Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • 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
  • G06Q10/00—Administration; Management
  • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
  • G06Q10/063—Operations research, analysis or management
  • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals

Definitions

• the present invention is in the field of use of computer systems in business information management, operations and predictive planning. Specifically, the assembly of a system that integrates the functions of business information and operating data, complex data analysis and use of that data, preprogrammed commands and parameters and machine learning to create a business operating system capable of reliable, predictive simulation of large, complex systems herein as applied to business venture outcome and the calculated risk to capital of predictive pathways.
• PLANATIRTM offers software to isolate patterns in large volumes of data
• DATABRICKSTM offers custom analytics services
• ANAPLANTM offers financial impact calculation services and there are other software sources that mitigate some aspect of business data relevancy
• the inventor has developed a distributed system for accurate and detailed modeling of systems with large and complex datasets using a distributed simulation engine.
• the system further uses results of business information analytics to optimize the making of business decisions and allow for alternate action pathways to be simulated using the latest data and machine mediated prediction algorithms.
• portions of the system are applied to the areas reliably predicting the outcomes of differential business decision paths and prediction of risk to business value for each set of decision choices through simulation of the progression of each decision pathway using the most current sensor data, specific programmed decision defining parameters and business environment data available and then presenting that data in a format most useful to the authors of the simulation.
• a system for accurate and detailed modeling of systems with large and complex datasets using a distributed simulation engine comprising: a business data retrieval engine stored in a memory of and operating on a processor of a computing device; a business data analysis engine stored in a memory of and operating on a processor of a computing device; and an automated planning and value at risk estimation module stored in a memory of and operating on a processor of one of more computing devices.
• An action outcome simulation module stored in the memory of and operating on a processor of one or more computing devices, wherein, the business information retrieval engine: retrieves a plurality of business related data from a plurality of sources; accepts a plurality of analysis parameters and control commands directly from human interface devices or from one or more command and control storage devices, and stores accumulated retrieved information for processing by data analysis engine or predetermined data timeout.
• the business information analysis engine retrieves a plurality of data types from the business information retrieval engine, and performs a plurality of analytical functions and transformations on retrieved data based upon the specific goals and needs set forth in a current campaign by business process analysis authors.
• the automated planning and value at risk estimation module employs results of data analyses and transformations performed by the business information analysis engine, together with available supplemental data from a plurality of sources as well as any current campaign specific machine learning, commands and parameters from business process analysis authors to formulate current business planning and risk status reports and employs results of data analyses and
• the action outcome simulation module retrieves at least a portion of the results of data analyses and transformations performed by the business information analysis engine, retrieves at least one piece of raw data from the business information retrieval engine, employs a plurality of parameters entered from the automated planning and value at risk estimation module, uses information obtained to execute predictive simulations of business venture or business decision progress pathway and outcome as originally initialized by simulation author using a simulation method that combines system dynamics method, discrete event method, or agent based method for at least one simulation instance, employs groupings of action profile data and configuration parameters to create computer based models of real-world items to act in the simulation.
• a system for fully integrated collection of business impacting data, analysis of that data and generation of both analysis-driven business decisions and analysis-driven simulations of alternate candidate business decision comprising: a business data retrieval engine stored in a memory of and operating on a processor of a computing device, 2.
• the business information retrieval engine stored in the memory of and operating on a processor of a computing device employs a portal for human interface device input at least a portion of which are business related data and at least another portion of which are commands and parameters related to the conduct of a current business venture campaign alternatives.
• the automated planning and value at risk estimation module uses at least information theory based statistical analysis to reliably predict future outcome of current business decision based analyzed previous data.
• the automated planning and value at risk estimation module uses at least Monte Carlo heuristic model value at risk principles to reliably estimate future value at risk figures of current business decision based analyzed previous data.
• the automated planning and value at risk estimation module uses a specifically designed graph-based data store service to efficiently store and manipulate the large data structures created during business decision outcome analysis.
• the automated planning and value at risk estimation module has job control function that allows both jobs that run in a single iteration with a single set of parameters and jobs that include multiple iterations and sets of predetermined sets of parameters with termination criteria to stop execution when desired analysis results are obtained. Some jobs are run offline in a batch like mode and other jobs are run online in an interactive mode where users enter parameters for subsequent iterations based upon results of previous iterations until a predesigned analysis result terminates execution. At least one simulation includes models for hazards, vulnerabilities, contractual obligations and financial capital loss.
• the automated planning and value at risk estimation module acts upon at least one computer based model to modify it prior to the simulation.
• a system for fully integrated collection of business impacting data, analysis of that data and generation of both analysis-driven business decisions and analysis-driven simulations of alternate candidate business decision comprising: A business information retrieval engine stored in the memory of and operating on a processor of a computing device, employs a portal for human interface device input at least a portion of which are business related data and at least another portion of which are commands and parameters related to the conduct of a current business venture campaign alternatives.
• An automated planning and value at risk estimation module uses a least information theory-based statistical analysis to reliably predict future outcome of current business decision based analyzed previous data.
• An automated planning and value at risk estimation module uses at least Monte Carlo heuristic model value at risk principles to reliably estimate future value at risk figures of current business decision based analyzed previous data.
• An automated planning and value at risk estimation module uses a specifically designed graph-based data store service to efficiently store and manipulate the large data structures created during business decision outcome analysis.
• An automated planning and value at risk estimation module has job control function that allows both jobs that run in a single iteration with a single set of parameters and jobs that include multiple iterations and sets of predetermined sets of parameters with termination criteria to stop execution when desired analysis results are obtained. Some jobs are run offline in a batch like mode and other jobs are run online in an interactive mode where users enter parameters for subsequent iterations based upon results of previous iterations until a predesigned analysis result terminates execution
• a method for fully integrated collection of business impacting data, analysis of that data and generation of both analysis-driven business decisions and analysis-driven simulations of alternate candidate business decision comprising the steps of: a) receiving business decision parameters and objectives using a client access interface stored in a memory of and operating on a processor of a computing device; b) retrieving a plurality of business data from a plurality of sources using a business data retrieval engine stored in a memory of and operating on a processor of a computing device; c) creating simulation models of real-world objects from available business data using an action outcome simulation module stored in a memory of and operating on a processor of one of more computing devices; d) predicting the outcome of predetermined business decision or business venture candidates and estimating the value at risk attached to each candidate by simulation of the outplay of the decision or venture using the action outcome simulation module.
• FIG. 1 is a diagram of an exemplary architecture of a business operating system according to an embodiment of the invention.
• FIG. 2 is a diagram of an exemplary architecture of an automated planning service cluster and related modules according to an embodiment of the invention.
• FIG. 3 is a diagram of an exemplary architecture of an action outcome simulation module and related modules according to an embodiment of the invention.
• Fig. 4 is flow diagram illustrations of setup and execution of three types of action outcome simulation module simulation runs according to an embodiment of the invention.
• Fig. 5. is a diagram depicting the primary processing locations of individual components of an action outcome simulation module simulation.
• Fig. 6 is a flow chart diagram illustrating centralized event queue timing according to an embodiment of the invention.
• Fig. 7 is a flow chart diagram illustrating time stepped queue timing according to an embodiment of the invention.
• Fig. 8 is a flow chart diagram illustrating conservative event-driven queue timing according to an embodiment of the invention.
• Fig. 9 is a flow chart diagram illustrating optimistic event-driven queue timing according to an embodiment of the invention.
• FIG. 10 is a block diagram illustrating an exemplary hardware architecture of a computing device used in various embodiments of the invention.
• FIG. 11 is a block diagram illustrating an exemplary logical architecture for a client device, according to various embodiments of the invention.
• Fig. 12 is a block diagram illustrating an exemplary architectural arrangement of clients, servers, and external services, according to various embodiments of the invention.
• Fig. 13 is another block diagram illustrating an exemplary hardware architecture of a computing device used in various embodiments of the invention
• Fig. 14 is flow diagram illustrations of single run and multiple run offline automated planning service cluster jobs according to an embodiment of the invention.
• Fig. 15 is flow diagram illustrations of single run and multiple run online automated planning service cluster jobs according to an embodiment of the invention.
• the inventor has conceived, and reduced to practice, a system and method for fully integrated capture and analysis of business information resulting in predictive decision making and simulation.
• Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise.
• devices that are in communication with each other may communicate directly or indirectly through one or more intermediaries, logical or physical.
• steps may be performed simultaneously despite being described or implied as occurring sequentially (e.g., because one step is described after the other step).
• the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the invention(s), and does not imply that the illustrated process is preferred.
• steps are generally described once per embodiment, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some embodiments or some occurrences, or some steps may be executed more than once in a given embodiment or occurrence.
• a "swimlane” is a communication channel between a time series sensor data reception and apportioning device and a data store meant to hold the apportioned data time series sensor data.
• a swimlane is able to move a specific, finite amount of data between the two devices. For example a single swimlane might reliably carry and have incorporated into the data store, the data equivalent of 5 seconds worth of data from 10 sensors in 5 seconds, this being its capacity. Attempts to place 5 seconds worth of data received from 6 sensors using one swimlane would result in data loss.
• a "metaswimlane” is an as-needed logical combination of transfer capacity of two or more real swimlanes that is transparent to the requesting process. Sensor studies where the amount of data received per unit time is expected to be highly heterogeneous over time may be initiated to use metaswimlanes.
• Fig. 1 is a diagram of an exemplary architecture of a business operating system 100 according to an embodiment of the invention.
• a data store 112 such as, but not limited to MONGODBTM, COUCHDBTM, CASSANDRATM or REDISTM depending on the embodiment.
• the directed computational graph module 155 retrieves one or more streams of data from a plurality of sources, which includes, but is in no way not limited to, a plurality of physical sensors, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and human input device information.
• data may be split into two identical streams in a specialized pre-programmed data pipeline 155a, wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis.
• the data is then transferred to the general transformer service module 160 for linear data transformation as part of analysis or the decomposable transformer service module 150 for branching or iterative transformations that are part of analysis.
• the directed computational graph module 155
• the high volume web crawling module 115 uses multiple server hosted preprogrammed web spiders, which while autonomously configured are deployed within a web scraping framework 115a of which SCRAP YTM is an example, to identify and retrieve data of interest from web based sources that are not well tagged by conventional web crawling technology.
• the multiple dimension time series database module 120 receives data from a large plurality of sensors that may be of several different types. The module is designed to accommodate irregular and high volume surges by dynamically allotting network bandwidth and server processing channels to process the incoming data.
• data from the multidimensional time series database and high volume web crawling modules may be sent, often with scripted cuing information determining important vertexes 145a, to the graph stack service module 145 which, employing standardized protocols for converting streams of information into graph representations of that data, for example, open graph internet technology although the invention is not reliant on any one standard.
• the graph stack service module 145 represents data in graphical form influenced by any pre-determined scripted modifications 145a and stores it in a graph-based data store 145b such as GIRAPHTM or a key value pair type data store REDISTM, or RIAKTM, among others, all of which are suitable for storing graph-based information.
• Results of the transformative analysis process may then be combined with further client directives, additional business rules and practices relevant to the analysis and situational information external to the already available data in the automated planning service module 130 which also runs powerful information theory 130a based predictive statistics functions and machine learning algorithms to allow future trends and outcomes to be rapidly forecast based upon the current system derived results and choosing each of a plurality of possible business decisions.
• the automated planning service module 130 may propose business decisions most likely to result is the most favorable business outcome with a usably high level of certainty.
• the action outcome simulation module 125 with its discrete event simulator programming module 125a coupled with the end user facing observation and state estimation service 140 which is highly scriptable 140b as circumstances require and has a game engine 140a to more realistically stage possible outcomes of business decisions under consideration, allows business decision makers to investigate the probable outcomes of choosing one pending course of action over another based upon analysis of the current available data.
• the pipelines operations department has reported a very small reduction in crude oil pressure in a section of pipeline in a highly remote section of territory.
• Fig. 2 is a diagram of an exemplary architecture of an automated planning service module and related modules according to an embodiment of the invention. Seen here is a more detailed view of the automated planning service module 130 as depicted in Fig. 1.
• the module functions by receiving business decision or business venture candidates as well as relevant currently available related data and any campaign analysis modification commands through a client interface 205.
• the module may also be used provide transformed data or run parameters to the action outcome simulation module 125 to seed a simulation prior to run or to transform intermediate result data isolated from one or more actors operating in the action outcome simulation module 125, 320, 320b, 320d, 320f during a simulation run.
• Contemplated actions may be broken up into a plurality of constituent events that either act towards the fulfillment of the venture under analysis or represent the absence of each event by the discrete event simulation module 211 which then makes each of those events available for information theory based statistical analysis 212, which allows the current decision events to be analyzed in light of similar events under conditions of varying dis-similarity using machine learned criteria obtained from that previous data; results of this analysis in addition to other factors may be analyzed by an uncertainty estimation module 213 to further tune the level of confidence to be included with the finished analysis. Confidence level would be a weighted calculation of the random variable distribution given to each event analyzed.
• Prediction of the effects of at least a portion of the events involved with a business venture under analysis within a system as complex as anything from the microenvironment in which the client business operates to more expansive arenas as the regional economy or further, from the perspective of success of the client business is calculated in dynamic systems extraction and inference module 214, which use, among other tools algorithms based upon Shannon entropy, Hartley entropy and mutual information dependence theory.
• the invention is therefore designed to run on expandable clusters 215, in a distributed, modular, and extensible approach, such as, but not exclusively, offerings of Amazon's AWS.
• these analysis jobs may run for many hours to completion and many clients may be anticipating long waits for simple "what if options which will not affect their business operations in the near term while other clients may have come upon a pressing decision situation where they need alternatives as soon as possible.
• This is accommodated by the presence of a job queue that allows analysis jobs to be implemented at one of multiple priority levels from low to urgent.
• job priorities can also be changed during run without loss of progress using the priority based job queue 218.
• Structured plan analysis result data may be stored in either a general purpose automated planning engine executing Action Notation Modeling Language (ANML) scripts for modeling which can be used to prioritize both human and machine-oriented tasks to maximize reward functions over finite time horizons 217or through the graph-based data store 145, depending on the specifics of the analysis in complexity and time run.
• ANML Action Notation Modeling Language
• Fig. 3 is a diagram of an exemplary architecture of an action outcome simulation module and related modules according to an embodiment of the invention 300. Set up and control for each simulation is specified through the client access web application 105 control screens programmed into the business operating system 305.
• These screens may include such options of choosing a simulation framework from a set of predefined simulation types 305a and eventually starting a novel simulation; inspecting progress of the multiple batch jobs that may represent the activities of actors, represent specific events and control the base environment, among other tasks of a simulation known to those skilled in the art 305d; streaming intermediary result data that is being collected during simulation progress 305b, obtaining the digital encryption keys for one or more simulation runs such that programmatic changes may be made to one or more static assumption parameters or to an algorithm that handles incoming model data among other examples known to those skilled in the art to improve the fidelity or usefulness of an upcoming planned run 305e; peruse and review results obtained and stored from past simulation runs 305c on a plurality of prospective business plans including, if included in the set up such parameters as: risk prediction, capital losses incurred, capital benefits obtained, closeness of end result to planning stage expectations, most influential variables in outcome, and assumptions used among a large plurality of other possibilities.
• the interface also allows comparison of result parameters from multiple related simulated experiments where starting values and assumptions of interest were changed to predict the influence of each 305c.
• the ability to run multiple related simulations in greatly shorter time than reality testing, which is an advantage of simulation in general and the ability to set up the simulation using, in large part graphical interface means and then have other modules of the integrated business system largely handle pulling in great amounts of current, highly relevant data and transformed supporting results for each simulation run is a significant advantage of the invention.
• the client access web application 105, 205, 305 offers a plurality of formats for presentation of the data 305f which may range from graphic- video or graphic-pictorial, to purely mathematical-numerical, as desired and appropriate to the simulation and intended use of the data at a given time point.
• AOSIM action outcome simulation module
• AOSIM agent nodes AOSIM worker 1 320a, AOSIM worker 2 320c, to AOSIM worker N 320e, the number of node expected to vary with the scale and complexity of the simulation being run, all under the programmatic control of the action outcome simulation master node which controls the introduction of data into the individual model actor substructures 320b, 320d, 320f and the timing of each simulations step run which may be changed to suit the level of inter-reliance of model actors within the simulation and the required accuracy level of the simulation versus time to run 600, 700, 800, 900 which may range from the requirement that intermediate data be transformed, possibly using the automated planning service module 130, 200.
• Processed data may then be entered manually back into the sequence 600, to a timing scenario where each actor steps through the simulation independently of all other actors, disregarding any possible inter- actor data dependency effects 900.
• each grouping of models or actors 320b, 320d, 320f which due to the distributive capabilities of the AOSIM module may operate as a single group, adjacent actors may model different participants in the real world system being simulated. So, as a simple, non-exclusive example, if simulating a city street corner, the first actor in group 320b may model a distracted pedestrian in a hurry to get to work while the second actor may model a moving car making a left hand turn and the third actor a basketball approaching the same intersection.
• actor may model such conditions fog or potholes that more passively act upon the other actors to some varying extent.
• environment level Beneath this entire actor structure is the environment level which monitors the working of each actor, collects and may transfer results to the master 320g for transformation and storage.
• the environment layer may also pass control directives to one or more of the actors at a given instance, one of which may be timing commands.
• Factors that may significantly affect the outcome reliability of a simulation such as but not limited to inherent variability within collected data, measurement inaccuracies, exogenous factors not envisioned in simulation setup, and discrepancies between computational parameter data and data from real world simulation analogs are ameliorated through the use of large sample sets of pertinent real world measurements collected from multiple available sources and stored by the system embodiment 330 for subsequent simulation guidance use, use of inferential information theory based statistics, and the use of heuristic modeling as described previously, among other techniques known to those skilled in the art to reduce uncertainty in such settings.
• MongoDB data store 350 collection 360 where the data is sorted by the system into actor related 360a, populations of actor related 360b, world 360c, projected vulnerability of simulation steps to negative effects on the overall outcome 360d, projected hazards 360e determined during simulation run and any actual projected loss incurred by the simulated venture or business pathway 360f. All of these data, in their finished state are included as part of the simulation result presentation 305f.
• Fig. 6 is a flow chart diagram illustrating centralized event queue timing according to an embodiment of the invention 600.
• the system being simulated is extremely complex or where even with the abilities of the AOSIM 300 to perform automated processing of data from categories such as but not limited to: physical world, cyber-physical interactions, socio-technical, information network interactions, persona integration and cognitive reasoning, at least some of the intermediate result data must be inspected and may be
• Fig. 7 is a flow chart diagram illustrating time stepped queue timing according to an embodiment of the invention 700.
• Fig. 8 is a flow chart diagram illustrating conservative event-driven queue timing according to an embodiment of the invention 800.
• the system being simulated includes actors that are somewhat interdependent and may rely on the outcome of one or more actors at one or more steps to result in reliable simulation results.
• Fig. 9 is a flow chart diagram illustrating optimistic event-driven queue timing according to an embodiment of the invention 900.
• the system being simulated does not include any interdependent actors that rely on the outcome of one or more actors at one or more steps to result in reliable simulation results or where time constraints and the effects of missing update such interdependent data are such that running the simulation as quickly as possible is desired.
• each actor 901, 902, 903, 904, 905, 906, 916 complete steps 907, 908, 909, 910, 911, 912, 913, (x) independently without regard to whether interdependency exists 914 before the actors begin the next step (x +1) 915 and therefore when this timing scheme is used, individual actors each 915 as rapidly as possible 914.
• Fig. 4 is flow diagram illustrations of setup and execution of three types of action outcome simulation module simulation runs according to an embodiment of the invention 400.
• the top panel 410 depicts the simple simulation which may be used to explore the myriad of "what if ideas that may arise during the operation of a business or where there is little or no foreseen capital and market risk involved.
• the set up phase of the simulation consumes the large majority of the steps as once simulation execution is initiated, the author must then only wait for and then interpret, possibly further manipulate the results 416.
• the process starts with the creation of administrative information 411, here such parameters as boundaries of data values to be used, the data store database origin or origins of data to be used, if more than one source exists, business operating system resource levels to be used, timing scheme to be used, progress indicators to be displayed and desired mode of display of the completed simulation may be just a few examples of a much larger set of administrative parameters known to those skilled in the art which may be specified.
• administrative information 411 here such parameters as boundaries of data values to be used, the data store database origin or origins of data to be used, if more than one source exists, business operating system resource levels to be used, timing scheme to be used, progress indicators to be displayed and desired mode of display of the completed simulation may be just a few examples of a much larger set of administrative parameters known to those skilled in the art which may be specified.
• a reliably useful end result integrally depends on the proper configuration of the factors that will affect the actors of the simulation, forming the milieu in which they will perform their programmed actions, here designated the "world” 412. That the constraints and
• Successful completion of a real- world reliable simulation may be greatly augmented by the ability to compare the results of a partial simulation run with as much of the limited real-world data as is available or possibly even incorporating portions of that real-world data into the constituent parts of the simulation such as the world configuration 412, selection of actor model characteristics 413 and types and the generation of appropriate actor populations 414.
• the business operating system offers a mechanism for use of this potential advantage by allowing the simulation constituents listed to be attached as run parameters to the automated planning service module, bringing the inferential statistic and Monte Carlo heuristic algorithms into support in directing the intelligent inclusion and use of data from other modules of the business operating system for improvement of the course taken by the simulation when run to completion 419.
• simulation initialization file containing all of the parameters, module tie-in references and administrative run directives may be created 415. This defines the simulation to be run in format readable by the action outcome simulation module. The simulation is then run, any post processing completed and the results displayed, possibly motion graphically through use of the observation state estimation service 140 with its game engine 140a and presentation manipulation scripting capabilities 140b, or possibly presented in some other format per the pre-design of authors of the simulation.
• the middle panel 420 depicts a simulation with financial aggregation which may be used to explore business decisions or ventures where the loss or gain of capital is possible.
• Capital as an actor behaves the same, so creating multiple instantiations of it in a simulation is a waste of time and potentially computing resources.
• Capital is therefore treated as an aggregate much as the actors in a system dynamics based simulation where all actors are aggregate agents of their real-world counterpart.
• the set up phase of the simulation consumes the large majority of the steps as once simulation execution is initiated, the author must then only wait for and then interpret, possibly further manipulate the results 427.
• the process starts with the creation of administrative information 421, here such parameters as boundaries of data values to be used, the data store database origin or origins of data to be used, if more than one source exists, business operating system resource levels to be used, timing scheme to be used, progress indicators to be displayed and desired mode of display of the completed simulation may be just a few examples of a much larger set of administrative parameters known to those skilled in the art which may be specified.
• administrative information 421 here such parameters as boundaries of data values to be used, the data store database origin or origins of data to be used, if more than one source exists, business operating system resource levels to be used, timing scheme to be used, progress indicators to be displayed and desired mode of display of the completed simulation may be just a few examples of a much larger set of administrative parameters known to those skilled in the art which may be specified.
• a reliably useful end result integrally depends on the proper configuration of the factors that will affect the actors of the simulation, forming the milieu in which they will perform their programmed actions, here designated the "world” 422. That the constraints and influences
• Successful completion of a real-world reliable simulation may be greatly augmented by the ability to compare the results of a partial simulation run with as much of the limited real-world data as is available or possibly even incorporating portions of that real- world data into the constituent parts of the simulation such as the world configuration 422, selection of actor model characteristics 423 and types and the generation of appropriate actor populations 424.
• the business operating system offers a mechanism for use of this potential advantage by allowing the simulation constituents listed to be attached as run parameters to the automated planning service module, bringing the inferential statistic and Monte Carlo heuristic algorithms into support in directing the intelligent inclusion and use of data from other modules of the business operating system for improvement of the course taken by the simulation when run to completion 429.
• simulation initialization file containing all of the parameters, module tie-in references and administrative run directives may be created 426. This defines the simulation to be run in format readable by the action outcome simulation module. The simulation is then run, any post processing completed and the results displayed, possibly motion graphically through use of the observation state estimation service 140 with its game engine 140a and presentation manipulation scripting capabilities 140b, or possibly presented in some other format per the pre-design of authors of the simulation.
• the middle panel 430 depicts a simulation for insurance or risk analysis which may be used to explore business decisions or ventures where the large scale loss of capital is possible due to wholesale loss of market share, loss of infrastructure or capital equipment investment or loss existing infrastructure or capital equipment is possible among other scenarios known to those skilled in the field.
• Insurance simulation may take the form of simulating the events that might lead to an expensive liability payout or equipment payout such as the failure of large oil pipeline in a secluded area with subsequent release of a significant number of gallons of crude oil, necessitating a massive cleanup and possible environmentally based restitution, or many other possible examples imaginable by one skilled in the field.
• the process starts with the creation of administrative information 431, here such parameters as boundaries of data values to be used, the data store database origin or origins of data to be used, if more than one source exists, business operating system resource levels to be used, timing scheme to be used, progress indicators to be displayed and desired mode of display of the completed simulation may be just a few examples of a much larger set of administrative parameters known to those skilled in the art which may be specified.
• administrative information 431 here such parameters as boundaries of data values to be used, the data store database origin or origins of data to be used, if more than one source exists, business operating system resource levels to be used, timing scheme to be used, progress indicators to be displayed and desired mode of display of the completed simulation may be just a few examples of a much larger set of administrative parameters known to those skilled in the art which may be specified.
• a reliably useful end result integrally depends on the proper configuration of the factors that will affect the actors of the simulation, forming the milieu in which they will perform their programmed actions, here designated the "world” 432. That the constraints and
• Successful completion of a real-world reliable simulation may be greatly augmented by the ability to compare the results of a partial simulation run with as much of the limited real-world data as is available or possibly even incorporating portions of that real- world data into the constituent parts of the simulation such as the world configuration 432, selection of actor model characteristics 433 and types and the generation of appropriate actor populations 434.
• the business operating system offers a mechanism for use of this potential advantage by allowing the simulation constituents listed to be attached as run parameters to the automated planning service module 439, bringing the inferential statistic and Monte Carlo heuristic algorithms into support in directing the intelligent inclusion and use of data from other modules of the business operating system for improvement of the course taken by the simulation when run to completion 439.
• the rules and boundaries for the handling and reporting of capital flow within the simulation must be specified before the simulation is run 435. This may include items such as but not limited to total capital budget, how much capital loss can be tolerated in a single or specified number of simulation steps, whether profit should be placed higher than other considerations in some or all sets of the simulation, whether capital can be traded for brand recognition or confidence and many more potential choices that may be known to one skilled in the field.
• Accurate, reliable simulation of the element of risk is extremely complex and requires the inclusion of several additional actors within a risk inclusive simulation such as, but not necessarily limited to models for hazard present under the conditions of the simulation 436, one or more catalogs of events that may lead to or add to both the initiation of a cataclysmic condition or its aftermath 437, models of known vulnerabilities of all actors within the simulation 438 and finally, models that specify liability possibilities the insurance coverage or finance contracts pertinent to the actors present in the simulation 440 to be run along with the potential loss figures for chains of events possible to transpire under the contractual obligations currently in force 440.
• a risk inclusive simulation such as, but not necessarily limited to models for hazard present under the conditions of the simulation 436, one or more catalogs of events that may lead to or add to both the initiation of a cataclysmic condition or its aftermath 437, models of known vulnerabilities of all actors within the simulation 438 and finally, models that specify liability possibilities the insurance coverage or finance contracts pertinent to the actors present in the simulation 440 to be run along with the
• simulation initialization file containing all of the parameters, module tie-in references and administrative run directives may be created 426. This defines the simulation to be run in format readable by the action outcome simulation module.
• the simulation is then run, any post processing completed and the results displayed, possibly motion graphically through use of the observation state estimation service 140 with its game engine 140a and presentation manipulation scripting capabilities 140b, or possibly presented in some other format per the pre-design of authors of the simulation. Due to the potential of multiple accidents which may result in the payment of very large sums of capital, this particular type of simulation may end with the system analyzing the policy portfolio of an underwriter to confirm that reserve capital is sufficient to cover a reasonable proportion of losses and premiums for individual corporations are in line with potential risk and resultant payout. 442
• Fig. 5. is a diagram depicting the primary processing locations of individual components of an action outcome simulation module. As has been implied and might be expected given the complexities of simulation of various business decision and business venture progression and outcomes, multiple modules within the business software operating system that makes up the invention may be involved with processing. As would be expected all simulation policy 501 is present within and the actual execution of simulations 502 take place within the action outcome simulation module 540.
• the actor or model component 505 which holds the modeled representation of the real-world items that take part in simulation is associated primarily with the action outcome simulation module where it runs, but the rules 505a that make up its actions and influence boundaries may be either modified before or after simulation run within the automated planning service module, 130, 550 as previously indicated 400 and possibly the decomposable transformer service module 150 the functions of which have been described previously. Similar, but not identical, to the actor component, the world component, the parameters that influence the actions of all actors during simulation are found and executed in the action outcome simulation module, but in this case, both the individual world entity 510 and its policy rules of action and influence during execution may be processed both before simulation run and after run commencement in.
• actor population component 515 while potentially important part of a plurality of simulations is in reality an aggregator rather than interacting directly with other components during simulation both it as a component of the simulation engine and the policy rules that make it up which has a framework derived from the world policy subcomponent 515a are entirely constructed and stored within the automated planning service module 130 and the decomposable transformer service module 150, 550 where weighting of the influence of certain individual actors and the total strength of population influence on the simulation may be coordinated to produce the most reliable, realistic end result of a simulation run.
• the last three components listed, hazard model 520, vulnerability model 525 and financial model 525 are all library like components included in specific simulations to enable the modelling of specific additional facets of a simulation not part of more simplistic simulation executions.
• the multidimensional time series data store 120 previously described, directed computational graph 155, decomposable transformer service module 150, 545, and the automated planning service module 550 may all be responsible for the creation and upkeep of accurate useful hazard model 520, vulnerability model 525 and financial model 530 components.
• Fig. 14 is flow diagram illustrations of single run and multiple run offline automated planning service module jobs according to an embodiment of the invention.
• Offline jobs run using the automated planning service module can be either single run, which include one set of objective and conditional and run specific parameters 1410 or multiple run which may contain multiple possible objectives multiple conditional and run specific parameters.
• the single run job 1410 as a model, the job starts with the system 130 in its initial state and a set of business objectives being entered 1403. All available data related to the objectives from various sources including the World Wide Web, sensors both physical sensors and logical sensors throughout the client company and any data obtained from sources know in the art are loaded 1401. Prior to actual analysis run, all of the data is first sanity validated 1411.
• Initial constructs are then created and the planning data structure instantiated as the populated framework for subsequent manipulation 1412.
• the core of the analysis 1413 may involve seeding the planning instance with the correct parameterized models 1414, executing the models, possibly using the transformative power of the directed computational graph 155, followed by collection of the results of those model analyses 1415 and then processing those results to align them with the intended scenarios.
• the outcome of business pathway analysis may be further analyzed 1416 for characteristics such as the amount of uncertainty intrinsic to the results under the input conditions and estimation of impact exogenous parameters not included may have on the predictions 1417.
• the results of the analysis, including confidence level information is distributed to the specifications of the service client who initiated the analysis 1418. Result distribution may involve the observation and state estimation service module 155 and any event modifications programmed to assist in representation 1404.
• the progression of multiple run offline jobs 1420 is quite similar to that above but allows for the analysis of business decisions where there may be multiple different values for one or more of the input parameters being used in the analysis such as where the client wants to change the time of implementation of a plan or where conditions are not accurately known.
• the invention is designed to accommodate any similar variances known to those skilled in the art. From the initial state 1402 with known objectives loaded 1403 and external data retrieved 1401 the job is initialized in the automated planning service module 130. Once all parameters are validated 1421, the explicit termination criteria for the multirun jobs is defined by the user 1422, this could be as simple as all scenarios have been analyzed. The mode with which system and world parameters are sampled, once, each run, or some other distribution must also be defined 1423.
• Fig. 15 is flow diagram illustrations of single run and multiple run online automated planning service module jobs according to an embodiment of the invention.
• results of one run of a job may have an effect that requires the client to then manually enter parameters for the next iteration or where determination of which data set is entered next depends on the results of a previous job run for the sake of expediency.
• automated planning service module jobs may be run in a more interactive or online priority 1530 and 1540 where the client has control of the process for a negotiated amount of time.
• the analysis process begins exactly as that of the corresponding offline job type 1531, 1541.
• explicit termination conditions are set 1532, 1542.
• the job then executes as per an offline single run job 1534, however, at the end of each data entry cycle, instead of distributing the results and exiting 1418, the explicit termination parameters, which may be the lack of further data entry is tested 1535 and the analysis cycle re-run 1534 with amended data 1533 until the termination criteria are met 1535 and the process exits 1535a. Results are distributed after each cycle (not shown).
• the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (ASIC), or on a network interface card.
• ASIC application-specific integrated circuit
• Software/hardware hybrid implementations of at least some of the embodiments disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory.
• a programmable network-resident machine which should be understood to include intermittently connected network-aware machines
• Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols.
• a general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented.
• At least some of the features or functionalities of the various embodiments disclosed herein may be implemented on one or more geneal-purpose computers associated with one or more networks, such as for example an end- user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof.
• at least some of the features or functionalities of the various embodiments disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments).
• FIG. 10 there is shown a block diagram depicting an exemplary computing device 10 suitable for implementing at least a portion of the features or functionalities disclosed herein.
• Computing device 10 may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware-based instructions according to one or more programs stored in memory.
• Computing device 10 may be configured to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.
• communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.
• computing device 10 includes one or more central processing units (CPU) 12, one or more interfaces 15, and one or more busses 14 (such as a peripheral component interconnect (PCI) bus).
• CPU 12 may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine.
• a computing device 10 may be configured or designed to function as a server system utilizing CPU 12, local memory 11 and/or remote memory 16, and interface(s) 15.
• CPU 12 may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like.
• CPU 12 may include one or more processors 13 such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors. In some
• processors 13 may include specially designed hardware such as application- specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device 10.
• ASICs application-specific integrated circuits
• EEPROMs electrically erasable programmable read-only memories
• FPGAs field-programmable gate arrays
• a local memory 11 such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory
• RAM non-volatile random access memory
• ROM read-only memory
• Memory 11 may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like.
• CPU 12 may be one of a variety of system-on- a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a Qualcomm SNAPDRAGONTM or Samsung EXYNOSTM CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices.
• SOC system-on- a-chip
• processor is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit.
• interfaces 15 are provided as network interface cards (NICs).
• NICs network interface cards
• NICs control the sending and receiving of data packets over a computer network; other types of interfaces 15 may for example support other peripherals used with computing device 10.
• interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like.
• interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRETM, THUNDERBOLTTM, PCI, parallel, radio frequency (RF), BLUETOOTHTM, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like.
• USB universal serial bus
• RF radio frequency
• BLUETOOTHTM near-field communications
• near-field communications e.g., using near-field magnetics
• WiFi wireless FIREWIRETM
• Such interfaces 15 may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM).
• an independent processor such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces
• volatile and/or non-volatile memory e.g., RAM
• processors 13 may be used, and such processors 13 may be present in a single device or distributed among any number of devices.
• a single processor 13 handles communications as well as routing computations, while in other embodiments a separate dedicated communications processor may be provided.
• different types of features or functionalities may be implemented in a system according to the invention that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below).
• the system of the present invention may employ one or more memories or memory modules (such as, for example, remote memory block 16 and local memory 11) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the embodiments described herein (or any combinations of the above).
• Program instructions may control execution of or comprise an operating system and/or one or more applications, for example.
• Memory 16 or memories 11, 16 may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein.
• At least some network device embodiments may include nontransitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein.
• nontransitory machine- readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD- ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory (as is common in mobile devices and integrated systems), solid state drives (SSD) and "hybrid SSD” storage drives that may combine physical components of solid state and hard disk drives in a single hardware device (as are becoming increasingly common in the art with regard to personal computers), memristor memory, random access memory (RAM), and the like.
• ROM read-only memory
• flash memory as is common in mobile devices and integrated systems
• SSD solid state drives
• hybrid SSD hybrid SSD
• such storage means may be integral and non-removable (such as RAM hardware modules that may be soldered onto a motherboard or otherwise integrated into an electronic device), or they may be removable such as swappable flash memory modules (such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices), "hot-swappable” hard disk drives or solid state drives, removable optical storage discs, or other such removable media, and that such integral and removable storage media may be utilized interchangeably.
• program instructions include both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a JAVATM compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language).
• interpreter for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language.
• systems according to the present invention may be implemented on a standalone computing system.
• Fig. 11 there is shown a block diagram depicting a typical exemplary architecture of one or more embodiments or components thereof on a standalone computing system.
• Computing device 20 includes processors 21 that may run software that carry out one or more functions or applications of embodiments of the invention, such as for example a client application 24.
• Processors 21 may carry out computing instructions under control of an operating system 22 such as, for example, a version of Microsoft's
• WINDOWSTM operating system Apple's Mac OS/X or iOS operating systems, some variety of the Linux operating system, Google's ANDROIDTM operating system, or the like.
• one or more shared services 23 may be operable in system 20, and may be useful for providing common services to client applications 24.
• Services 23 may for example be WINDOWSTM services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system 21.
• Input devices 28 may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof.
• Output devices 27 may be of any type suitable for providing output to one or more users, whether remote or local to system 20, and may include for example one or more screens for visual output, speakers, printers, or any combination thereof.
• Memory 25 may be random-access memory having any structure and architecture known in the art, for use by processors 21, for example to run software.
• Storage devices 26 may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form (such as those described above). Examples of storage devices 26 include flash memory, magnetic hard drive, CD-ROM, and/or the like.
• systems of the present invention may be implemented on a distributed computing network, such as one having any number of clients and/or servers.
• FIG. 12 there is shown a block diagram depicting an exemplary architecture 30 for implementing at least a portion of a system according to an embodiment of the invention on a distributed computing network.
• any number of clients 33 may be provided.
• Each client 33 may run software for implementing client-side portions of the present invention; clients may comprise a system 20 such as that illustrated above.
• any number of servers 32 may be provided for handling requests received from one or more clients 33.
• Clients 33 and servers 32 may communicate with one another via one or more electronic networks 31, which may be in various embodiments any of the Internet, a wide area network, a mobile telephony network (such as CDMA or GSM cellular networks), a wireless network (such as WiFi, Wimax, LTE, and so forth), or a local area network (or indeed any network topology known in the art; the invention does not prefer any one network topology over any other).
• a mobile telephony network such as CDMA or GSM cellular networks
• a wireless network such as WiFi, Wimax, LTE, and so forth
• a local area network or indeed any network topology known in the art; the invention does not prefer any one network topology over any other.
• Networks 31 may be implemented using any known network protocols, including for example wired and/or wireless protocols.
• servers 32 may call external services 37 when needed to obtain additional information, or to refer to additional data concerning a particular call.
• external services 37 may take place, for example, via one or more networks 31.
• external services 37 may comprise web-enabled services or functionality related to or installed on the hardware device itself.
• client applications 24 may obtain information stored in a server system 32 in the cloud or on an external service 37 deployed on one or more of a particular enterprise's or user's premises.
• clients 33 or servers 32 may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks 31.
• one or more databases 34 may be used or referred to by one or more embodiments of the invention. It should be understood by one having ordinary skill in the art that databases 34 may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means.
• one or more databases 34 may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as those referred to in the art as "NoSQL” (for example, Hadoop Cassandra, Google BigTable, and so forth).
• SQL structured query language
• variant database architectures such as column-oriented databases, in- memory databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the invention.
• database any combination of known or future database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular embodiment herein.
• database as used herein may refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system.
• security systems 36 and configuration systems 35 may make use of one or more security systems 36 and configuration systems 35.
• Security and configuration management are common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood by one having ordinary skill in the art that any configuration or security subsystems known in the art now or in the future may be used in conjunction with embodiments of the invention without limitation, unless a specific security 36 or configuration system 35 or approach is specifically required by the description of any specific embodiment.
• FIG. 13 shows an exemplary overview of a computer system 40 as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data. Various modifications and changes may be made to computer system 40 without departing from the broader scope of the system and method disclosed herein.
• Central processor unit (CPU) 41 is connected to bus 42, to which bus is also connected memory 43, nonvolatile memory 44, display 47, input/output (I/O) unit 48, and network interface card (NIC) 53.
• I/O unit 48 may, typically, be connected to keyboard 49, pointing device 50, hard disk 52, and real-time clock 51.
• NIC 53 connects to network 54, which may be the Internet or a local network, which local network may or may not have connections to the Internet.
• power supply unit 45 connected, in this example, to a main alternating current (AC) supply 46.
• AC alternating current
• functionality for implementing systems or methods of the present invention may be distributed among any number of client and/or server components.
• various software modules may be implemented for performing various functions in connection with the present invention, and such modules may be variously implemented to run on server and/or client.

Landscapes

• Engineering & Computer Science (AREA)
• Business, Economics & Management (AREA)
• Human Resources & Organizations (AREA)
• Theoretical Computer Science (AREA)
• Educational Administration (AREA)
• Software Systems (AREA)
• Economics (AREA)
• Entrepreneurship & Innovation (AREA)
• Strategic Management (AREA)
• General Physics & Mathematics (AREA)
• Physics & Mathematics (AREA)
• Tourism & Hospitality (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Operations Research (AREA)
• General Business, Economics & Management (AREA)
• Marketing (AREA)
• Game Theory and Decision Science (AREA)
• Artificial Intelligence (AREA)
• Quality & Reliability (AREA)
• Data Mining & Analysis (AREA)
• Evolutionary Computation (AREA)
• Medical Informatics (AREA)
• Development Economics (AREA)
• Computing Systems (AREA)
• General Engineering & Computer Science (AREA)
• Mathematical Physics (AREA)
• Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Applications Claiming Priority (3)

Publications (2)

Family

ID=60664584

Family Applications (1)

Country Status (3)

Families Citing this family (5)

Family Cites Families (6)

• 2017
  • 2017-06-19 WO PCT/US2017/038200 patent/WO2017219044A1/en unknown
  • 2017-06-19 CN CN201780036125.1A patent/CN109588054A/zh active Pending
  • 2017-06-19 EP EP17814277.4A patent/EP3472767A4/de not_active Withdrawn

Also Published As

Similar Documents

Legal Events