may comprise additional information at a (hierarchical) level associated with the two entities (e.g.
  • 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 ? - because A is a 80% bully and B is a 70% little 20% flimsy slacker; why is A a bully ? - because it infected B and C and D and I 100% think is wrong).
  • 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. an observed object, high factorized artifacts, semantic groups, semantic identities, themes of interest etc.).
  • the anchoring may be based on a collection of artifacts and the system uses projected inference and semantic analysis based on such anchors.
  • 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.
  • 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.
  • the system may use specific hierarchical levels to represent semantic groups of specific and/or leader semantic artifacts.
  • the system may activate various hierarchical levels in the semantic network model based on semantic analysis, drive and leadership semantics.
  • 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 interprets an input (e.g. semantic) from a particular flux as being questionable maybe because it doesn’t fit the semantic inference and/or theme of the semantic flux, 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).
  • 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 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. It is to be understood that instead of challenging or asking for feedback about a particular cyber-physical system alternatively, or in addition, it may ask for feedback about a semantic and/or semantic group associated with the cyber physical system.
  • 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.
  • the system is able to use semantic conditioning for a large variety of purposes including inference in a semantic mesh.
  • 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 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
  • 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.
  • 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. While some depicted examples use variable voltages for modulating semantic signals it is to be understood that alternatively, or in addition, variable currents values may be used to modulate such signals and/or represent semantic artifacts. [00421] It is to be understood that such 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 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.
  • 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.).
  • a sensed semantic e.g. ingested via optical or sound sensing entities
  • 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.
  • a sequence of semantic units such as SU1, SU2
  • the system may modulate the semantic wave with a composite signature (e.g. DNASEQSU1 -Level 1 DNASEQSU2-Levell) of those units, which, when routed through SU3 is identified and collapsed into a further composite signature (DNASEQ3-Level2) which allow the unit SU3 to modulate and gate the semantic wave based on the new composite signature.
  • a composite signature e.g. DNASEQSU1 -Level 1 DNASEQSU2-Levell
  • DNASEQ3-Level2 further composite signature
  • the unit SU3 is a border semantic unit between multiple semantic stages and/or hierarchical levels (e.g.
  • Level 1 and Level2 and/or semantic stages and thus the collapsed signature may be available, collapsible or inferred only at Level2 and/or beyond but not at Level 1.
  • the previous example uses a limited number of units and signatures it is to be understood that this may expand to a more complex semantic structure including more units, multiple hierarchical levels, semantic groups (e.g. of units, endpoints, sub-models and/or signatures etc.).
  • the term “signature” has been used it is to be understood that the term may refer to DNA sequences, semantic artifacts, identification etc.
  • 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. based on vision sensors, 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.
  • medical imaging sensors e.g. based on vision sensors, modalities such as CT (computed tomography), MRI (magnetic resonance imaging), NM (nuclear medicine), US (ultrasound) etc.
  • 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.).
  • the system may perform memory, semantic model and/or semantic units access control, gating, factorization, decaying, enablement, disablement, invalidation, expiration, pruning in order to isolate the use of semantic artifacts at various hierarchical levels.
  • 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.
  • 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.
  • 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.
  • 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. While 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. Thus, 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 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.
  • 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. In a cascading semantic wave, models and rules are generated based on recursive semantic analysis on semantic waves, models and rules and used for further generation of semantic waves. In some examples, at least two semantic waves are composed while the waves are modulated based on the cascading learning. In some examples 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 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.
  • 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.).
  • the 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.
  • mediums and interfaces e.g. network
  • 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.
  • 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.
  • 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.
  • semantic route 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 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 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. Further, 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. sound, tactile, pressure, radio frequency, piezo, capacitive, inductive, analog, digital, semantic flux, semantic stream and other signal modalities).
  • sensing types and modalities e.g. 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.
  • the system selects drive semantic artifacts and routes associated with synonyms belonging to groups where the drive semantic is a leader.
  • 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.
  • the semantic routes themselves may be grouped.
  • Semantic routing may comprise semantic orientation and profiling for a semantic trail.
  • semantic routing is intrinsically connected to semantic orientation in semantic analysis; as such, when mentioning either one is to be understood that the other one may be implicitly involved. 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 may be used to asses if short term planning (e.g. comprising sub-goals time management rules) and/or execution matches long term (strategic) planning (e.g. comprising high-level and/or composite goals time management rules). While the shorter-term (e.g. fast decaying) goals may incur larger drifts in relation with the strategic goals (e.g. based on factorizations and/or budgeting) the longer term artifacts (e.g. slower decaying, higher level artifacts) may incur smaller goal drifts.
  • short term planning e.g. comprising sub-goals time management rules
  • lategic planning e.g. comprising high-level and/or composite goals time management rules
  • the shorter-term (e.g. fast decaying) goals may incur larger drifts in relation with the strategic goals (e.g. based on factorizations and/or budgeting)
  • the longer term artifacts e.g. slower decaying, higher
  • 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.
  • the system may infer alternate projections and/or sub-goals; alternatively, or in addition, it may adjust, decay and/or invalidate the (strategic) goal.
  • 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 and/or further semantic artifacts used in projections.
  • Competing requirements e.g. associated with various semantic profiles
  • for short-term and/or long-term planning may determine elevated drifts and/or confusion factors which may be decayed by further budgeting, flux challenges, semantic profiling, hierarchical and/or gated inference of factors and/or indicators and further semantic analysis.
  • the system may perform deep learning feature recognition (e.g. based on CNN, RNN, LSTM) on the semantic shape and fuse the features and attributes detected within the sematic inference.
  • deep learning feature recognition e.g. based on CNN, RNN, LSTM
  • 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.
  • 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. REMOTE CONTROL); thus, 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).
  • 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.
  • semantic models and semantic rules e.g. heat semantics mapped to endpoints, semantic time management
  • 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 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 leam patterns, semantic artifacts (e.g. indicators, routes, groups) of usual or unusual behavior pursued by malware.
  • semantic artifacts e.g. indicators, routes, groups
  • 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 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 contr oiling/ 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.).
  • 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.
  • 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 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.
  • 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 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.
  • 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.
  • 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. Additionally, 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.
  • 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. challenges) the semantics, semantics, theme and semantic factors with another system or component to see if is available and/or in what semantic budget (e.g. cost, semantic interval) will be; in some cases, parts of memory are bulk transferred between systems based on the semantics and themes of interest and access control rules.
  • semantic memory segments must stay unchanged while other segments may be updatable based on various conditions including access control rules.
  • the system may pursue additional semantic artifacts and/or routes based on the levels of coherence and/or confusion factors relative to interrupted semantic routes, goals, views and/or other semantic artifacts.
  • 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.
  • 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.
  • semantic identification command is used to identify a semantic group and the semantic group is configured with the optimized configuration.
  • 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 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.
  • inputs e.g. data, signal, waveform, value, pattern, etc.
  • semantic analysis e.g. data, signal, waveform, value, pattern, etc.
  • 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 hierarchy of memory is implemented in hardware, alternatively, or in addition, it may be virtualized thus abstracting hardware implementations.
  • the virtualization may be based and comply with semantic views connect and semantic gating requirements.
  • 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.
  • the signal e.g. semantic wave
  • routed and passing through such memory zones may be transformed and routed based on zones semantics.
  • 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. In some examples, 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.
  • 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. in rapport with competition products, budgets, price etc.).
  • 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. requestor) 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 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.
  • a semantic system e.g. requestor
  • 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.
  • 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.
  • the selection of collaborators can use similar techniques used for semantic grouping, semantic identification, semantic routing, semantic marking and/or inference.
  • 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.
  • 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. They can communicate via a cloud and/or mesh networking. Such 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. As such, 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 may have an authoritative leader based on the particular contexts of semantic inference and/or 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. While the previous example mentioned larger areas and/or higher-level endpoints (e.g. based on more abstract or transfer semantics) it is to be understood that similar techniques apply to more granular areas and lower level endpoints. As previously mentioned, such inference can be associated with any direction within the hierarchical structure.
  • the 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.
  • the system may decrease or increase the semantic factors associated to compositional semantics.
  • the system may update the semantic budgets associated with selected semantic routes.
  • a semantic time budget is updated based on the inferred semantic factor to reflect that the goal may be reached earlier or later than predicted.
  • the system uses the semantic chain and semantic analysis to update semantic artifacts.
  • the system uses the semantic chain and/or model of a semantic view and/or semantic view frame for optimization.
  • 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 from various domains; further collaborative semantic image interpretations from various systems ensure multi domain knowledge fusion.
  • the system might collect data from various fluxes and fusion, challenge (e.g. interrogate) and give priority and/or more weight to those fluxes which provide data from areas where is daytime, assuming that less cycling is usually done overnight and that the system has inferred strong factored semantic artifacts (e.g. composite semantics, semantic groups, semantic routes) based on semantic artifacts of cycling (e.g. cycling related semantics, semantic groups and semantic routes whether based on semantic relationships of cycling and further semantic analysis) with daytime (e.g. based on semantic time).
  • 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. As such, 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. Additionally, weights, ratings and semantic factors of fluxes based on those determinations may be also affected.
  • 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.
  • 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.
  • 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.
  • the system groups elements in the semantic model based on semantic analysis (e.g. composition).
  • semantic analysis e.g. composition
  • endpoints and/or links may be composed at any level of the hierarchy.
  • semantic analysis may be based on grouping of semantic model artifacts.
  • 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.
  • 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 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.
  • 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 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. Further, 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.
  • 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 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 leam 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.
  • 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).
  • 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
  • the 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.
  • 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.).
  • 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. Further 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 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. acceleration, spin, gravity) in a high velocity fashion and the efficiency is increased by inferring semantics closer to the sensor itself on a processing unit (e.g. microcontroller, semantic unit) on a chip.
  • the semantic engine on the chip might instruct the sensor to adjusts its settings based on the inferred semantics and/or received semantics via semantic fluxes.
  • 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.
  • 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. Further, 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. shaping and overlay learned models on renderings, graphics, images, frames and/or perform semantic analysis etc.).
  • User pointed traj ectories 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).
  • 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. It is to be understood that 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. In further examples, the overlays are rendered and/or mapped based on such profiles and/or templates. [00595] 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.).
  • system uses an additional orientation and/or drive semantics provided by user (e.g. using similar or different means via semantics, semantic routes etc.) together with the initial semantic trajectory in order to create semantic groups, routes and rules.
  • additional orientation and/or drive semantics provided by user (e.g. using similar or different means via semantics, semantic routes etc.) together with the initial semantic trajectory in order to create semantic groups, routes and rules.
  • 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.
  • the system may use semantic analysis and mapping to highlight, select, link and/or overlay display artifacts on narrative components.
  • a user may identify semantic group artifacts (e.g. via selecting it on a touch screen; selecting an area and/or trajectory with artifacts) and further associate semantic artifacts (e.g. semantics, semantic groups, semantic routes, links etc.) associated with the identified artifacts.
  • semantic artifacts e.g. semantics, semantic groups, semantic routes, links etc.
  • 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 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. label, control type, control content, color, font type and/or other assigned and/or inferred attributes) from selected and/or identified semantic group artifacts; 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.
  • information e.g. label, control type, control content, color, font and/or other assigned and/or inferred attributes
  • 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.
  • an image associated with a SMILEY (BEAR) FACE semantic identity is stored (e.g.
  • 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.
  • artifacts e.g. images, sounds etc.
  • 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.
  • 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 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.
  • 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. high visibility, non-peripheral frame/view etc.) or low augmentation factors (low- visibility, peripheral frame/view etc.) based on the factors associated with inferences.
  • the system is able to select, enable, render and/or update display labels, graphics and/or fields based on semantic analysis.
  • display labels, graphics and fields are associated with semantic artifacts whether gated or/not.
  • the system is able to perform inference based on the information on the display controls and the information of any linked semantic fluxes.
  • 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 combobox UI control based on the semantic identities).
  • 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.
  • the system may mask (e.g. overlay semantic network models, blur, change color etc.) leadership features of particular semantic identities at various hierarchical levels (e.g. mask features related with eyes, skin etc.) and thus only the particular semantic identities are allowed to pass.
  • 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.
  • the transformation from another language to the main representation language may resemble the transformation to and/or from “baseband” in a signal processing system.
  • Such transformation can use semantic analysis including semantic orientation and/or semantic drift.
  • 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. [00613]
  • 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. The information is routed to semantic units and/or semantic groups based on semantic analysis and semantic routing and via semantic gating. Semantic circuitry may be semantic gate driven and thus it can be referred as a hardware semantic gate.
  • semantic input and output e.g. semantic, semantic flux
  • 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.).
  • semantic artifacts e.g. semantic groups of subjects, devices, blocks etc.
  • a semantic group and/or semantic distributed ledger is 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. based on a pattern and/or amplitude and/or frequency and/or pulse and/or phase), potentially based on composition.
  • a semantic wave can be simple or composite; a semantic wave may comprise the semantic composition and potentially the identification of modules in 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). Alternatively, or in addition, they may be authenticated via 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.
  • 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.
  • 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.
  • the core semantic inference rules to which the models have been configured will drive the development of semantic models.
  • Coherent semantic inference allows a system (and/or semantic group) to reduce superposition via semantic analysis including composition and/or semantic collapse.
  • 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. semantic groups, routes etc.) based on coherent and/or incoherent inferences and/or collapse.
  • semantic artifacts e.g. semantic groups, routes etc.
  • Such artifacts may be used later for semantic analysis in a way that the system will prefer coherent artifacts when the superposition is high and/or the budgets are low or, use more incoherent artifacts when the superposition is low and/or budgets are high.
  • 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 leam 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.).
  • 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.
  • 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).
  • semantic time e.g. a spectrogram associated to semantics and/or themes, potentially in a semantic flux and/or wave, may be used.
  • 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. As such, 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 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.
  • the system may associate some intrinsic behaviors with semantic identities and/or semantic groups.
  • intrinsic behavior for (A/THE) SCREEN the intrinsic behavior for particular endpoints, locations and/or profiles is NOT TOUCHED and hence in order to avoid unnecessary inferences the system may decay, block/gate, dispose and/or expire intrinsic behaviors artifacts (e.g. routes) in association with semantic views.
  • 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.
  • the target flux system does not perform inference and may send a negative semantic for the request or, alternatively, the budget in which is realizable.
  • 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. In some examples the semantic indexing is applied on a semantic artifact or semantic drift tolerance, threshold or interval and the semantic indexing factor is calculated based on the semantic and/or route. [00648] The system relies on increasing noise to detection ratio on various semantic fluxes and semantic waves based on semantic analysis.
  • 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’. In the case that the ‘POLE LIGHT’ factor is high because a camera has detected the actual bulb feature then 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.
  • 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.).
  • 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, data exchange protocols etc.
  • the segmentation of computing platforms is important in obtaining secure computing systems.
  • the segmentation includes network segmentation, data segmentation, function segmentation and others. More often, in general computing systems the segmentation functionality is less flexible, however a semantic system could better understand the needs of segmentation at various levels and provide more flexible and secure approaches.
  • 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. As an example, if the semantic chain which needs to be analyzed for a semantic goal is large in comparison with a current semantic view then 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. Alternatively, or in addition, 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.
  • 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 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. In case that 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 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.
  • 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.
  • the system may process inputs on more fluxes/streams and/or chains based on the semantic model, semantic rules and semantic routes.
  • the system executes semantic inference based on the semantic model and rules at each unit; the rules and model may be learned and updated during semantic inference or at other semantic times. The learning and updating may be controlled through semantic gating.
  • the 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
  • 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. learn semantic routes and rules based on conditioning and deconditioning).
  • 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. It is to be understood that 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.
  • 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.
  • the trajectory analysis, semantic analysis and composition can occur and be associated with artifacts at any hierarchy level of the semantic model.
  • 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 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. based on a semantic route, whether a number of semantics in a route where used, or based on semantic factors and/or thresholds, potentially accumulated during inference) until a semantic budget is consumed or until a semantic mission or goal is achieved, potentially within a semantic budget.
  • 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.
  • 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. In an example they may be coupled for achieving goals and/or inferences.
  • 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.).
  • Composable models allow the linking, connection and/or composition of semantic artifacts (e.g. endpoints) based on semantic analysis.
  • 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.
  • semantic artifacts e.g. semantics, semantics at endpoints, links, orientation, trajectory etc.
  • 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.
  • the coupling, linking and composition of semantics artifacts is based on semantic goal inference on the composable artifacts.
  • 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 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 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.
  • 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.
  • These 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.
  • 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 for artifacts which obscure and/or mask the semantic identity of YELLOW CAR.
  • 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. fluxes, sensors) assigned to such areas; it is to be understood that such challenges and/or interrogations might trigger semantic inferences based on the challenging semantic artifacts and/or identities (e.g. such as YELLOW CAR, PARKING LOT A etc.). Further, for fast searching the system identifies YELLOW as a leadership semantic and as such parses the frames for the YELLOW color.
  • semantic identities e.g. GIVE ME ALL YELLOW CARS WITHIN PARKING LOT A IN THE LAST HOUR
  • the system parses the frames for CAR semantic and creates the set of frames having both semantics and further assessing whether the color YELLOW corresponds to CAR based on semantic analysis.
  • the system uses semantic analysis to restrict the artifacts associated with time budgets (e.g. frames WITHIN THE LAST HOUR), semantic identities and/or fluxes (e.g. associated with PARKING and/or more specifically PARKING LOT A).
  • 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. license plate, semantic scene associated frame, (mapped) semantic artifacts etc.); it is to be understood that the 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).
  • the system leams the goal based on further explanation - e.g. GIVE ME ALL YELLOW CARS BECAUSE I AM LOOKING FOR THE FANCIEST ONE or GIVE ME THE FANCIEST YELLOW CARS - and as such the system may gate, sort, display, augment all the YELLOW CARS artifacts based on the leadership goal of BEING FANCY for more general profiles or BEING FANCY for ⁇ particular semantic profiles>.
  • 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 providing users with the required frames, video snippets, semantic artifacts and/or semantic groups thereof (whether group dependent and/or group independent).
  • 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 may restrict and/or mute (embedded) advertisings and/or artifacts which determine high confusion, incoherency, low resonance and/or are not allowed (e.g. for particular users, groups, profile etc.).
  • 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.
  • it 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.
  • such techniques and/or 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
  • GIVE may entail other modalities such as sound, tactile, wearable feedback, vibration etc.
  • the challenge may specify a semantic identity (e.g. ME) and as such 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.
  • a semantic identity e.g. ME
  • specific semantic identities may be provided, alternatively, or in addition, the system may infer semantic identities based on circumstances and/or semantic analysis.
  • 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 thereol) if the access control rule, publishing, capabilities and/or gating allows. In some examples, in particular circumstances as inferred during semantic analysis, a device and/or semantic group may be associated with allowing all and/or particular (e.g.
  • semantic augmentation capabilities based on publishing, budgets, factors, enablement, diffusion etc.
  • semantic augmentation 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. paragraphs, documents, images, signals, waves etc.). Further content metadata may be used by semantic inference.
  • the system communicatively couples at least two artifacts such as posts, devices, components, modules, units, fluxes, UI controls, video Tenderers and/or further artifacts based on semantic inference and/or routing.
  • artifacts such as posts, devices, components, modules, units, fluxes, UI controls, video Tenderers 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.
  • 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. high risk factors) or none at all (e.g. because OFF DUTY is negative and/or have high entropy in rapport to ON DUTY).
  • 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.
  • semantic based virtualization e.g. dynamically allocate resources on a virtualization platform based on semantic inference
  • 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 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. atomic, electronic, chemical, molecular, photon, plasma, surface etc.), quantum tunneling and/or gating in the semantic network model and mapped artifacts (e.g. sensors, devices, components, gratings, meshes and/or crystals).
  • 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. display, I/O, semantic fluxes, semantic unit, memory, computer etc.) with GIVE ME IN THE NEXT 10 MINS THE HAZARDOUS LOCATIONS THAT I CAN BEAR and thus based on semantic diffusive analysis and further semantic analysis of challenger circumstances and locations performs semantic augmentation; it is to be understood that sub-goals such as BEAR, TOLERATE, ENDURE, ACCEPT, ALLOW may be based on an accepted reward to risk factor in rapport with the composite goal.
  • 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, 3 A, 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 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. [00715] In some embodiments 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.
  • 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. potentially through a semantic group in which the factor and/or the goal is defining the group or has leadership status in a group).
  • 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.
  • 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. an object of a certain semantic group/s behaving in a certain way into the future, future inference of particular semantic artifacts for the object etc.); if the projections are met then the system may further increase the 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 anew 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.
  • a factor e.g. for weighting, risk etc.
  • 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. based on drift, goal and/or projected inference) between the semantic identity of the newly created group and the semantic identity of the semantic groups used in the initial projections.
  • the system updates the semantic groups (e.g. with the newly inferred semantics and/or groups) of semantic artifacts used in the original projection and potentially further factorize them; such updates may happen if the system is unable to differentiate between the semantic identities of the semantic groups.
  • 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. Analogously, 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).
  • the system may prioritize the semantic goals via indicators and factors and form pluralities of semantic groups and pursuing those in semantic analysis/inference.
  • 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 other factors may be received and inferred from any data and input including user, sensing entity, internal, external etc.
  • 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.
  • 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. inference of HAD INCOMING 10000 SPRINT BLACK SHOES SIZE 10 and inference of EXPEDITED 9900 SPRINT BLACK SHOES SIZE 10) and further infer a composite sematic of UNDER 100 PAIRS and further DELIVER 10000 PAIRS FOR the required size (e.g. SPRINT BLACK SHOES SIZE 10) for matching the goals.
  • the goal inference may be based on semantic artifacts (e.g. semantic routes, semantic views) whether hierarchical or not (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.
  • 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. within the semantic group itself, by the semantic group itself, by the leader, by other semantic groups etc.) and thus transmitted within the semantic infrastructure where further adaptations (e.g. of goals), negotiations, disablement, invalidation, rating, factorization based on semantic analysis and feedback may be inferred.
  • 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.
  • the system may perform and/or guide the semantic analysis based on or of the loss (e.g. dissociation, un-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) 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.
  • 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.
  • 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.
  • imaging sensors or imaging streams e.g. video, images
  • it may map a semantic network model of endpoints and/or links to artifact locations as detected from image and/or frame data.
  • 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 semantics, semantic time and/or semantic indexing. It is to be understood that 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. endpoints and/or links) the semantic models of the data renderings and further to infer the semantic of shapes modifications, motions and boundaries (see picture). For example, if the semantic scene is represented by an observed object, the system maps the semantic model to areas or locations in a frame/image/capture and/or data rendering of the object.
  • points e.g. endpoints and/or links
  • 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. While multiple models may be used, 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 detection e.g. electromagnetic scattering/reflection sensors, time of flight camera, depth camera, laser, radio frequency sensors
  • location-based endpoints when referring to 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. between a reset state of the detector and charging to a particular energy state and/or a threshold of photon counts and/or energy quanta count).
  • 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. via composition) at a second level.
  • the transitions may be related with risks, targets, costs and other semantic factors and goal indicators.
  • 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.
  • groups of sensors and/or endpoints are grouped as semantic groups and the inferences composed in a hierarchical manner.
  • 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).
  • the 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.
  • endpoints e.g. sensing elements or group identification
  • 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 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.
  • the system leams 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.
  • 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 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. In some examples the causality may be modeled as semantic routes, endpoints, links (e.g. oriented) and/or other semantic artifacts.
  • 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.
  • 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.
  • semantic elements/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 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 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. Also, the access between various views and/or profiles is based on semantic gating and access control.
  • Semantic profiles may be associated and/or based on semantic groups.
  • 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.
  • the semantic profile may be assigned or associated to semantic identities and/or user preferences.
  • the system may leam and/or infer sentiments based on semantic profiles and/or semantic groups.
  • 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 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.
  • 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 semantic groups and so on. It is to be understood that such publishing, control, profiling and sharing may be analyzed, encrypted, unencrypted and/or authenticated in a hierarchical manner based on corresponding credentials.
  • Semantic inference produces semantic artifacts.
  • the 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.
  • the system uses 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.
  • elements and artifacts in the scene, image, frames, maps or renderings e.g. pixels, area, locations, sub scenes, sub-frames, objects
  • renderings e.g. pixels, area, locations, sub scenes, sub-frames, objects
  • 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.
  • the internal representation e.g. images, groups, models etc.
  • the system uses semantic groups of elements to capture information and perform inference (e.g. associating an object with a semantic group of elements and/or identifiers), then 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 reconstruction mechanism of an observation may be altered or become even completely unfeasible. If the semantic artifact has been related, replaced, collapsed and/or fused with/within other semantic artifacts then the reconstruction may take place using those semantic artifacts.
  • 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. 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.
  • 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.
  • the system filters noisy signals from a semantically identified artifact in the semantic scene (e.g. filter sounds and/or other signals from a bird detected and/or mapped via optical and/or other sensing means) and mapped to an endpoint; thus 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.).
  • optical mapping and rendering may use differences in appearance between semantic model artifacts and/or semantic groups to interpret or render the scene.
  • 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.
  • the system may use other gradient mapping to semantic network models.
  • 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 the inference of only the differences between/within semantic scenes. As such, 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.
  • optical mapping and rendering 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 model as links and/or endpoints.
  • a processed gradient image (e.g. based on convolution and/or filtering) and/or frame is mapped to the semantic network model. It is to be understood that such 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.
  • 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 sensor element grid areas, zones the system performs mapping on any detected and/or rendered scene and artifacts (e.g. pixels, areas, zones, sub-scenes, objects etc.).
  • scene and artifacts e.g. pixels, areas, zones, sub-scenes, objects etc.
  • 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 model that don’t have associated semantics and/or the semantics expired and/or they don’t comply with a goal-based inference. It is to be understood that 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. In further examples, the system uses semantic analysis on the goals, indicators and budgets to allow or disallow artifacts in the view frame. In further examples the system disallows some inferred artifacts and/or provide rule updates, ask for feedback and/or generate alerts.
  • 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 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. mapping on envelopes, spectrograms, gradients etc.).
  • the system may use transforms such as Fourier transforms, wavelet transforms, passbands, window functions to perform semantic inference on the resulting signal artifacts.
  • 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 collapse s 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.
  • the semantic time management (e.g. validity) in a semantic view frame and/or view may be also associated with various profiles based on the user entity of the view frame and/or view.
  • a 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.
  • the system perform marking, enablement, and/or selection of semantic artifacts in the semantic memory.
  • Memory capacitive elements and components associated with the semantics (including marked semantics) and/or factors are charged to a particular voltage and then discharged in time via variable resistance coupling or varactors based on semantic analysis and time management.
  • a 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.
  • the 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.
  • 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.
  • 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.
  • 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.
  • semantic comparison e.g. semantic drift, drive semantic decaying, semantic collapse
  • 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.
  • the shape comparison may comprise semantic orientation.
  • 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 candidate semantic groups are compared against the drive semantic artifact based on semantic orientation, leadership and/or semantic drift.
  • a drive or reference semantic or semantic group e.g. represented as a semantic network model
  • semantic route orientation the system may perform semantic route orientation.
  • 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. In an example 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 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. CLIMBING related).
  • the system may ask for feedback from a user or through semantic fluxes and adjust the rating and/or factors for such rules and potentially validate or invalidate it.
  • 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. from optical/RF receive/backscatter. camera/optical sensor/vision sensor
  • semantic flux data can be used by a vehicle semantic unit to determine that a group of pedestrians are traversing the street at locations in front of the car.
  • the unit Based on various telematics, environmental, capabilities parameters and current view the unit issues actuation to braking, steering, suspension, electric system based on semantic budgets, semantic fluxes from other participants, semantic analysis.
  • 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.
  • the 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.
  • 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 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.).
  • the observer e.g. car, sensor, person etc.
  • 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. While the example provided has been using a BRAKING assessment for achieving the allowed trajectory 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.
  • 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.
  • the semantic is a composite specifying a semantic group and/or chain of actuation (e g. ADJUST ACTUATOR VALVE OF BRAKES FRONT).
  • the addressability is not present explicitly, case in which the system infers the address based on the registered, inferred and determined semantics.
  • 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.
  • semantics e.g. PERFORM COVERAGE ANALYSIS UP TO DISK USAGE OF 50%
  • semantic time management e.g. DISK USAGE HIGHER THAN 50%
  • 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%.
  • 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 leam 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. Further 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. In another example, 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 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.
  • scripts e.g. HTML
  • templates e.g. XSLT
  • programming languages e.g. HTML
  • 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 rendering medium e.g. display, memory, buffer, graphic interface etc.
  • the semantic unit rendering semantics are determined via semantic analysis.
  • 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. text areas, labels, textboxes, listboxes etc.) and uses semantic fluxes and semantic gating 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 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.
  • 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.). Alternatively, or in addition to pointing and/or touch sensing 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.).
  • rendered or display objects e.g. a RED CAR, a MEDICAL CHART 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 MEDICATION WAREHOUSE (area) may be associated with a more general, less restrictive route of SELECT MEDICATION, (ALLOW DISPENSE), (DISPENSE ALLOWED).
  • semantic SELECT MEDICATION, (ENTER) MOTIVE 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. MOUSE CLICK, MOVE RIGHT TO FIELD AREA may be automated and/or allowed for some profiles while (MOVE RIGHT) (TO FIELD AREA) may be denied for some profiles).
  • 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. a composite flux/control diffuses and/or propagates to composition fluxes/controls, a composition flux/control diffuses and/or propagates to a composite flux/control and so on).
  • 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.
  • semantic artifacts and semantic analysis on such artifacts 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/pattems 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.
  • the engine assigns semantics associated with inputs and signal noise whether discrete or analog. Further, when the system encounters the signal/data/pattems 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 leam semantics, groupings and relationships between them. Further, the system may leam 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.
  • factors e.g. cost, risk or other indicators
  • 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.
  • 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.
  • 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/sy stems; 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.
  • 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 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.
  • 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. Alternatively, or in addition, they 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. Further, 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.
  • the factors may be based on inferred semantics and/or time management rules which may contain semantic time intervals.
  • the factors of the 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. For example, 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 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. In an example, the semantic engine determines that a semantic route exceeds a semantic budget and has high costs/risk while has little rewards (e.g.
  • 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. It is to be understood that such expectations may be corroborated and/or based on sensing at the particular locations (e.g. by vision, touch, sound, vibration, temperature etc.).
  • 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 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 proj ected 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.
  • 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 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 proj ected 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. [00903] The system may use semantic factors or principles of operation (e.g.

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Abstract

Un système de détection comprend un capteur avec un système informatique et une mémoire en communication avec le système informatique, la mémoire stockant une pluralité de points d'extrémité. Le système informatique est conçu pour recevoir des préférences d'activité d'un dispositif au niveau d'un point d'extrémité et détermine en outre la probabilité d'activités au niveau du point d'extrémité. En outre, il reçoit des préférences d'identification sémantiques du dispositif en communication avec le système informatique et le système abandonne les identités sémantiques correspondantes sur la base des préférences reçues.
EP20938858.6A 2020-06-03 2020-11-03 Système de fusion d'environnement physique-virtuel Pending EP4162766A4 (fr)

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US16/891,893 US20200298401A1 (en) 2019-01-03 2020-06-03 Semantic Robotic System
US16/929,680 US20200356951A1 (en) 2019-01-03 2020-07-15 Robotic Devices
US16/999,691 US11562565B2 (en) 2019-01-03 2020-09-08 System for physical-virtual environment fusion
US17/064,198 US11604832B2 (en) 2019-01-03 2020-10-06 System for physical-virtual environment fusion
US17/076,979 US11669913B2 (en) 2019-01-03 2020-10-22 System for physical-virtual environment fusion
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