SYSTEM FOR INTELLECTUAL PROPERTY LANDSCAPE ANALYSIS, RISK MANAGEMENT, AND OPPORTUNITY IDENTIFICATION

An automated and continuous system and method for conducting worldwide analysis of the status of intellectual property in fields of interest and providing comprehensive and continuous IP landscape visualization, IP risk management, and IP opportunity identification sufficient for making informed business decisions regarding intellectual property in those fields.

BACKGROUND OF THE INVENTION

Field of the Invention

The disclosure relates to the field of use of computer systems to automatically analyze, manage, and monitor intellectual property risk and to identify opportunities for intellectual property expansion.

Discussion of the State of the Art

Intellectual property (IP) continues to become increasingly important in the modern business world. Yet, the management of IP risk and identification of IP opportunities is largely a manual task. When assessing the IP landscape associated with a certain field, manual searches are conducted through patent applications to determine whether relevant patents have been filed (typically referred to as a “Freedom to Operate” or FTO search). While this process helps with one aspect of a business' IP strategy, avoidance of IP infringement, is inefficient and provides only limited information as to the entire IP landscape surrounding that field. This process fails to provide sufficient information to develop a comprehensive IP strategy, including such considerations as the likelihood of patent invalidations, cross-licensing strategies, defensive IP strategies, areas of opportunity to expand IP holdings, and the like.

What is a needed is a system that provides automated and continuous IP analysis, and provides businesses with comprehensive guidance regarding the IP landscape, risks, and opportunities their fields of interest, so that they can develop effective, comprehensive IP strategies.

SUMMARY OF THE INVENTION

Accordingly, the inventor has conceived, and reduced to practice, a system and method that conducts automated and continuous multi-variate IP analysis from numerous data sources and provides comprehensive and continuous IP landscape visualization, IP risk management, and IP opportunity identification sufficient for making informed business decisions regarding intellectual property in those fields.

In a preferred embodiment, the system would provide automated and continuous analysis of the intellectual property landscape in fields of interest by using a deep web extraction engine to gather data from a comprehensive set of sources, including not only standard searches such as patent filings, but also such things as books, articles, academic course materials, technical papers, conference listings, analysis of publicly-available source code. The gathered data would be run through an IP landscape evaluator, which would provide a comprehensive evaluation and visualization of the status of intellectual property in fields of interest, including, but not limited to, generating a Freedom to Operate (FTO) analysis, analyzing the relative intensity of research, filings, and development of new technologies in the fields of interest, identifying gaps in existing intellectual property, assessment of intellectual property in related or adjacent fields, and identifying potential areas for intellectual property growth, acquisitions, or sales. This information would be displayed in a variety of formats, including graphs of relative intensity of research, filings, and development in fields of interest, geographical maps of intellectual property (worldwide, national, regional), charts of gaps in existing intellectual property, and relational diagrams of adjacent and related IP markets.

In another preferred embodiment, the system would also conduct automated and continuous IP risk management analysis. Using the data from the IP landscape evaluator, an IP risk management evaluator would conduct further analysis to provide comprehensive evaluation and visualization of the risks associated with a business' IP activities, including such things as patent infringement assessments, patent invalidation assessments, cross-licensing strategies, defensive IP strategies, and potential buyers or acquisition targets.

In another preferred embodiment, the system would also conduct automated and continuous IP opportunity analysis. Using the data from the IP landscape evaluator, an IP opportunity evaluator would conduct further analysis to provide comprehensive evaluation and visualization of the opportunities related to a business' IP activities, including such things as S-curve analysis for readiness of commercialization of new technologies, deterministic and stochastic evaluation of path-dependent technology evolution, fusion of multiple developing technologies into new IP, and identification of possible step changes in, or leapfrogging of, older technologies.

In an aspect of at least one embodiment, the method for providing automated and continuous analysis of the intellectual property landscape would comprise the steps of: (a) using a deep web extraction engine to search the internet for a comprehensive set of information related to intellectual property in a given field; (b) processing the information by performing at least a plurality of transformations and predictive analysis on the information and specifying at least an intended focus on intellectual property; and (c) providing a comprehensive evaluation and visualization of the status of intellectual property in fields of interest, sufficient for making informed business decisions regarding intellectual property in those fields.

In an aspect of at least one embodiment, the method for providing automated and continuous analysis of the intellectual property landscape would comprise the further steps of: (a) retrieving the comprehensive evaluation and visualization of the status of intellectual property in fields of interest; (b) processing the comprehensive evaluation and visualization of the status of intellectual property in fields of interest by performing at least a plurality of transformations and predictive analysis on the information and specifying at least an intended focus on intellectual property; and (c) providing a comprehensive evaluation and visualization of risk associated with intellectual property in fields of interest.

In an aspect of at least one embodiment, the method for providing automated and continuous analysis of the intellectual property landscape would comprise the further steps of: (a) retrieving the comprehensive evaluation and visualization of the status of intellectual property in fields of interest; (b) processing the comprehensive evaluation and visualization of the status of intellectual property in fields of interest by performing at least a plurality of transformations and predictive analysis on the information and specifying at least an intended focus on intellectual property; and (c) providing a comprehensive evaluation and visualization of business opportunities associated with intellectual property in fields of interest.

DETAILED DESCRIPTION

Accordingly, the inventor has conceived, and reduced to practice, a system and method that conducts automated and continuous multi-variate IP analysis from numerous data sources and provides comprehensive and continuous IP landscape visualization, IP risk management, and IP opportunity identification sufficient for making informed business decisions regarding intellectual property in those fields.

Definitions

As used herein, the term “intellectual property” or “IP” means intangible property of potentially commercial value in the form of patents, trademarks, trade secrets, copyrights, and other forms, as defined by applicable international, federal, and state laws.

As used herein, a “vector” may be defined as a container for compute instructions, and may comprise instructions and descriptions for data locality, process locality, priority, type, search, approach, and the like. Vectors may also be used in a search process, and for declaration of constraints regarding the conditions under which specific actions may be taken, limitations on inputs, limitations on outputs, limitations on downstream uses to be attached to outputs, and the like.

As used herein, a “run” may be a vector which has been evaluated and processed by a parameterized model execution engine according to various factors contributing to overall utility and objective function optimization.

Conceptual Architecture

FIG. 1is a diagram of an exemplary architecture of an advanced decentralized financial decision platform100according to an embodiment of the invention. Client access105to system or platform100for specific data entry, system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, occurs through the system's distributed, extensible high bandwidth cloud interface110which uses a versatile, robust web application driven interface for both input and display of client-facing information and a data store112such as, but not limited to MONGODB™, COUCHDB™, CASSANDRA™ or REDIS™ depending on the embodiment. Much of the business data analyzed by the system both from sources within the confines of the client business, and from cloud based sources107, public or proprietary such as, but not limited to: subscribed business field specific data services, external remote sensors, subscribed satellite image and data feeds and web sites of interest to business operations both general and field specific, also enter the system through the cloud interface110, data being passed to the connector module135which may possess the API routines135aneeded to accept and convert the external data and then pass the normalized information to other analysis and transformation components of the system, the directed computational graph module155, high volume web crawler module115, multidimensional time series database120and a graph stack service145. Directed computational graph module155retrieves one or more streams of data from a plurality of sources, which includes, but is not limited to, a plurality of physical sensors, network service providers, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and human input device information. Within directed computational graph module155, data may be split into two identical streams in a specialized pre-programmed data pipeline155a,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 may be then transferred to a general transformer service module160for linear data transformation as part of analysis or the decomposable transformer service module150for branching or iterative transformations that are part of analysis. Directed computational graph module155represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph. High-volume web crawling module115may use multiple server hosted preprogrammed web spiders which, while autonomously configured, may be deployed within a web scraping framework115aof which SCRAPY™ is an example, to identify and retrieve data of interest from web based sources that are not well tagged by conventional web crawling technology. Multiple dimension time series data store module120may receive streaming data from a large plurality of sensors that may be of several different types. Multiple dimension time series data store module120may also store any time series data encountered by system100such as, but not limited to, environmental factors at insured client infrastructure sites, component sensor readings and system logs of some or all insured client equipment, weather and catastrophic event reports for regions an insured client occupies, political communiques and/or news from regions hosting insured client infrastructure and network service information captures (such as, but not limited to, news, capital funding opportunities and financial feeds, and sales, market condition), and service related customer data. Multiple dimension time series data store module120may accommodate irregular and high-volume surges by dynamically allotting network bandwidth and server processing channels to process the incoming data. Inclusion of programming wrappers120afor languages—examples of which may include, but are not limited to, C++, PERL, PYTHON, and ERLANG™—allows sophisticated programming logic to be added to default functions of multidimensional time series database120without intimate knowledge of the core programming, greatly extending breadth of function. Data retrieved by multidimensional time series database120and high-volume web crawling module115may be further analyzed and transformed into task-optimized results by directed computational graph155and associated general transformer service160and decomposable transformer service150modules. Alternately, data from the multidimensional time series database and high-volume web crawling modules may be sent, often with scripted cuing information determining important vertices145a,to graph stack service module145which, 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). Through the steps, graph stack service module145represents data in graphical form influenced by any pre-determined scripted modifications145aand stores it in a graph-based data store145bsuch as GIRAPH™ or a key-value pair type data store REDIS™, or RIAK™, among others, any 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 data already available in automated planning service module130, which also runs powerful information theory-based predictive statistics functions and machine learning algorithms130ato allow future trends and outcomes to be rapidly forecast based upon the current system derived results and choosing each a plurality of possible business decisions. Then, using all or most available data, automated planning service module130may propose business decisions most likely to result in favorable business outcomes with a usably high level of certainty. Closely related to the automated planning service module130in the use of system-derived results in conjunction with possible externally supplied additional information in the assistance of end user business decision making, action outcome simulation module125with a discrete event simulator programming module125acoupled with an end user-facing observation and state estimation service140, which is highly scriptable140bas circumstances require and has a game engine140ato 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.

A significant proportion of the data that is retrieved and transformed by the business operating system, both in real world analyses and as predictive simulations that build upon intelligent extrapolations of real world data, may include a geospatial component. The indexed global tile module170and its associated geo tile manager170amay manage externally available, standardized geospatial tiles and may enable other components of the business operating system, through programming methods, to access and manipulate meta-information associated with geospatial tiles and stored by the system. The business operating system may manipulate this component over the time frame of an analysis and potentially beyond such that, in addition to other discriminators, the data is also tagged, or indexed, with their coordinates of origin on the globe. This may allow the system to better integrate and store analysis specific information with all available information within the same geographical region. Such ability makes possible not only another layer of transformative capability, but may greatly augment presentation of data by anchoring to geographic images including satellite imagery and superimposed maps both during presentation of real world data and simulation runs.

FIG. 2Ais a diagram of components of the advanced decentralized financial decision platform100configured specifically for use in investment vehicle management according to an embodiment of the invention200. The business operating system100previously disclosed in co-pending application Ser. No. 15/141,752 and applied in a role of cybersecurity in co-pending application Ser. No. 15/237,625, when programmed to operate as quantitative trading decision platform, is very well suited to perform advanced predictive analytics and predictive simulations to produce investment predictions. Much of the trading specific programming functions are added to the automated planning service module130of the modified business operating system100to specialize it to perform trading analytics. Specialized purpose libraries may include but are not limited to financial markets functions libraries251, Monte-Carlo risk routines252, numeric analysis libraries253, deep learning libraries254, contract manipulation functions255, money handling functions256, Monte-Carlo search libraries257, and quant approach securities routines258. Pre-existing deep learning routines including information theory statistics engine259may also be used. The invention may also make use of other libraries and capabilities that are known to those skilled in the art as instrumental in the regulated trade of items of worth. Data from a plurality of sources used in trade analysis are retrieved, much of it from remote, cloud resident201servers through the system's distributed, extensible high bandwidth cloud interface110using the system's connector module135which is specifically designed to accept data from a number of information services, either public or private, through interfaces to those service's applications using its messaging service135aroutines, due to ease of programming, are augmented with interactive broker functions235, market data source plugins236, e-commerce messaging interpreters237, business-practice aware email reader238and programming libraries to extract information from video data sources239.

Other modules that make up the advanced decentralized financial decision platform100may also perform significant analytical transformations on trade related data. These may include the multidimensional time series data store120with its robust scripting features which may include a distributive friendly, fault-tolerant, real-time, continuous run prioritizing programming platform221such as, but not limited to, Erlang/OTP, and a compatible but comprehensive and proven math library functions222, for example C++math libraries, data formalization and ability to capture time series data including irregularly transmitted burst data; the GraphStack service145which transforms data into graphical representations for relational analysis and may use packages for graph format data storage245, such as Titan or the like, and a robust scripting engine246, which may be a highly accessible programming interface, an example of which may be Akka, although other, similar, combinations may equally serve the same purpose in this role to facilitate optimal data handling; the directed computational graph module155and its distributed data pipeline155asupplying related general transformer service module160and decomposable transformer module150which may efficiently carry out linear, branched, and recursive transformation pipelines during trading data analysis may be programmed with multiple trade related functions involved in predictive analytics of the received trade data. Both possibly during and following predictive analyses carried out by the system, results may be presented to clients105in formats best suited to convey the both important results for analysts to make highly informed decisions and, when needed, interim or final data in summary and potentially raw for direct human analysis. Simulations which may use data from a plurality of field spanning sources to predict future trade conditions these are accomplished within the action outcome simulation module125. Data and simulation formatting may be completed or performed by the observation and state estimation service140using its ease of scripting and gaming engine to produce optimal presentation results.

In cases where there are both large amounts of data to be cleansed and formalized, and intricate transformations such as those that may be associated with deep machine learning, first disclosed in ¶067 of co-pending application Ser. No. 14/925,974, predictive analytics and predictive simulations, distribution of computer resources to a plurality of systems may be routinely required to accomplish these tasks due to the volume of data being handled and acted upon. The business operating system employs a distributed architecture that is highly extensible to meet these needs. Additionally, a number of the tasks carried out by the system may be extremely processor intensive. For these processor-intensive tasks the highly integrated process of hardware clustering of systems, possibly of a specific hardware architecture particularly suited to the calculations inherent in the task, may be desirable, if not required, for timely completion. The system includes a computational clustering module280to allow the configuration and management of such clusters during application of the business operating system. While the computational clustering module is illustrated inFIG. 2Aas directly connected to specific co-modules of the business operating system, these connections, while logical, are for ease of illustration and those skilled in the art may realize that the functions attributed to specific modules of an embodiment may require clustered computing under one use case and not under others. Similarly, the functions designated to a clustered configuration may be role, if not run, dictated. Further, not all use cases or data runs may use clustering.

Additionally, within the large amounts of data gathered and stored, a substantial amount of the stored data may require frequent updating, for instance, stock symbols and corresponding prices, which may prove to be time-consuming. Business operating system100may be configured to autonomously and continuously gather data in a background process, for example, using subroutines of connector module135, such as email reader238or market plugins236; using subroutines of automated planning service module130, such as financial markets function library251; using web crawler module115to scour news financial news sites; or using time series data store120to receive updated stock pricing at regular intervals. The data may then be processed and used by business operating system100to improve and update stored data. These operations may include, but not limited to, semantic extraction from corporate news and macro data; cross-linking to GraphStack entries; and automated time series feature engineering through the use of libraries like TSFresh, or using dimensionality reduction. Additionally, the high-bandwidth capabilities of business operating system100enables low-latency links to market data providers and venues to provide a nearly real-time channel to market data for the user using a ticker plant module233shown inFIG. 2C. The data that may be provided by market data providers and venues may include, but is not limited to, stock symbols and pricing, order book information, fill reports, news, and fundamentals. Business operating system100may also be configured to perform error-checking and self-heal the data as it is received.

In fields like finance, risks may be plentiful, and may come from many diverse sources. The source of risks may include, but is not limited to, systemic risks, for example collapse of a stock market; government risks, for example new regulations or legislative activity; and general risk, for example operational risks, disasters, personnel risk, and legal risks. With business operating system100configured to analyze market data, and other external data sourced from, for instance, financial news outlets or expert opinion, and analyzed using functions such as Monte Carlo risk routines252, business operating system100may be able to take into consideration the various risks, and more accurately determine their adverse effects on financial holdings. This may enable a user to stay on top of potential downward trends, and offer them the opportunity to take action in the face of new risk development.

FIG. 2Bis an extension of the advanced decentralized financial decision platform100shown inFIG. 2Ashowing directed computational graph module155furthered configured to perform financial data analysis using its associated transformer service module according to various embodiments of the invention. Specially configured directed computational graph module155may comprise routines for traditional model functions261, trading field mechanical calculations263, stochastic models and processes265, and generalized analytics and simulation calculations267. Traditional model functions261are operations involving standard models commonly used in the art. Examples of models used in traditional model functions261may include Black-Scholes, Ho and Lee, Hull-White, and Swan diagram modeling.

Trading field mechanical calculations263are operations involving standard pricing related calculations, for example, calculations involving pricing frames, options pricing calculations, and arbitrage calculations.

Stochastic models and processes265are operations relating to multivariate operations used in the art, for example, random walks process, Brownian motion, Weiner process, Ito differential, multivariate distributions (i.e. Markov chain Monte Carlo), multivariate Pareto sampling, and advanced estimators.

Generalized analytics and simulation calculations267are operations involving general mathematics, for example integrations, linear algebra calculations, predictive risk estimates, path dependent calculations, and time dependent calculations.

FIG. 2Cis an extended connector module as illustrated inFIG. 2A. In addition to functions and features found inFIG. 2A, connector module135may also have a custom algorithm module234, a ticker plant module233, and an extractor module232. Custom algorithm module234provides an interface to enable a user to add custom, user-created trading algorithms. The algorithms may utilize a rules-based system which is commonly found in business process modelling. For example, on a very basic level, a user may create algorithms to execute a particular trade when certain conditions are met, for instance when a certain order book spread occurs, or a stock arrives at a certain price. Ticker plant module233, provides a low-latency, practically real-time link to market data sources that may provide information, such as pricing pertaining to stocks, bonds, commodities, futures, options, and currencies. Extractor module232may be used by business operating system100to intelligently extract relevant information from sources such as current events, news, and sentiment and may be configured to extract information based on region or sector. The extracted information may be cleansed and processed for use in other modules of business operating system100.

It should be understood that the routines and subroutines illustrated in inFIGS. 2B and 2Care not intended to be comprehensive, and should instead be seen as an example of operations that may be configured for directed computational graph module155with the associated transformer modules, and connector module135. The operations listed are also not required to all be run in a single process, and may be selected and executed piecemeal in a modular manner depending on the requirements of the user.

FIG. 3is a flow diagram300of an exemplary function of the advanced decentralized financial decision platform100in the calculation of future investment performance. New investment opportunities are continuously arising and the ability to profitably participate in these new opportunities is of great importance. An embodiment of the invention100programmed to analyze investment trading related data and recommend investment vehicles may greatly assist in development of a profitable plan in potential new markets. Retrieval or input of any prospective new market related data from a plurality of both public and available private or proprietary sources acts to seed the process in step301, specific modules of the system such as connector module135with its programmable messaging service135a,high volume web crawler115, and directed computational graph module155, among possible others act to scrub, format, and normalize data from many sources for use. Such data is then subjected to predictive analytical transformations in step302, which may include traditional model functions such as, but not limited, to Black-Scholes, Ho and Lee, and Hull-White; trading field mechanical calculations such as, but not limited to, pricing frameworks, options pricing calculations, and arbitrage calculations; and more generalized analytics and simulation calculations such as, but not limited to, integrations, linear algebra calculations, predictive risk estimations, stochastic processes functions, path dependent calculations, and time dependent calculations, all of which may serve to create the most accurate assessment of investment fitness given a particular vehicle and the large volume of data that surrounds and affects its current and predictable future performance. During the calculation process, there may be information added to the body of data by the input interaction of an analyst or other human expert party in step313to increase the accuracy of the interim calculated projections as one of the designed functions of the business operating system is to retrieve, cleanse and aggregate the overwhelming volume of data connected to a field of decision allowing human users to concentrate on the creative and higher order aspects of that data.

Many of the calculations above may be carried out as part of linear, branched or recursive pipelines using either general transformer service module160, which may be specialized to rapidly perform linear transformation pipelines, and decomposable transformer service module150for branching and recursive pipelines in step317. Again, expert interaction may be added at this point in the form of added data or modified programmed functions. At step321, these results may then be formatted for direct display, formatted for further analysis by third party solutions or directly stored for later analysis, possibly in combination with other data in step323, if no predictive simulation is needed. Otherwise, accumulated data may be used in the creation of predictive simulations prior to display of that simulated information in the desired format in step322.

FIG. 4is a diagram of an indexed global tile module400according to an aspect. A significant amount of the data transformed and simulated by the business operating system has an important geospatial component. Indexed global tile module170allows both for the geo-tagging storage of data as retrieved by the system as a whole and for the manipulation and display of data using its geological data to augment the data's usefulness in transformation, for example creating ties between two independently acquired data points to more fully explain a phenomenon; or in the display of real world, or simulated results in their correct geospatial context for greatly increased visual comprehension and memorability. Indexed global tile module170may consist of a geospatial index information management module which retrieves indexed geospatial tiles from a cloud-based source410,420known to those skilled in the art, and may also retrieve available geospatially indexed map overlays from a geospatially indexed map overlay source430known to those skilled in the art. Tiles and their overlays, once retrieved, represent large amounts of potentially reusable data and are therefore stored for a pre-determined amount of time to allow rapid recall during one or more analyses on a temporal staging model450. To be useful, it may be required that both the transformative modules of the business operating system, such as, but not limited to directed computational graph module155, automated planning service module130, action outcome simulation module125, and observational and state estimation service140be capable of both accessing and manipulating the retrieved tiles and overlays. A geospatial query processor interface460serves as a program interface between these system modules and geospatial index information management module440which fulfills the resource requests through specialized direct tile manipulation protocols, which for simplistic example may include “get tile xxx,” “zoom,” “rotate,” “crop,” “shape,” “stitch,” and “highlight” just to name a very few options known to those skilled in the field. During analysis, the geospatial index information management module may control the assignment of geospatial data and the running transforming functions to one or more swimlanes to expedite timely completion and correct storage of the resultant data with associated geotags. The transformed tiles with all associated transformation tagging may be stored in a geospatially tagged event data store470for future review. Alternatively, just the geotagged transformation data or geotagged tile views may be stored for future retrieval of the actual tile and review depending on the need and circumstance. There may also be occasions where time series data from specific geographical locations are stored in multidimensional time series data store120with geo-tags provided by geospatial index information management module440.

FIG. 5is a diagram of an exemplary architecture of a regulatory label aware message routing system500according to an aspect. The embodiment works to simplify the exchange of messages containing sensitive and regulation controlled information by allowing routing boundaries, rules, policies and router handling programming for each to be centrally entered and then dictate message flow for the entire controlled WAN. The messages may enter the embodiment from external sources through a message label switch (MLS) aware messaging client505which is so named as it may set up routing paths based upon payload content dictated labels. The labels may contain policy and regulatory information pertaining to an individual, and pertaining to similar information connected to entities at an organization or government level. These messages may arrive at the messaging client already possessing a label designation for the source router, which may be software based, to be employed to send it, one or more labels disclosing the payload and thus designating the payload router, which again may be software based, to be targeted and a destination location indication of where the author requests the message sent505. Designation of formal destination or “receiver” MLS aware router may be made by an MLS addresser module510which selects a receiver router for the message at least partially based upon the current rule, policy and regulation entries stored in a MLS rules write ahead data store540. Once addressed with a receiving router, the message, now with its source router, payload router and receiver router designated, will pass to the source exchange module515which may serve as a message aggregator for the specified MLS source router560. The source router, which may be software based may be implemented and configured upon arrival of a message payload requiring specific regulatory of policy dictated capabilities. Also shown is an MLS type source label which indicates an individual (IND), organization (ORG) and government (GOV) labeling structure515awhere information about the sender, the sender's organization and the sender's country or geographical zone may be disclosed. For example, “US.ABC1234MHOSP.NKEANMD” may identify Dr. Noa Kean at ABC1234 Memorial Hospital in the US. Each portion of this label may invoke pre-engineered programming rules within the regulatory label aware message routing system that effect the payloads that may be sent, who may send the payload and the receivers to which they may be transmitted. At this stage in the process a pre-programmed rule such as but not limited to whether NKEANMD may send messages from the source router may be exercised. If this example rule, together with other possible source router rules are passed, the message may be bound to the source router560. A next process to occur prior to transmission of the message may be the analysis of the payload label plus any other policy markers that may accompany the message header in preferred aspects, in a payload exchange module565. The payload label may of be the form “payload class”<CLASS>, “payload method”<METHOD>, and “payload origin”<STDIN|OUT|ERR>565a.This label, like that for binding the source router, invokes pre-engineered programming pertaining to the characteristics of the payload contained in the message as disclosed by the payload label and in at least some instances, additional policy markers attached to the message possibly in a header stack. One of a great plurality of examples may be payload containing a HIPAA regulated patient record possessing a “PRECORD.TRANSFER.STDOUT” label. Some pre-programmed rules that may be applied are whether the sending individual, Dr. Kean in our example, may legally access and send the payload. A failure to pass this test or other tests, individually or in combination (where the ‘AND’ conjunctive is implicitly in effect by default but ‘OR’ disjunctive may also be used) may stop the transaction. Another rule may address whether ABC1234 Memorial Hospital may send the HIPAA regulated payload to the intended recipient and a last pre-programmed rule may determine whether the recipient has the credentials to receive the HIPAA protected payload. If all payload routing rules and policies are met, the message will be bound to the payload router570, which may be implemented and configured on-the-fly and the message may then be transmitted to the receiver exchange module575which serves as an aggregator for incoming messages to that router using a more global reverse receiver message, which while it has the general form of <GOV>.<ORG>.<IND> may use a more generic form of the label where the individual recipient is programmatically substituted with a generic, all inclusive, identifier (*).

The transfer to the receiver router, more than others, may involve the transmission of the message from one regulatory label aware message routing system, which itself may be highly distributed to another distributed regulatory label aware message routing system, possibly requiring a plurality of intermediary hops. Due to the use of message layer routing (OSI7/8) instead of packet layer routing (OSI3) and a networking protocol, multi-protocol label switching (MPLS), which, among a plurality of other capabilities, may allow an edge router, which the source router may be considered an example, to specify the router for the next hop in the path to the ultimate destination as well as possibly designating the ultimate destination router. At each intermediate router along the pathway the current router may strip its designation from the list and add that of the chosen next hop router in its place. An extension of MPLS may also allow labels constraining the travel of the routed message to routers with specific capabilities, possibly security protocols or message integrity related, or geographical zones, for instance only within the US, to be placed on the label stack such that only network routers with those characteristics may be used. This feature of adding policy labels may allow individuals, organizations and governments using regulatory label aware message routing system services to easily ensure that their network messages fulfill all necessary data transfer laws and regulations.

While <GOV>.<ORG>.<IND>515aand <CLASS>.<METHOD>.<STDIN|OUT|ERR>565amay be expected as common MLS router and MLS payload label sets, other embodiments may use labels having different informational constituents that are known to that messaging network system but are not <GOV>.<ORG>.<IND>or <CLASS>.<METHOD>.<STDIN|OUT|ERR> as the invention does not specify what label types must be used or the number of label types that constitute a valid label. This feature provides a greatly expanded set of the types of information may be used and may provide a large degree of flexibility for evolution of the system as laws, regulations and corporate practices continue to change.

Messages sent from a source to a receiver successfully are aggregated in the receiver router's receiver exchange module575. There, label constituents and associated policy labels may be inspected to confirm that the receiving government or organization facility is authorized to receive the payload. For example the message from “US.ABC1234MHOSP.NKEANMD” that apparently includes a patient record as the payload “PRECORD.TRANSFER.STDOUT.” As the receiver may be another hospital in the US, “US.WXYZ54321MHOSP.*”575awhich may be programmatically implemented on a physical node on-the-fly so most likely has all processes for the receipt of HIPAA governed materials already in place, the message is expected to be received and placed in a client upstream payload exchange module585where the ability of the receiving individual, Dr. Jo Wilson, may be confirmed using the payload label585abefore being placed in a client federated payload exchange module590for the recipient, J. Wilson, MD. under the handling requirements for the materials listed in the payload label590a.In cases where a single message arrives with more than one recipient, the entire message may be duplicated such that each recipient gets an autonomous copy of the message which may be modified or tracked per programmed rules of the embodiment.

Laws, regulations and both corporate and network service policies may change significantly over time. Embodiments of the regulatory label aware message routing system provides the ability to write routing rules using a plurality of programming languages and may have extension libraries for at least a subset of those languages to allow for the precise and efficient codification of message handling actions such that all nuances of these important, potentially complex directives may be accurately represented. Programming of route or policy directives may be accomplished remotely545in most embodiments using programming interface clients specific for either route rule command entry520or route policy command entry530. Certain aspects may use only direct MLS programming client connections for route rule programming changes, policy rule programming changes or both to maintain a higher level of security. MLS route rule programming is normalized in an MLS route writing module525and, upon confirmation of the authority of the programming author by the MLS route writing module may be committed to an append-only MLS rules write-ahead data store540for persistent storage. Similarly, MLS policy rule programming is normalized in an MLS policy writing module535and, upon confirmation that the author of the new programming is authorized to add rule code to the routing system, committed to the append-only MLS rules write ahead data store540for persistent storage.

For maximal forensic analysis opportunity and change tracking capabilities, embodiments of the write ahead log540, which hold the current, working, set of both routing and policy rules as well as records of all previous rules may incorporate a distributed ledger. One distributed ledger mechanism that may be used are available blockchains such as BITCOIN™, FACTOM™, LBRY™ and BIGCHAINDB™ among others where any modification of previous entries once committed is extremely difficult, if not impossible. While these blockchain services currently suffer from low data storage ceilings and may require purchase of cryptocurrency per unit storage, this drawback may be overcome by embodiments by combining secured, conventional database storage to store the full rule programming information while using one of the blockchain services to store hash recorded information to serve as the ledger. Another mechanism for secure, persistent write ahead log change tracking that may be used by embodiments is to control the change of route and policy rule programming through smart contracts or some other, similar vehicle known to those skilled in the art.

Translation of the current router and policy rules of the write ahead log540into the router560,570,580behavior of the embodiment may be performed by the MLS route module550for router rules and the MLS policy module555for policy expressions. These modules may perform updates by destroying existing software based routers and creating new routers compliant for the newest rule state or by updating the existing router or routers to reflect the current rule status based upon instantaneous embodiment conditions or implementation. This allows for the most efficient rule entry to rule implementation pathway based upon the specific needs of the embodiment.

As embodiments are designed to be a distributed service, each of the described features may individually take place on different physical servers possible residing in separate, distant, data centers.

FIG. 6is a flow diagram of an exemplary function of a regulatory message label aware message routing system600in routing sensitive electronic messages according to an aspect. The message payload is generated by the message client and may include data comprised of one or more of a plurality of both sensitive or regulated information parts which in turn may include but are not limited to personal identification information such as bank account numbers, personal identification numbers (ex. a social security number, driver license number, or similar such code known to those skilled in the art), national security and defense information, or intellectual property information, just to name a few examples of the focus of the function of the embodiment, and non-regulated portions601. During the creation of the message, the author may also indicate the entity meant to receive the message. The message client may then create a header specifying the source of the message as well as the contents of its payload in the message's payload level header, placing labels, also known as “keys” corresponding to <GOVERNMENT>, <ORGANIZATION>, and <INDVIDUAL> for the source router of the message and <CLASS>, <METHOD>, and <ORIGIN>describing the payload602. Source router label information within the header and the payload description label information may then be used to address the message to a receiver router based upon the contents of the message header, the intended recipient and the current routing rules and policies stored within the embodiment603. It is possible that the combination of the message header's source router and payload keys and the current embodiment's router rules and policies, no acceptable receiver router will be generated as the message may not be sent to the intended recipient. Under this condition when the message is bound to the source router by the header's sourceKey604, this routing rule failure or some other routing rule or policy failure later determined605may lead606to the message not being sent607in which case the message client (FIG. 5, 505) may be informed. The nature and restrictions upon the payload of the message may also be determined based upon the embodiment's message client generated payload label designations609after the message is aggregated upon passing through the source router and bound to the payload router608. Again, failure to comply with routing rules and policies based on payload contents may lead610to a failure of the message to progress to the intended recipient611for security, secrecy, or statutory restrictions, just to name a few examples of delivery failure categories familiar to those skilled in the art and handled by embodiments. Upon successful inspection of the payload key with all rules and policies fulfilled, the message may be sent to the recipient. This may be done by first sending the message to a receiving router for the organization, ignoring the receiving individual and may take multiple transitions between connected routing appliances (hops) to accomplish. These hops are pre-specified by embodiments with the header receiver label first pointing to the first intermediate hop router, which upon reaching the first intermediate hop router is stripped from the header and replaced by the label for the second intermediate hop router and so on, the process of substituting the receiving router label repeating until the ultimate destination router is reached. The path or router hops taken may be affected by other policy or router rules such as but not limited to restrictions on geographical zone or region or information protection protocols present, that each router must fulfill, for example “US”, “defense department controlled” or “HIPAA safeguards in place” to name just a few illustrative possibilities, the message may be restricted only to MLS routers in the US, restricted only to MLS routers controlled by the military, or only MLS routers running specific information handling or protection protocols, HIPAA protections, in the example. Upon reaching the originally designated receiving MLS router612, often serving the organization to which the receiving individual belongs613, the MLS header including all labels may be stripped the message forwarded, provided that individual is determined to be authorized to handle the sent information612, using lookup for the recipient individual, the message is delivered using classic OSI layer3routing and layer2switching614.

FIG. 7is a diagram illustrating the use of routing regulatory labels to create availability zones700. One way of characterizing the areas where message payloads governed by equivalent regulations and policies is through the construct of availability zones. Availability zones may be a large geographical region such as a country, for example the United States701, Mexico702and Canada703just to list three of the plurality known to those skilled in the art. Other availability zones may result from the presence of a specific organization such as but in no way limited to military installations710a,710b,710cand790which may possess the ability to process defense regulated messages715a,715b,715cor health care facilities720a,720b,720cwhich may occupy geography as small as a single building and be equipped to process HIPAA regulated messages725a,725b,725c.Based upon these availability zones and MLS actionable labels, messages may be tightly controlled for transmission and delivery. A USA (US) defense (DEF) regulated and labeled message717with an MLS header717amay thus be sent to USA military installations such as but not limited to bases and buildings710a-cover MLS service routers715a,715b,715c.When employed sensitive US defense (DEF) messages717may be successfully sent from the source router715ato one or more destination receiver routers715band715cwithin other US DEF availability zones710b,710c.Messages with US and DEF labels, signifying they are regulated by rules for US and DEF will not be sent762to a DEF availability zone for Mexico (DEF MEX)790as the MLS router795has only the credentials imparted by “DEF.” The same message will not be sent761to a US HIPPAA compliant availability zone720aas the HIPAA MLS router in the zone lacks DEF authorization. Similarly, a health care message payload755with a MLS compliant header755awill be successfully sent by a HIPAA compliant MLS source router725cto a HIPAA compliant MLS router725bat a second HIPAA credentialed availability zone720bbut not to a US DEF authorized availability zone710cwhich lacks HIPAA data handling protocols763. Embodiments may route messages through compliant MLS router exclusive paths715ato715bto715cwhen intermediate hops are required. Failed message transmission attempts761,762,763would fail prior to transmission out of the source availability zones. Partial paths in those samples were solely to illustrate the intended, failing target.

Certain embodiments may routinely encrypt the payload or handle payloads with task specific encoding such as but not limited to structured threat information expression (STIX), trusted automated exchange of indicator information (TAXII), and cyber observables (CybOX), among other similar offerings known to those skilled in the art.

Advanced decentralized financial decision platform100, and the systems and methods discussed above, while proficient and analyzing and predicting changes in financial markets, may require additional configuration to be more adept in dealing with the distributed nature, and latency dependence of globally distributed high-frequency trading.FIG. 8is a block diagram of an exemplary system architecture for a system800for decentralized trading according to various embodiments of the invention. System800may comprise a parametric evaluator810, an optimizer820, a rules engine830, a model definition language service840, and a data store860. System800may use functions of business operating system100to continually monitor and track current status of connections and system states. For example, sensor capabilities may be used to collect and store time-series data in multidimensional time-series data store120, or observation and state estimation service140may be used for continuous monitoring. Connection and system data may additionally be indexed with a global tile module170.

It should be understood that the components of system800may be in logical form, or may be an external service. Other embodiments of system800may have less components than what is shown inFIG. 8, while other embodiments may have additional components. A messaging system, such as the system discussed inFIGS. 5-7, may be used to route labeled data sent from system800.

Parametric evaluator810may be configured to assess model performance and bias, and may comprise a model execution engine811. Parametric evaluator810may utilize functions of business operating system100, such as DCG module155with associated transformer modules or automated planning service130, to analyze a plurality of data flow localities and priorities, and compile a list of results according to predefined factors, such as overall associated costs, volatility, profitability, effectiveness of global system optimizations, and the like.

Model execution engine811may utilize functions of business operating system100, such as DCG module155with associated transformer modules or automated planning service130, to analyze and parameterize a plurality of vectors, and their outcomes when given a plurality of factors relating to a trade, such as overall cost, effectiveness in global system optimization, profitability, volatility, and the like. The parameterization of a vector description may result in a “run”, which may be sent to optimizer820for further processing and analysis.

Optimizer820may be configured to use functions of business operating system100, such as DCG module155with associated transformer modules, or automated planning service130, to analyze “runs” that received from parametric evaluator810, and generate recommendations regarding appropriateness of one or more data flow localities, such as regulatory issues or legality, or utility for one or more sets of exogenous factors or system states. For example, optimizer820may recommend a combination of data flow and storage localities based on current global system states to determine a course of action for one or more financial trades resulting in favorable outcomes by choosing whether to migrate data, migrate processes, or call into spot markets to control data and processing locality in order to minimalize latency associated with execution trades across geographically distributed market centers; or analyzing hypothetical system states, such as using simulation engines, either provided by business operating system100or an external simulation system, to operate an identical instance in simulation to identify current and future bottlenecks.

When used in handling of rules, optimizer820may be configured to define a set of rules pertaining to the appropriateness of data locality and process locality with regards to a system condition for a given purpose, for instance, for determining profitable trades, which may be expressed in a declarative formalism accessible to rules engine830. When used in conjunction with machine learning methods, such as deep learning, transfer learning, reinforcement learning, and the like, optimizer820may develop an understanding of optimal models, groups of models, or rules defining model appropriateness or performance over time; and may change or restrict ordering of model packages or rules combinations based on the developed understanding.

Rules engine830may be configured to use functions of business operating system100, such as DCG module155with associated transformer modules, graph stack service145, and automated planning service130, to enable management of system rules, and also to evaluate specific elements of a given instance of one or more models when given any definition for the current or future state of said models. For example, rules engine830may verify that a request is allowed or appropriate based on the intended use, for example, feasibility or legality of an intended trade; whether a defined confidence requirement or other conditions are met; and evaluate configuration-specific terms and requirements as specified in user-defined operating constraints or guidelines. Rules engine830may evaluate rules by executing a forward chaining deduction of data amassed from a set of antecedents derived from model definition language service840for a particular application or purpose. Rules engine830supports layered “batteries” of modular tests, where functional decomposition of rules supports higher degrees of user productivity and rules re-use.

Model definition language service840may be configured to use functions of advanced decentralized financial decision platform100, such as DCG module155with associated transformer modules, graph stack service145, and automated planning service130, to allow user management of models, and defining of vectors using a declarative specification language (DSL). The use of a DSL for vectorizing the compute environment and data flow descriptions may enable linking of search processes to the rules engine830, parametric evaluator810, and feedback loop processes during ongoing operational-use based on the ability to encode appropriateness when combined with rules engine830, serving as a basis for deep and reinforcement learning to support ongoing improvement to functions of optimizer820. Model definition language service840may also enable a user or an autonomous trading system to initiate evaluation of specific pipelines, activities, overall system health, and the like of a specific instance of system800.

FIG. 9is an illustration of an exemplary topography900of a system employing a plurality of decentralized trading systems800[a-d] according to various embodiments of the invention. Topography900is an example of a layout of various components within a geographical area, for example spanning a continent or even on a global scale, and illustrates a plurality of systems800[a-d] connecting with a plurality of user global market centers910[a-e], such as a stock market or foreign exchange markets, through a wide area network connection; and a plurality of user devices930[a-n], which may be a single user or group of users accessing trading platform800athrough, for example, a web application, mobile device, spatial operating system, AR or VR system, and the like.

Systems800[a-d] may be flexible in their placement and locale, which may include, for example, as a standalone system800a;running in a virtual machine of a cloud service provider, such as AMAZON AWS920,800d;residing inside a global market center910b,800c;or even submerged in a body of water940,800b,for example inside a mobile submersible data center. Locations for systems800[a-d] may be strategically chosen, so that they may be useful in operating as an intermediate connection to a trading market. Topography900utilizes a centralized control point in system800afor users to communicate with decentralized deployment of a plurality of instances of system800[b-d]. Any particular instance may be chosen by an optimizer of system800aas the locality for data processing and storage; or system in which to execute a trade based on metrics such as system availability, latency to reach a target global market for trading a certain asset, and the like.

It should be understood that the layout and components depicted inFIG. 9is used for demonstration purposes, and does not represent a limitation of the present invention. For example, there may be more than one control point, more decentralized trading system endpoints, more global markets, and the like.

FIG. 12is a block diagram illustrating an embodiment1200of a system for providing automatic and continuous IP landscape evaluation, IP risk management, and IP opportunity identification. A deep web extraction engine1201is used to gather data from a comprehensive set of sources, including not only standard searches such as patent filings, but also such things as books, articles, academic course materials, technical papers, conference listings, analysis of publicly-available source code. The gathered data would be run through an IP landscape evaluator1202, which would provide a comprehensive evaluation and visualization of the status of intellectual property in fields of interest, including, but limited to generating a Freedom to Operate (FTO) analysis, analyzing the relative intensity of research, filings, and development of new technologies in the fields of interest, identifying gaps in existing intellectual property, assessment of intellectual property in related or adjacent fields, and identifying potential areas for intellectual property growth, acquisitions, or sales. This information would be displayed using visualization tools1205in a variety of formats, including graphs of relative intensity of research, filings, and development in fields of interest, geographical maps of intellectual property (worldwide, national, regional), charts of gaps in existing intellectual property, and relational diagrams of adjacent and related IP markets. In another embodiment, the system would also conduct automated and continuous IP risk management analysis. Using the data from the IP landscape evaluator1202, an IP risk management evaluator1203would conduct further analysis to provide comprehensive evaluation and visualization of the risks associated with a business' IP activities, including such things as patent infringement assessments, patent invalidation assessments, cross-licensing strategies, defensive IP strategies, and potential buyers or acquisition targets. In another embodiment, the system would also conduct automated and continuous IP opportunity analysis. Using the data from the IP landscape evaluator1202, an IP opportunity evaluator1204would conduct further analysis to provide comprehensive evaluation and visualization of the opportunities related to a business' IP activities, including such things as S-curve analysis for readiness of commercialization of new technologies, deterministic and stochastic evaluation of path-dependent technology evolution, fusion of multiple developing technologies into new IP, and identification of possible step changes in, or leapfrogging of, older technologies.

Detailed Description of Exemplary Aspects

FIG. 10is a flow diagram for an exemplary method1000for model evaluation using a parametric evaluator according to various embodiments of the invention. At an initial step1003, parametric evaluator810receives a plurality of vectors. As discussed above, vectors may be specified by a user using model definition language service840. At step1006, factors contributing to overall utility or objective may be specified by either a user or an autonomous trading system. At step1009, parametric evaluator810may compile a list of results based on performance and bias with regards to the vectors, and specified utility or objective. At step1012, parametric evaluator810to processes the list of results using a model execution engine, which may generate one or more “runs”. At step1015, parametric evaluator810sends the one or more runs to optimizer820for further optimization.

FIG. 11is a flow diagram for an exemplary method1100for optimizing a request according to various embodiments of the invention. At an initial step1103, optimizer820receives one or more runs from parametric evaluator810. At step1106, optimizer820evaluates appropriateness or utility of a run's data flow locality, for instance, whether the intended target instance is capable or allowed to execute a particular trade. At step1109, optimizer820evaluates exogenous factors and system states of a target system, such as latency or other factors contributing to connection problems. At step1112, optimizer820generates recommendations based on results of the evaluation conducted in steps1106and1109. At step1115, a user is presented with the recommendations. In some embodiments, the recommendations may be used by an autonomous trading platform, which may execute trades based on defined user preferences.

FIG. 13is a diagram illustrating an application of an aspect1300of the system, the previously disclosed IP landscape evaluator1202. Using the previously disclosed deep web extraction engine1201, data is gathered from a comprehensive set of sources1301, including not only standard searches such as patent filings, but also such things as books, articles, academic course materials, technical papers, conference listings, analysis of publicly-available source code. The gathered data would be run through an IP landscape evaluator1202, which would provide a comprehensive evaluation and visualization of the status of intellectual property in fields of interest1302, including, but limited to generating a Freedom to Operate (FTO) analysis, analyzing the relative intensity of research, filings, and development of new technologies in the fields of interest, identifying gaps in existing intellectual property, assessment of intellectual property in related or adjacent fields, and identifying potential areas for intellectual property growth, acquisitions, or sales.

FIG. 14is a diagram illustrating an application of an aspect1400of the system, the previously disclosed IP risk management evaluator1203. Using the outputs1401from previously-disclosed IP landscape evaluator1202, an IP risk management evaluator1203would conduct further analysis to provide comprehensive evaluation and visualization1402of the risks associated with a business' IP activities, including such things as patent infringement assessments, patent invalidation assessments, cross-licensing strategies, defensive IP strategies, and potential buyers or acquisition targets.

FIG. 15is a diagram illustrating an application of an aspect1500of the system, the previously disclosed IP opportunity evaluator1204. Using the outputs1501from previously-disclosed IP landscape evaluator1202, an IP opportunity evaluator1204would conduct further analysis to provide comprehensive evaluation and visualization1502of the opportunities related to a business' IP activities, including such things as S-curve analysis for readiness of commercialization of new technologies, deterministic and stochastic evaluation of path-dependent technology evolution, fusion of multiple developing technologies into new IP, and identification of possible step changes in, or leapfrogging of, older technologies.

FIG. 16is a diagram illustrating an application of an aspect1600of the system, the previously disclosed IP visualization tools1205. The collective outputs1601from the IP landscape evaluator1202, the IP risk management evaluator1203, and the IP opportunity evaluator1204, would be displayed using visualization tools1205in a variety of formats1602, including graphs of relative intensity of research, filings, and development in fields of interest, geographical maps of intellectual property (worldwide, national, regional), charts of gaps in existing intellectual property, and relational diagrams of adjacent and related IP markets.

FIG. 17is a process flow diagram illustrating a method1700for providing automated and continuous IP landscape evaluation, IP risk management, and IP opportunity identification. A deep web extraction engine is used to search the internet for a comprehensive set of information related to intellectual property in a given field1701. The gathered data is passed to an IP landscape evaluator to be processed into a comprehensive evaluation and visualization1705of the status of intellectual property in fields of interest, sufficient for making informed business decisions regarding intellectual property in those fields1702. The outputs of the IP landscape evaluation are optionally provided passed to an IP risk management evaluator to provide a comprehensive evaluation and visualization of risk associated with intellectual property in fields of interest1703. The outputs of the IP landscape evaluation are also optionally provided passed to an IP opportunity evaluator to provide a comprehensive evaluation and visualization of business opportunities associated with intellectual property in fields of interest1704.

Hardware Architecture