Token mismatch detection and correction device

Abstract

The system, apparatus, and method aligns a token representing the status of a care recipient with at least one stakeholder represented by an additional token. The system identifies incentives in multi-party interactions, where such incentives are represented by specification-value pairs, and through the use of game theory, machine learning, pattern identification and recognition, and / or any form of digital twin, misalignments in such specification-value pairs can be identified and responded to in a manner that can avoid and / or mitigate the impact of such misalignments on the interactions of those parties, including seeking to reduce fraud or other undesirable behavior within at least one system.

Description

[Technical Field]

[0001] Aspects of the present disclosure generally relate to aligning a token representing a condition of a care recipient with at least one stakeholder represented by a further token. [Background technology]

[0002] Many situations involving a person's wellness and care may involve a variety of incentives by and for the person, including the systems designed to support the person, other people, institutions, and other stakeholders involved with the person, which are at least in part representative of the interests of those stakeholders.

[0003] For example, these incentives may include money, safety, security, stress, influence, compliance, time management, optimization, theft, PR / branding, reputation, marketing, competitive advantage, deception, social advantage, information management (including concealment / restriction / information retention, etc.), effort minimization, recognition, privacy, control, and various behaviors such as jealousy, envy, and / or other individual and / or collective behaviors. Summary of the Invention [Problem to be solved by the invention]

[0004] In many situations, these incentives can become misaligned, resulting in systematic and / or individual abuse of these systems and relationships. Almost all systems have constraints of some kind that can be exploited for unintended consequences and / or to the benefit of malicious and / or self-interested actors. [Means for solving the problem]

[0005] Embodiments include systems, devices and methods for aligning a token representing a care recipient's condition with at least one stakeholder represented by a further token.

[0006] In one embodiment, the system includes a plurality of environmental sensors. The plurality of environmental sensors monitor a series of interactions of the care recipient, resulting in a detection data set and providing a first token. The first token includes the detection data set representing the behavior of at least one stakeholder in the environment. Each behavior is represented by a multidimensional feature set that forms part of the care recipient's health management profile. The system further includes a fraud avoidance predictive system having a transceiver, a non-transitory computer-readable storage medium, and at least one hardware processing unit. The transceiver receives the first token from the plurality of environmental sensors. The non-transitory computer-readable storage medium stores a digital twin token. The digital twin token represents the care recipient's previously uneventful behavior in the environment. The at least one hardware processing unit, which may be a microprocessor, compares the first token with the digital twin token and matches the first token with the digital twin token using game theory.

[0007] The alignment of the first token with the digital twin token may be through a resulting incentive to at least one stakeholder.

[0008] Aligning the first token with the digital twin token can align the configuration of multiple environmental sensors.

[0009] Game theory can be cooperative and non-cooperative games, standard and scalable games, simultaneous and sequential games, constant-sum, zero-sum and non-zero-sum games, and symmetric and asymmetric games.

[0010] In another embodiment, the transceiver receives detected datasets from a plurality of environmental sensors. The detected datasets represent behaviors of at least one stakeholder in the environment. Each behavior is represented by a multidimensional feature set that forms part of a healthcare profile of the care recipient. The at least one hardware processing unit receives the detected datasets from the transceiver and creates a first token including the detected dataset. The non-transitory computer-readable storage medium stores the digital twin token. The digital twin token represents the care recipient's previously uneventful behavior in the environment. The at least one hardware processing unit compares the first token with the digital twin token and aligns the first token with the digital twin token using game theory.

[0011] The alignment of the first token with the digital twin token is achieved through a resulting incentive to at least one stakeholder.

[0012] Aligning the first token with the digital twin token can align the configuration of multiple environmental sensors.

[0013] Game theory can be cooperative and non-cooperative games, standard and scalable games, simultaneous and sequential games, constant-sum, zero-sum and non-zero-sum games, and symmetric and asymmetric games.

[0014] In another embodiment, a non-transitory computer-readable storage medium is encoded with data and instructions. When read by a computer, the instructions cause the computer to receive, via a transceiver, detected datasets from a plurality of environmental sensors. The detected datasets represent behaviors of at least one stakeholder in the environment. Each behavior is represented by a multidimensional feature set that forms part of a healthcare profile of the care recipient. The detected datasets are received from the transceiver, and a first token is created that includes the detected dataset. The non-transitory computer-readable storage medium stores a digital twin token. The digital twin token represents the care recipient's previously uneventful behavior in the environment. At least one hardware processing unit compares the first token with the digital twin token and aligns the first token with the digital twin token using game theory.

[0015] The alignment of the first token with the digital twin token is achieved through a resulting incentive to at least one stakeholder.

[0016] Aligning the first token with the digital twin token can align the configuration of multiple environmental sensors.

[0017] Game theory can be cooperative and non-cooperative games, standard and scalable games, simultaneous and sequential games, constant-sum, zero-sum and non-zero-sum games, and symmetric and asymmetric games.

[0018] For a better understanding of the nature and advantages of the present disclosure, reference should be made to the following description and the accompanying drawings. It should be understood, however, that each figure is provided for illustrative purposes only and is not intended as a definition of the limits of the scope of the present disclosure. Furthermore, as a general rule, unless otherwise clear from the description, when elements in different figures use the same reference numerals, the elements are generally identical or at least similar in function or purpose. [Brief explanation of the drawings]

[0019] [Figure 1] FIG. 1 is a block diagram of the care village system.

[0020] [Figure 2] Figure 2 shows an example of a fraud prediction system (FAPS).

[0021] [Figure 3] Figure 3 is an illustrative example of FAPS, Care Village Digital Twins, and a predictive system.

[0022] [Figure 4] FIG. 4 is a further illustrative example of a care village system.

[0023] [Figure 5] FIG. 5 is a further illustrative example of a care village system.

[0024] [Figure 6] FIG. 6 is an illustrative example of an operating CVDT. DETAILED DESCRIPTION OF THE INVENTION

[0025] Aspects of the present disclosure include systems, devices, and / or methods described herein that use game theory and machine learning to identify and develop manifestations of potential and / or possible incentive misalignments within systems and / or with stakeholders in those systems. This can include incentive misalignments between sets of parties within a system based on their relationships, both as individual actors and / or acting as a group. These manifestations can be defined as a set of systematic, collective, and / or individual misuses of such systems. Using game theory and machine learning, potentially in combination with digital twins, to identify "loopholes" in processes, operations, patterns, stakeholder actions, and / or other care village operations that implicate stakeholders individually and / or collectively (e.g., in collusion) as participants provides a unique approach to the effective, transparent, and trustworthy operation of these systems, including those deployed in care villages.

[0026] One aspect of using game theory and machine learning, at least in part in combination with one or more digital twins, is to identify the balance of incentives representing the interaction of multiple stakeholders. This may include identifying the balance of incentives that are beneficial or detrimental to one and / or group of stakeholders. In some embodiments described herein, this is described as a Fraud Prediction System (FAPS).

[0027] FAPS can be used to identify interfaces and / or friction points and / or process bottlenecks and / or other impediments between stakeholders and / or the systems they interact with, thereby supporting the identification of incentive sets that indicate tampering, abuse, non-compliance and / or other misalignments.

[0028] Using FAPS to provide prediction, prevention, and in conjunction with Care Village's response systems to provide recovery from incentive misalignments, provides a transparent, reliable, robust, and trustworthy operating environment for the Care Village system and its stakeholders.

[0029] A further aspect of the representation of the Care Village's operations is the identification of value and monitoring of the Care Village to establish metrics of value of the product and / or service offering from a multi-stakeholder perspective.

[0030] Value may be expressed in many metric terms, such as money, health, wellness, stress, convenience, satisfaction, etc., and may be considered, recognized, and / or perceived in different terms by the stakeholders involved. Many of the current metrics used as transactional expressions may be inadequate to represent the state and operations of the Care Village and its stakeholders from the perspective of the wellness and care of such stakeholders.

[0031] For example, the quality of life of PUM and possibly other stakeholders may not necessarily be represented via transactional metrics, such as monetary, procedure-based, or other quantifications expressed in transactional terms. For example, in a sharing economy model, such transactional value representations may be insufficient to represent the quality of factors such as review, reputation, and so on.

[0032] Using a multi-incentive model to express value can provide a representation that better reflects the different priorities, optimizations, and / or perspectives of each stakeholder, including via stakeholder interactions, interfaces, and / or touchpoints, as each stakeholder may have different internal and external incentives.

[0033] One aspect in which incentives exist for information manipulation and other fraudulent behavior is through screening, ratings, and / or scores, etc., which can determine reputation indicators for stakeholders of the care village, such as product and / or service providers, and can directly or indirectly transform business opportunities and financial revenues for the stakeholders. Prevention of such fraudulent behavior can be achieved by using FAPS to identify loopholes in the process, by using secure and easily traceable transaction data recording methods such as distributed ledgers, smart contracts, and by using machine learning methods, including methods combined with game theory, to detect fraudulent patterns in actual, predicted, and / or simulated scenarios.

[0034] In some embodiments, stakeholders, such as PUMs, family, friends, or neighbors, can own or share a dashboard representation of the stakeholder's status regarding particular events, services, and / or products with which such stakeholders may interact. Like many other similar dashboards, this can include alerts, messages, confirmations, metrics, records, and other data relevant to the stakeholder.

[0035] In some embodiments, the metrics may be represented by tokens such as NFTs. For example, the use of at least one distributed ledger for recording and tracking transactions and care village stakeholder reputation indicators allows those indicators to be represented as tokens for automated verification and / or possible value exchange.

[0036] Figure 2 illustrates one embodiment of a FAPS. In this example, two stakeholders (101 and 102) have a set of interactions, engagements, and / or activities (109), but in an actual deployment, there may be many stakeholders with many interactions, engagements, activities, etc. Figure 2 shows only two of these for simplicity. The stakeholders (101, 102) are monitored, in whole or in part, by one or more sensors, devices (including worn and / or carried devices), monitoring systems, and / or care processing systems of any form (202). Data sets generated by the configuration and / or operation of such sensors, devices, monitoring, and / or care processing (202) may be integrated, in whole or in part, with a fraud avoidance and prevention system (FAPS), shown herein as 201.

[0037] Such a FAPS may include one or more game management systems (203) incorporating one or more game theory-based games, strategies, rewards, and game players. Such game management systems may interact with one or more machine learning systems, which may be utilized to evaluate one or more games, assist in whole or in part in the selection and / or deployment of one or more games, evaluate one or more strategies, rewards, and / or player actions within such games, and / or act as players, e.g., on behalf of stakeholders, in collaboration with stakeholders, and / or as independent actors within one or more games (204). The operation of the game management systems (203) and / or machine learning systems (204) may be complemented by the use of one or more Care Village Digital Twins (CVDTs) and any predictive systems utilizing such CVDTs (205). The configuration and / or operation of 203, 204, and / or 205 may utilize any form of one or more pattern and / or token analysis systems (206).

[0038] The outcomes of the configurations and / or operations of 202, 203, 204, 205, and / or 206 associated with the interaction (109) of multiple stakeholders (101, 102) may be represented in a decision matrix (207), which may interact with one or more response systems (208). These outcomes, decisions, and / or responses may be part of one or more smart contracts (209) and may be stored in one or more distributed ledgers (210).

[0039] Patterns and Analysis

[0040] The care village comprises a set of stakeholders with various relationships, at least one of which is a health profiler (HCP) and a person under supervision (PUM) with a primary residential environment.

[0041] Care Village operations are primarily based on patterns of behavior, actions, events, and / or other identifiable characteristics. These patterns provide the ability to monitor individuals, PUMs, in a manner that protects their privacy while enabling the support needed to manage their HCPs.

[0042] The representation and / or calculation of incentives as evaluable specifications and / or metrics allows for the formulation of a multivariate representation of the perspective of one or more stakeholders with respect to a particular set of circumstances, including, for example, all relationships with other stakeholders and / or products or services offered by the stakeholders, represented as one or more patterns that may be represented by one or more tokens.

[0043] Stakeholders may have core incentives that apply in almost all situations, while other incentives may vary depending on the situation. Core incentives may be consistent across time, situations, and / or behaviors, including those represented by one or more patterns, while other incentives may be contextual to specific situations and may be represented by additional patterns. For example, a core incentive may be that PUMs prefer to avoid using luxury or expensive products and / or services. In this example, there may be two core incentives: a first core incentive is their preference for less expensive products and / or services, and a second core incentive is their preference for evaluating multiple products and / or services to select an option that satisfies the first core incentive. The combination of these incentives can provide a framework for evaluating stakeholder intent, actions, and / or outcomes. For example, a predictive system utilizing machine learning techniques and potential embodiments as a digital twin may enable prediction of such intent, actions, and / or outcomes to potentially benefit at least one stakeholder and / or the entire care village system.

[0044] In some embodiments, one or more types of feedback can be used to establish, verify, confirm, or identify a stakeholder's intent. This can include the use of games, where stakeholders are presented with alternative response sets and can include their proposed incentives and associated rewards, possibly represented in whole or in part by one or more metrics. For example, rewards can provide tangible or intangible benefits to the stakeholder and thus provide further indication as to the accuracy of their proposed intent.

[0045] In some embodiments, incentives may be explicitly proposed by one or more stakeholders, e.g., expressed as specifications. Incentives may also be determined, at least in part, by the actions of the stakeholders in a set of situations, e.g., behaviors expressed by patterns of eating, sleeping, exercise, etc. In this manner, the proposed or specified incentives may have a quadratic function, e.g., behaviors observed by one or more sensors, devices, and / or systems, where such observations differ at least in part from the proposed and specified incentives.

[0046] FIG. 1 illustrates an embodiment of a care village system in which multiple stakeholders, shown for simplicity in this figure as stakeholder S1 (101) and stakeholder S2 (102), have an interaction set (109) within a care village (108). Such stakeholders may express and / or attribute, derive, predict, or otherwise affect incentives determined by one or more care village systems (110 and 111). Additionally, each stakeholder has a specification set (103 and 104), which may include the stakeholder's HCP. Each stakeholder may primarily reside within an environment (107), which may include one or more sensors and / or a care processing system. Additional sensors, such as sensors worn and / or carried by the stakeholders (105 and 106), may be deployed, including sensors embedded within one or more devices. These devices, sensors, and systems can generate datasets represented by one or more patterns observed during stakeholder interactions with the environment and / or during any form of stakeholder interaction with each other. In some embodiments, this can include the use of a prediction system to predict such patterns, which represent the behavior of those interactions (112). Such predictions and simulations can be performed, for example, by one or more prediction and simulation systems (114) that can interoperate with monitoring and analysis systems (113), which can operate on datasets generated by 105, 106, and / or 107, incentives (111 / 112), specifications (103 / 104), and / or patterns (112). The game management and decision system may interoperate in any manner with care village systems, such as, for example, datasets from 105, 106 and / or 107, incentives and specifications (103 / 104 / 110 / 111), patterns (112), prediction and simulation systems (114) and / or monitoring systems (114).

[0047] In some embodiments, stakeholders may be asked to rate their incentives, for example using a scalar representation, which may then be used as part of an initial framework for establishing the intended behavior of the stakeholders, as represented by their published incentive ratings.

[0048] For example, this approach can involve a simple scalar, such as 1 to 10, or can involve options, such as rating incentive 1 versus incentive 2. For example, a slider with money and stress management as two variables. In this example, stakeholders can set the relative weighting of these incentives. Such scalars, in some embodiments, can be specific to particular behavioral patterns, environments, and / or stakeholder interactions. These interactions may be represented as pattern sets and can form behavior tokens. The use of one or more games based on game theory can be utilized to establish and / or determine these scalars. In some embodiments, stakeholders, as well as AI / ML modules, can act as players in such games.

[0049] These initial scalars may then be compared to predicted behavior using machine learning, e.g., represented in the form of a digital twin (Care Village Digital Twins—CVDT). In some embodiments, games and game theory may be used to determine, at least in part, such predicted behavior. As stakeholder actions, represented at least in part by patterns of stakeholder behavior, which may be represented by one or more behavior tokens, are uncovered, relationships between the stakeholder's declared and / or specified incentives and the stakeholder's actions become apparent and may be incorporated into one or more CVDTs to more accurately predict stakeholder behavior in various situations. One aspect of this is, for example, behavior related to the economic impact of care; for example, a person may want to minimize costs and therefore accept that their care will be suboptimal.

[0050] Based on the operation of the digital twin, the incentive set may be expanded when observed behavior indicates additional incentive structures, such as incentive structures operating across multiple stakeholders. Identification of such incentives may be performed, at least in part, by at least one machine learning technique.

[0051] In some embodiments, one or more games can be deployed, for example, on one or more digital twins, to at least partially determine potential behaviors of one or more stakeholders, including multiple stakeholders. In this manner, behavior probabilities may be determined by utilizing multiple games, which can inform one or more sensors, devices, and / or systems regarding the likely predicted behavior of one or more stakeholders. Such an approach can identify behaviors of multiple stakeholders that may have wellness and care implications or financial or other impacts on one or more stakeholders.

[0052] Such use of one or more games, such as, for example, repeated games, information transfer games, multi-stage games, risk assessment games, and / or games in which a single game is played with multiple digital twins, any of which may have one or more ML / AI modules acting as players in such game, can determine and identify strategies to be utilized by such stakeholders, and result in appropriate incentives for one or more stakeholders whose actions can result in beneficial outcomes.

[0053] In some embodiments, a pattern specification language can be deployed for the creation and management of patterns used within the care village. This can be based on a pattern library containing pattern segments that can be built into pattern sets. For example, a set of event sequences representing data received from at least one sensor, device, and / or system can be instantiated as a pattern segment with specifications for sensor data, time periods, sensor configurations, location data, etc. Such event sequences may be managed, for example, by a care processing system and can form part of at least one pattern. There are many available choices of computer languages ​​in which such specifications can be written.

[0054] These patterns can form behavioral tokens called Beboken individually and / or in any form, which can represent, for example, a time of day (ToD) and / or can represent calm or event behavior represented as a token.

[0055] One aspect of the system is the representation of events, actions, and other activities as sets in the form of patterns. In some embodiments, these patterns can form, in whole or in part, one or more behavioral tokens, described herein as bebokens. This approach reduces the need to individually evaluate each sensor, device, and / or system-generated event, action, and / or activity, because it is the relationships between events, actions, and / or other data sets that form the patterns being evaluated. This approach emphasizes both predictive and responsive system operation, so that appropriate response mechanisms can be invoked if patterns and / or behavioral tokens indicate sufficient deviations from expected, specified, and / or previously established parameters. This operating granularity at the level of patterns and / or tokens and their sets provides an efficient and effective monitoring approach that can support the privacy of one or more stakeholders and inherently enables the detection of misaligned incentives, disruptive activity, fraud, and / or other behaviors that may impact the care village's operations.

[0056] For example, a pattern may be a representation of the state of the PUM and the environment of the PUM, including, for example, any other stakeholders. In some embodiments, each state may be represented, in whole or in part, by one or more patterns.

[0057] Using pattern and / or token evaluation techniques, it is possible to consider those patterns and / or tokens from the perspectives of multiple stakeholders, thereby identifying potential discontinuities and misaligned incentives that may form part of the interaction set between stakeholders. Such evaluations can then be used, for example, in a digital twin with the benefits of machine learning, to evaluate alternative interactions from the multiple perspectives of different stakeholders and identify potential optimizations for those interactions. Such approaches can also be used to evaluate the relative incentives that one or more stakeholders may have in the interaction set and identify any behavior that is detrimental to another stakeholder.

[0058] One aspect of the system is the use of patterns and / or tokens for multiple stakeholders, which may be based on templates, for example, and represent a common set of behaviors for the stakeholders, but may have different outcomes for each stakeholder. These different outcomes may depend on inputs, environments, and / or outputs within the same pattern framework.

[0059] Patterns can be used to identify, discover, predict, and represent friction, tension, and / or discontinuities in outcomes and / or responses to multiple parties, as represented by these patterns and / or tokens throughout their interactions, thereby providing a systematic approach to avoiding or mitigating such situations. When there are unavoidable interactions where the incentives of the parties are not sufficiently aligned, for example, regarding the pricing of a product or service, discontinuities in this alignment can form a focus for identifying activities where at least one party is likely to manipulate or cheat the situation. This can be when one party takes advantage of another, for example, when one party has a health condition that reduces mental functioning and the other party attempts to exploit the situation for their own gain. This can include identifying pattern and / or token interactions that exhibit predatory behavioral characteristics.

[0060] One aspect used in evaluating patterns and / or tokens is the evaluation of outcomes and / or responses. For example, an outcome or response can be determined to be neutral if each of the parties involved in the pattern and / or token has an outcome or response that results in no or negligible loss or gain to the pattern and / or token as determined by the parameters of the pattern and / or token. In some embodiments, this can be evaluated, at least in part, through one or more games in which the parties involved are players. In instances in which at least one party benefits at the expense of another party, such outcome or response can be evaluated with respect to specified incentives for each party to ascertain whether it represents that party's intent to manipulate or exploit the system for their own benefit, often to the detriment of another party, such as a participant in the game.

[0061] These outcome and response measures may represent at least one party's representation of the degree to which their expectations and predictions are aligned with those outcomes. These alignments may be expressed as metrics including degrees of satisfaction, wellness and care, stress, etc., and may involve the integration of feedback provided by at least one party, although generally such feedback may be provided by multiple parties, including the system.

[0062] 3 illustrates an exemplary deployment of a FAPS including one or more CVDT and predictive systems. In this example, data is input from one or more sensors, devices, and / or systems involved in monitoring PUM in an environment (301). This data is ingested by one or more interfaces (302), and, for example, one or more matching systems may be utilized (303) to match, in whole or in part, this data to one or more patterns, such as patterns managed by one or more pattern state management systems (305). These patterns and / or data sets may be compared and evaluated in terms of a specification of an incentive set (304), which may be either explicit or implicit and which at least partially correlate with the patterns in the pattern state management (305). One or more CVDTs (306) can use a set of states for each pattern, e.g., pattern (P1) can exist at time (t0), and the CVDT prediction system can repeat that pattern as a representation of a set of state changes over a period of time, where the period is represented by increments of n (n1, n2, n3), represented by corresponding patterns, e.g., N1, N2, N3, N4, etc. In this way, the CVDT and prediction system can provide insight into potential changes in state and the patterns corresponding to those state changes that the PUM may experience.

[0063] These predicted state changes and corresponding patterns, as well as stakeholder incentive specifications (304), may be communicated to the game management system (203), and games that represent these states and / or patterns and their predicted changes, in whole or in part, may be evaluated with respect to one or more games that represent these states, patterns, and / or variations. The operation of the game management system (203), CVDT and prediction (306), and / or matching system (303) may be integrated with a machine learning / artificial intelligence module (307) to apply one or more suitable machine learning techniques in any form. In some embodiments, the game theory system (203) and machine learning / artificial intelligence module may be part of an embodiment of the FAPS (201).

[0064] The outcomes and / or results of these assessments and / or processing may be communicated to one or more response management systems (309) and / or repositories (310), which may also receive response outcomes. In some embodiments, the response management system may communicate one or more responses as actions (311), such as configuration data sets, specifications and / or instructions, data or other actions, etc., to one or more PUMs, sensors, devices and / or systems in any form, and / or to one or more stakeholders.

[0065] Such an approach can provide the ability to identify optimal sequences of multi-party outcomes, reporting, feedback, and / or verification, or other metrics. In this manner, patterns and their outcomes, as well as sets of sequences containing patterns that indicate activity detrimental to at least one stakeholder, can be identified. Such identification can provide and validate degrees of compliance or non-compliance, so that the degree of variation can be assessed and necessary corrective or other actions can be taken.

[0066] Part of this approach may lead to the discovery of stakeholder patterns, such as through the use of machine learning techniques, in which patterns identify stakeholder behavior. In instances where there are patterns that represent repetitive stakeholder behavior to gain advantage or abuse the system at the expense of another stakeholder and / or the system itself, for example through non-compliance with system specifications, such stakeholder may be restricted and / or limited in their access to one or more systems, stakeholders, or other care village entities.

[0067] These stakeholder patterns and / or tokens can provide an effective approach to determining those collaborations between multiple parties that exploit, disrupt, and / or create incentive misalignment through the actions of these multiple stakeholders, such as whether a product and / or service provider collaborates with a product and / or service recipient to charge another stakeholder for the cost of providing the product and / or service without actually providing the product and / or service.

[0068] One aspect of the system is the use of incentive specifications, e.g., in combination with game theory and / or machine learning, to enable the prediction of incentive misalignments and / or behavioral deviations that may lead stakeholders to attempt to circumvent compliance with at least one set of specifications.

[0069] The use of one or more games to represent stakeholder incentives aids in identifying strategies utilized by such stakeholders within the games representing those stakeholders' incentives. For example, a game with alternative outcomes, e.g., health versus money, can be used to establish the relative weighting of one or more stakeholders' incentives in a set of situations. The use of variable rewards can further increase or decrease the variability in the weightings representing stakeholder incentives. This approach, in some embodiments, can include multiple games with different strategies that can be played by one or more machine learning modules acting as proxies for one or more stakeholders. When combined with a digital twin, such games, strategies, rewards, and / or outcomes can be evaluated to at least partially determine the incentives of one or more stakeholders, including those not explicitly represented by such stakeholders.

[0070] Evaluation of behavioral variations supports identification of at least one intent behind such variations and / or deviations, particularly if such variations create a pattern that may be represented in whole or in part by one or more tokens, that is likely focused on fraud and / or compliance evasion. The use of sensors, devices, and / or systems to monitor the operating patterns and / or activity of one or more stakeholders within the context of an HCP can provide an early warning and / or trigger for the configuration of that sensor and / or other sensors, devices, and / or systems to verify such variations and deviations. Depending on the degree to which this occurs, at least one system response can be generated and / or at least one process can be invoked to monitor such variations and / or deviations over a period of time to detect patterns indicative of changes in the stakeholder's behavior relative to the system and / or another stakeholder.

[0071] In some embodiments, stakeholders may determine the terms of their services, expressed, for example, as specifications forming smart contracts. These contracts may be evaluated to establish the incentives underlying these specifications, which may then be incorporated into a monitoring system. Within such smart contracts, the care village system may operate to provide limits on variability and / or deviation opportunities.

[0072] FIG. 4 illustrates an example of a care village system including a predictive system. For example, a set of data (401), such as a set of data generated by one or more sensors, devices, and / or systems involved in monitoring one or more PUMs and / or other stakeholders in one or more environments, may be ingested into a machine learning / artificial intelligence module (307). In this example, one or more CVDTs (409) may be utilized by the predictive system, which may include one or more machine learning techniques incorporated into the machine learning / artificial intelligence module (307), to create one or more simulations of the behavior, state, pattern, interaction, incentive, specification, compliance, and / or other characteristics of the monitored entities. These predictive systems and / or simulations may operate on and / or in conjunction with one or more sets of specifications, for example, managed by a specification module (402). In this example, such specifications may include stakeholder specifications (403), pattern specifications (404), and / or incentive specifications (405), the latter being specifications of one or more stakeholders. These specifications (402) and the incoming data set (401) may be represented by one or more states managed by a state management system (406), which may have various weightings, parameters, priorities and / or other attributes, such as attributes that may be used by the CVDT prediction and simulation system for calculations and / or processing, managed by an attribute module (407). This may include a set of attributes from other similar PUMs and / or stakeholders that are similar to the attributes represented by the incoming data (401). Such attributes may be derived from other care village operations and may be anonymized.

[0073] The CVDT predicted state management module (408) can maintain probable states of one or more situations, including commonly occurring states as well as states representing the most likely states, based at least in part on the dataset. These predictions can then form part of one or more games managed by a game management system (410), thereby generating one or more decision matrices that at least in part inform a decision engine (411). The output of this engine (411) can be communicated to one or more response management systems (309), which can then generate one or more actions (412) that can be communicated to any form of sensor, device, system, and / or stakeholder.

[0074] Game theory

[0075] In many cases, the majority of operations in and by the sensors, devices, and / or systems of a care village can be represented in the form of structured interactions with one or more stakeholders in the care village according to at least one set of specifications. These specifications, in some embodiments, can be characterized as a game, involving patterns of interactions acting as representations of the behavior of stakeholders in the care village. This can include interactions that occur physically in real time and can include one or more digital representations in the form of one or more digital twins. In some embodiments, including many situations, the alignment of stakeholder incentives can be represented as having an equilibrium in the sense of using game theory. Furthermore, the underlying dynamics of a game representing such incentives and the actions and events therefrom can represent stakeholder expectations and / or the sensors, devices, and / or systems that engage in such activities.

[0076] For example, if the set of incentives and / or interactions of a set of stakeholders in a care village can be represented as a finite non-zero-sum non-cooperative game, then there exists a Nash equilibrium involving mixed strategies of that set of stakeholders.

[0077] While much research has been done in applying game theory to economics, its application to care villages involving multiple stakeholders with economic and / or health and wellness outcomes presents a new and unique approach to avoiding, mitigating, and / or reducing misconduct, fraud, or other harmful behaviors, including economic-based and / or health and wellness-based misalignments.

[0078] In many stakeholder interactions, what one stakeholder believes about the actions and intentions of another stakeholder, such as the PUM and the PUM's doctor, will significantly influence his incentives and actions with respect to those stakeholders. In this example, a game that can represent such a situation is a version of the Ultimatum Game or Dictator Game, with the doctor playing the role of dictator. The exchange of value traded in the game is the PUM's health, which includes some variant of that PUM's quality of life. For example, a particular medication for treating one aspect of the PUM's health may have other effects that reduce the PUM's quality of life.

[0079] Within a care village, the interaction set is finite and somewhat predictable, but the strategies deployed by different stakeholders pursuing their own incentives can vary considerably.

[0080] One aspect of the system is the evaluation of HCPs and patterns within HCPs, which may be represented by one or more tokens, to establish which games suitable for game theory may be applicable. This includes establishing incentives and events, including their sequences, within a pattern and / or set of patterns, so that suitable games, including one or more strategies, can be evaluated to represent those incentives, events, actions, and / or outcomes of those patterns, including their representation by one or more tokens.

[0081] In some embodiments, this can include a matching system whereby a set of games is maintained in a repository, and the matching system evaluates HCPs and patterns within HCPs, both previous and / or predicted behaviors, to determine which games are appropriate representations of those patterns that can be represented by tokens. This can include multiple games representing a single set of patterns and / or tokens, e.g., these games are run within one or more digital twins, and outcomes of these games are matched with real-world activities to establish optimal matches. These matches can then be recorded and used in further matching operations, e.g., so that a pattern and / or token or set thereof can have an associated game deployed when certain criteria, such as one or more stakeholder types, classes and / or other classifications, HCPs, pattern sequences, sets of tokens, etc., are met. In some embodiments, machine learning can be applied to support such a matching system, e.g., to identify appropriate classifications, matching criteria, etc.

[0082] In situations where existing games do not achieve a sufficient match with the pattern and / or token or set being evaluated, the system can deploy elements of the game, such as payout matrices, relative risk / reward values ​​and / or weightings, whether the game is zero-sum or non-zero-sum, types of strategies deployed by stakeholders, decision matrix types, etc., to establish a framework for a new game, which can then be deployed and stored in one or more repositories.

[0083] These new game frameworks may then be compared to existing games held in the repository and, where applicable, modified or adjusted to fit those games. Because interactions between stakeholders are generally well understood within a care village, most situations are likely to be addressed by this approach. However, as new games are identified, they are added to the game repository. Such an approach creates an adaptive approach to identifying new potential games and strategies therein that are likely to evolve as the operation of the care village and the stakeholders within it operate over time.

[0084] A further aspect uses machine learning and digital twins to evaluate potential games to partially identify the effectiveness of one or more equilibrium games. This can include machine learning modules representing players, e.g., stakeholders, where potential strategies of the stakeholders can be deployed, each with different incentives. For example, if an incentive is to deceive another stakeholder, such as by billing insurance companies for products or services that were not provided, this can be done, at least in part, with one or more digital twins to identify appropriate games for identifying such incentives and applicable strategies that the stakeholders can employ to achieve this outcome. In this way, the system can identify actions of one or more stakeholders that are inconsistent with the care village system. The same approach can be used to identify other behaviors of one or more stakeholders involved, such as altruistic behavior, tit-for-tat, value exchange, etc.

[0085] In some embodiments, much of this evaluation by the matching system may be performed using a best-fit approach, but may also be performed using other statistical methods including, for example, fuzzy logic and / or other probabilistic, combinatorial, and / or machine learning techniques.

[0086] Many of these games may be used in training at least one machine learning technique, whereby an existing corpus may be used for the machine learning technique and / or the games may be used to create such a corpus.

[0087] One aspect of the use of games and game theory is the alignment of game parameters, weightings, risks, rewards, and decision matrix elements with incentives of and for stakeholders.

[0088] Such games may be deployed across sensors, devices, systems, and / or infrastructure to incorporate one or more games, where such games can inform the operation of such sensors, devices, systems, and / or infrastructure, for example, via an appropriate decision matrix, one or more outcomes, one or more strategies, and / or one or more equilibria. In some of these deployments, the decision matrix may be expressed as an outcome, and thus the sensors, devices, systems, and / or infrastructure may adopt different strategies to achieve those outcomes in whole or in part. For example, this may include the configuration of such sensors, devices, systems, and / or infrastructure, and the outcome of such a deployed game is the configuration of the set of sensors and / or other devices with relationships to those sensors, devices, infrastructure, and / or systems operating such games. For example, a set of sensors with a care processing system may deploy a particular combination of sensor data and algorithmic processing to create a dataset that at least partially conforms to such a decision matrix.

[0089] Such an approach may include one or more communication devices capable of providing messages to one or more stakeholders, such that the decision-making matrix of a particular game in which the communication devices are participants may be influenced by actions, decisions, and / or messages from the stakeholders. For example, a stakeholder may receive a communication from a set of sensors, devices, and / or systems that indicates a potential adverse wellness event occurring to the PUM, and the stakeholder may then configure the set of sensors to send a message, e.g., to monitor such event or to alert another stakeholder.

[0090] In another example, a potential misalignment of incentives may be represented by a game outcome, e.g., in a decision matrix. For example, this state may be communicated to stakeholders with one or more messages potentially prompting or suggesting solutions, e.g., actions, for one or more such imbalances, so that stakeholders, including through their proxies, can take action in response to such imbalances.

[0091] In some situations, a game representing the revealing events of one or more stakeholders, potentially including sensing and care process sensors, devices, and / or systems, can be deployed in the digital twin, and if these revealing events, including strategies deployed by one or more stakeholders and / or sensors, devices, and / or systems, no longer match the current game parameters, this may indicate that at least one of the stakeholders, sensors, devices, systems, care processes, or other system elements is not in compliance with specifications and / or has deployed a previously unidentified strategy, which may indicate that such a situation represents a malicious or harmful misalignment of incentives, such as when a stakeholder is cheating the system and / or a fault or error in the operation of the system itself.

[0092] Often, these assessments can be performed by one or more monitoring systems, including monitoring systems that utilize one or more digital twins, which can include human intervention, assistance from machine learning modules, and / or further monitoring as needed.

[0093] In some embodiments, the game creation, matching, and rating system includes: -Running game parameters Establishing the running game as part of the hierarchy -Notification of the option to switch to another game Further game call information

[0094] Game System

[0095] In some embodiments, there may be a game management system that can work in conjunction with other system modules to determine appropriate games to be deployed and the associated tiers and / or selection criteria for such games. This can encompass both operating games deployed in real time and operating games running on digital twins to evaluate both past and future operations. This can also include the selection of one or more machine learning techniques to be deployed for training and / or deployment of operations.

[0096] In some embodiments, sensors, devices, and / or systems may include at least one game management system and / or components thereof, which may be integrated with such sensors, devices, and / or systems and / or accessible to the sensors, devices, and / or systems via one or more communication networks. A care village system infrastructure component may have access to one or more game management systems and / or components thereof and / or one or more sets of games. For example, a care processing system may have access to a set of games to at least partially determine which strategies are being utilized by stakeholders in certain situations and determine which care processing elements best fit those situations.

[0097] In some embodiments, a gaming system may be provisioned to support collaboration with and between the underlying operating environments of one or more sensors, devices, and / or systems. For example, collaborative gaming may be utilized to align the configuration of one or more sensors, devices, and / or systems with other sensors, devices, and / or systems and / or care processes and / or systems and / or infrastructure to provide additional data regarding potential, predicted, actual, and / or previous misalignments of incentives. For example, if a game running on a digital twin representing a stakeholder's interaction with another stakeholder produces an outcome that suggests a misalignment, e.g., via a game outcome, another sensor, device, and / or system may be invoked by the collaborative game to determine whether such a misalignment has occurred.

[0098] 5 illustrates an exemplary embodiment of a game management system (410) that can process datasets received from one or more stakeholder (504) interactions (505), which may be represented by one or more bebokens and / or patterns (503), and may include one or more incentive specifications (506), in conjunction with one or more machine learning modules (505) and / or one or more CVDTs (507) in combination with a game identification system (502). The game management system (410) can use one or more repositories (501) to store and manage games, strategies, frameworks, tokens (including bebokens), incentive specifications, stakeholder specifications, patterns, players, and / or rewards, as well as any other related data in any form.

[0099] In some embodiments, the game management system and its games may differ in terms of machine-to-machine, e.g., device-to-device, sensor-to-sensor, and other interactions, from those utilized in, for example, machine-to-human and human-to-human interactions involving human stakeholders.

[0100] The range of games utilized in the machine-to-machine realm can include cooperative and non-cooperative games, the outcomes of which may be used to evaluate patterns involving tokens, actions, and / or events of sets of stakeholders to assess any incentive misalignment. Each type of game may be deployed in any form to suit different situations and interactions between stakeholders, systems, devices, sensors, and / or other care village entities. These may include, for example, cooperative and non-cooperative games, standard and scalable games, simultaneous and sequential games, constant-sum games, zero-sum games, and non-zero-sum games, and symmetric and asymmetric games, and may be deployed in any form. The objectives of the sensors, devices, and / or systems may be determined at least in part by the configuration of the sensors, devices, and / or systems, for example, as controlled by the care processing system, and may be further influenced by the objectives of both the sensors, devices, and / or systems and / or additional game specifications (including, for example, the deployment of different games for different situations, maintained in memory and / or available repositories of such sensors, devices, and / or systems).

[0101] Figure 6 illustrates a set of operating games (605), with one or more stakeholders (607) as players within such games. These game players may include ML / AI modules (601) acting as proxies for one or more stakeholders and / or ML / SI modules (602) acting as independent players. These players may use one or more Bebokens, which represent, at least in part, one or more patterns (609), as part of the operating games. Interactions (606) between the stakeholders (607), stakeholder proxies (601), and any ML / AI players (602) may be influenced, in whole or in part, by stakeholder incentive specifications (608). These operating games may be instantiated and / or supported by a game management system (613) including a game identification system (612), which may evaluate interactions (606) and / or players (601, 607, 602), behaviors and / or patterns (609), and / or optional specifications (608) to identify appropriate games for operation. This evaluation and game management system operation may be supported by one or more machine learning modules (603), one or more CVDT and / or prediction systems (604), one or more repositories (610), and / or one or more smart contracts / distributed ledgers (611).

[0102] Example

[0103] One aspect of the system is the determination and representation of a range of potential rewards represented as outcomes of a set of games and / or in the form of a decision matrix. This may include the use of tokens. A mapping of a set of incentives associated with and / or involved in a set of patterns that may be represented by tokens, and a representation of stakeholders as a set of games initially encompassing their interactions within a set of intended and possible interactions, may be formed in a repository. While this repository initially represents typical interactions within the care village, unexpected combinations, actions, events, and / or outcomes may exist from and within these interactions, and thus such a game management module supports an adaptive and extensible repository management system, which may include both machine and human operation as needed. Such repositories may be distributed across cloud, edge, and other distributed systems.

[0104] The use of a hierarchy of games may enable a care village system to manage and launch multiple games and game systems, helping to identify, avoid, and / or reduce incentive misalignments and / or conflicts, including, for example, location, time, and / or resources. One aspect of this is to enable effective coordination and cooperation between the care village system, stakeholders, and / or sensors, devices, and / or systems. For example, improving, optimizing, and / or managing the deployment, use, and / or utility of resources in pursuit of specifications, compliance, and / or other objectives may be enhanced by such an approach.

[0105] In some situations, the relative relationships among stakeholders in a care village may be asymmetric, in that some stakeholders have significantly more influence than others. This can be represented in each party's incentives, for example, using a weighting scheme, and further represented using asymmetric games and related strategies. Asymmetric games, such as the Princess Bride game, can have different, typically opposing, incentives for different players. These differences may be emphasized by varying rewards for different players. This is particularly useful when applied to different populations, such as product and / or service providers and consumers or users. Such an approach can incorporate evolutionary dynamics, whereby players' use of mixed strategies can lead to an equilibrium representing a stable state, e.g., a tranquil state represented by one or more tokens.

[0106] The use of evolutionary dynamics as a predictive method is well suited to the deployment of machine learning, and iterations of the scalable game may be played with multiple probabilities for rewards for one or more players, and can indicate strategies and / or situations where misaligned incentives are likely to produce activity by one or more stakeholders that abuses the care village system.

[0107] In some embodiments, the application of stable matching problems may be incorporated, at least in part, as a technique for addressing the issue of stakeholders with different incentives in and for stakeholder interactions. This approach can be deployed to create incentive matches, so that predicted interactions can be evaluated in light of actual interactions and detected misalignments. Furthermore, this approach can be used to determine such potential misalignments, and corrective action can be taken before the actual interaction with the intent of reducing and / or potentially eliminating such misalignments. For example, if a stakeholder has incentive A with a value of N, and another stakeholder with whom that stakeholder interacts has an equivalent incentive, including a direct equivalent, with a value that is a fraction of N, such misalignment can be avoided, diffused, or mitigated by offering each stakeholder a different value for those incentives, either in terms of money, time, effort, or other characteristics.

[0108] In some embodiments, the incentive specification may include metrics that relate individual incentives to a set, such as a continuum, dimension, sequence, or other scalar. For example, a metric on a 1-10 scale may be utilized, with the axis of the metric representing financial cost at one end and time to service at the other, e.g., financial represented as 1 and time to service represented as 10, and this may be communicated to stakeholders as a slider on a scalar, where the stakeholders select a value that represents the relative importance of the two incentives relative to one another. This may be expanded to include a number of incentives, each of which may be considered, at least in part, a dimension for which the stakeholder can select at least one value. These may form multi-dimensional metrics in any form.

[0109] The relative value of incentives may be changed so that stakeholders who assign high value to one incentive may be persuaded to change such value in light of other incentives that another stakeholder may offer.

[0110] For example, stakeholders may have cost incentives that are orthogonal, opposed, conflicting, and / or contradictory to service quality incentives, and to avoid inconsistencies, stakeholders and / or service providers may need to adjust the value attached to these incentives in a particular instance of service provision. Such values ​​and forms of incentives may then be stored and, in some embodiments, may be made, at least in part, part of at least one smart contract.

[0111] Using such an approach allows for the distribution of work, money, effort, wellness outcomes, and / or other decisions within the system to be perceived as fair, resulting in less overall motivation for stakeholders to feel mistreated, disadvantaged, or otherwise under-benefited from care village operations.

[0112] Games may be deployed locally and / or remotely based on device capabilities in response to any form of device communication.

[0113] Machine Learning

[0114] In some embodiments, the combination of machine learning and game theory for the identification of patterns, incentives for and for those patterns, potential and realized strategies of stakeholders towards each other, and patterns and discovery of other indicators and / or metrics enables prediction of potential situations that may require intervention and / or response by the system, another stakeholder, emergency services, and / or other parties involved in the delivery of wellness services, including at least one decision management system interacting with such response and intervention system.

[0115] In some cases, this may include deploying configuration data to one or more sensors, devices, and / or systems to more fully monitor the situation and, for example, alert human response teams and / or automated response systems.

[0116] For example, GANs (generative adversarial networks) may be trained according to the principles of game theory, in that players may be generative or discriminative, and each approach is applied to a corpus represented in part by sensor, device, and / or system data and operating patterns, and the corpus may be represented by one or more tokens with different but complementary objectives.

[0117] Another approach involves using multi-agent techniques such as multi-agent reinforcement learning or similar techniques, where each agent is a player in one or more games and evolves strategies to improve its performance as it moves towards equilibrium.

[0118] Adversarial training of neural networks can also be used, where different adversarial techniques are applied to players, represented as players in one or more games, with the aim of improving their strategies.

[0119] Using game theory with machine learning involves translating machine learning properties into properties deployed in game theory. For example, when deploying fair values ​​to various incentives to stakeholders, such as in Shapley and similar algorithms, each of the machine learning functions, its ranking, and target value ranges can be correlated to players, strategies, and outcomes, for example.

[0120] One aspect of this is, for example, the relative marginal cost of changing the value attached to an incentive or set thereof by a stakeholder. Machine learning can operate on a broad or selected corpus, including, for example, a selection or combination of incentives, patterns, sensor data, events, environmental attributes, and / or other care village data sets. When stakeholders can converge on the value attached to their incentives and the incentives of the stakeholders are the same or sufficiently similar, the relative incentive for either stakeholder to attempt to disadvantage another, cheat the system, or otherwise behave counter to care village operations is significantly reduced. Furthermore, if a stakeholder continues to operate in a manner that systematically and continuously exploits other stakeholders and / or the care village system, that stakeholder may have their access to the care village reduced or revoked.

[0121] One aspect is the identification of stakeholder incentives with direct interactions between stakeholders, which in some embodiments may be instantiated as a graph, e.g., stakeholder incentives, which may be considered variables in this context, may directly interact and be represented by edges, while incentives that indirectly interact may depend on the value of other incentives and may therefore be considered conditionally independent.

[0122] In some embodiments, predicting incentive misalignment may utilize one or more machine learning models. For example, in some embodiments, this process of predicting the likelihood of an incentive misalignment occurring at an early stage may include utilizing one or more machine learning models optimized to detect early variations from one or more patterns, including patterns represented by tokens representing the behavior and / or interactions of one or more stakeholders. These models may be trained using broad datasets encompassing a range of data sources. These sources may include sensor data, which may provide insight into the environment and contextual variables, as well as stakeholder behavior data. These datasets are curated to capture relevant patterns and correlations that may signify the emergence of incentive misalignment.

[0123] The training data is further augmented with past instances of incentive misalignment, enabling the machine learning model to identify significant patterns that indicate the presence of misalignment. Through a process of iterative learning, these models become adept at recognizing subtle indicators that might otherwise go unnoticed by a human observer. By analyzing the interactions between sensor data, stakeholder behavior, and past instances of misalignment, the machine learning model develops a sophisticated understanding of potential risk factors associated with incentive misalignment.

[0124] This may be defined as distinguishing between each of these representations and may be part of determining dependencies between incentives and / or sets thereof in both directed and undirected representations. One aspect of stakeholder interaction is the stakeholders' declared and undisclosed values ​​with respect to incentives. Not all stakeholders can disclose all of their incentives or their values, and therefore the system can calculate sets of incentives and values ​​that may be granted to stakeholders in different circumstances. In this case, the determination of the declared and undisclosed relationships between incentives and their values ​​may be represented, for example, by a graph, which may then be used to evaluate any incentive misalignment. This identification of interdependencies may be used to assist stakeholders in evaluating trade-offs, strategies, rewards, and any other outcomes based at least in part on the alignment of incentives and any representation of those incentives within the context of the application of game theory.

[0125] In some embodiments, the system provides a graphical representation of these variables within a game framework, allowing stakeholders to operate on these incentives and their values ​​in a simplified manner for the benefit of the stakeholders.

[0126] In some embodiments, the feature extraction techniques utilized by the machine learning techniques may be configured to support machine-to-machine and machine-to-human communication.

[0127] One aspect of this approach is determining optimal actions to address detected and / or predicted misalignments. For example, in some embodiments, once an incentive misalignment or the risk of its occurrence is identified, machine learning models can be used to determine appropriate actions to mitigate or eliminate the incentive misalignment, its risk, and / or its impact. From comprehensive datasets containing records of various incentive misalignments and subsequent actions taken, these models can predict the outcomes of different response strategies. The learning process involves understanding how specific actions interact with particular types of misalignments and how these actions affect stakeholder behavior and system dynamics.

[0128] For example, machine learning models can be adept at recommending the most appropriate actions based on the unique context of each situation. This proactive decision-making approach can help address emerging misalignments quickly, minimize their negative impact, and foster an environment of alignment and collaboration among stakeholders.

[0129] The risk calculation may, in some embodiments, be determined through an evaluation of a payoff matrix of a set of games representing the interactions of a set of stakeholders, and if there are dominant strategies, such strategies can be evaluated with respect to the incentives of the stakeholders to identify the incentive values ​​that are most likely to cause misalignment.

[0130] Risk may be determined in part by an evaluation of incentive values ​​associated with a reward matrix, which may inform stakeholder decision-making. Such risk calculations may be expressed in terms of the system, one or more stakeholders, devices, sensors, or other system entities. In this manner, such risk calculations may then form the basis of further games, where the reward matrix may be used, in whole or in part, as the basis for a decision-making matrix to enable risk management strategies that mitigate incentive misalignment.

[0131] In some situations, when a misalignment of incentives is identified using, for example, a predictive digital twin such as Care Village Digital Twins (CVDT), a message can be sent to at least one stakeholder involved in such misalignment to alert them to the situation. In this way, such a message can prompt the stakeholder to adapt their behavior to mitigate or eliminate the misalignment and / or prompt the involved stakeholder to modify their actions from a communication perspective.

[0132] The pattern may be represented by a game, including multiple strategies within the game that represent the pattern, and the "distance" of the measurement from the boundary indicates the direction (vector) of the trajectory from compliance to non-compliance.

[0133] One aspect of a machine learning system is the determination of the rate of change of at least one dataset, e.g., if a pattern is operational, the pattern represents the initialization of a dataset to be used as a corpus for applying machine learning techniques. For example, an operating CVDT may have a dataset (X) provided by devices, sensors, care processes, and / or other care village systems. Machine learning may use various techniques, including inference, deep learning, and various instantiations of Boltzmann machines, to at least partially determine the maximum value of a portion or the entire dataset with respect to one or more axes, variables, or other portions of the data. Such predictive operations may then be used, at least in part, to configure one or more sensors, devices, and / or systems and / or other data providing and / or processing systems. These operations may also be used to establish endpoints for a set of interactions, for example, as represented in a decision matrix generated as a result of one or more games, whereby the incentives and their values, as well as the strategies deployed within the games and the final rewards forming such decision matrix, may be evaluated to determine any misalignment of these incentives and values, where a misalignment indicates that at least one stakeholder is attempting to exploit the care village system and / or stakeholders, or to disadvantage another stakeholder in a manner contrary to the care village's operations. Because it is impractical to attempt and define potential stakeholder interactions using a classical ruleset approach, especially in situations involving three or more stakeholders, the use of game theory and machine learning enables the ability to identify potential incentive misalignments, which can then inform and / or configure at least one care village response system before, during, or after such misalignments to prevent, reduce, mitigate, or correct such misalignments.

[0134] In some embodiments, adaptive (including dynamically adjusting) sets of boundary conditions and / or constraints can be deployed, with such conditions and constraints identified at least in part through machine learning techniques. This can include identifying causal possibilities / probabilities regarding relationships between devices, sensors, systems, stakeholders, games, and the datasets they generate. This can include identifying new variables, weighting those variables, knowledge-based conditions, contextual variations, matching, etc. Some benefits of this approach are the mitigation of unexpected use cases, including those based on incentive alignment and weighting.

[0135] One aspect of Care Village's deployment of the machine learning system can include generative AI for enhanced messaging for one or more actions.

[0136] In some embodiments, the task of correcting, mitigating, or preventing identified incentive misalignments or their potential risks can be delegated to an automated system. This system can utilize a variety of strategies, including communication with relevant stakeholders. The goal of these interactions is to effect changes in stakeholder behavior that are consistent with desired outcomes. Communication can take a variety of forms, from one-way communication to interactive dialogue using one or more communication channels.

[0137] To optimize these communications efforts, generative AI models can help. These models generate content tailored to the specific context and stakeholders involved. By analyzing past communication patterns, stakeholder preferences, and desired objectives, generative AI can generate nuanced, informative, and / or persuasive messages. These messages can be strategically designed to resonate with stakeholders, encourage collaboration, and foster a shared understanding of the importance of alignment, and can be focused on shared benefits and / or the stakeholders' own interests.

[0138] compliance

[0139] One aspect of the system is the ability to assess and / or track stakeholder compliance with a specification representing that compliance. For example, if a stakeholder, such as a caregiver, is contracted to respond to a PUM at a certain location within a specified time frame, one or more sensors, devices, and / or systems may be instructed to provide data within that time frame regarding whether or not the contracted stakeholder's response can be confirmed. This approach can involve "hard" data, such as proximity connections, where the PUM has a device and the caregiver has a device that creates an event data stream upon proximity that confirms the contracted period. Additional sensors, such as entry / exit sensors, cameras, and motion detectors, can then provide additional data sets against which the behavior of both the PUM and the caregiver can be matched with activity patterns representing their interactions. In this example, the PUM and the caregiver can each have distinct patterns, or both can have common or shared patterns. Such patterns can be represented by one or more tokens. In this way, the degree of deviation from any specified event or occurrence can be considered within the context of these pattern interactions, thus representing a much more flexible and adaptable monitoring system compared to rule-based systems. This monitoring may be performed using a digital twin that includes a set of digital twins, for example, an environment represented by a digital twin, and stakeholders, both PUM and caregiver, also represented by digital twins, whereby there is a combination of digital twins in which data from sensors of the environment, PUM, and caregiver are combined into patterns to create a unified representation of this interaction. Such interactions may be hosted, for example, in a manner where a monitoring function monitors the combined set of patterns. Such a monitoring system or module may be configured to generate alerts or events according to compliance specifications, such that exceptions can be generated if data does not match expected, configured, and / or specified compliance parameters.

[0140] These exceptions may be passed to an exception handling system, which can then take appropriate corrective action as necessary and / or provide appropriate reports to relevant systems, individuals and / or other controlling entities.

[0141] Identifying and detecting activities with deviations from agreed-upon terms, specifications, and / or behavioral norms can be useful for tracking and managing changing situations, for example, caused by fluctuations in the health of the PUM and / or by more gradual changes in the behavior of a set of stakeholders over time. In this way, gradual variations likely to occur as stakeholders interact and relative changes in incentives for and to those stakeholders can be identified, and responses can be initiated as needed. This allows for the gradual and periodic deployment of corrective responses, such as changes in incentives for stakeholders. For example, a change in appointment time, an increase in payment, a decrease in workflow, or one or more other incentives may affect the overall balance of incentives for a set of stakeholders before a change in behavior, including more significant impacts, outcomes, or system abuse, can be avoided. This tendency for minor deviations and corrections to occur in any given set of interactions may often be gradual over multiple iterations, and therefore any responses and / or corrective actions may similarly be implemented in a series of incremental iterations.

[0142] In some embodiments, specifications for compliance may be determined and expressed in terms of incentive alignment; for example, a care village system may present a set of specifications that represent each stakeholder's incentives from the care village's perspective. Each stakeholder may also express its own incentives and represent them as part of its own specification, which may be represented at least in part by its digital twin. Although each party's incentive specifications, as expressed by those stakeholders, may represent different and potentially conflicting values, these specifications may form the basis for each party's compliance as they interact.

[0143] Such compliance may be assessed, for example using a matching system to establish compliance of events, including sequences of events, and any actions, with such specifications.

[0144] One aspect of incentive analysis operations is the ability to configure sensors, devices, and / or systems, or sets thereof, to provide additional data about an event or sequence of events to provide more detailed, focused, advanced, and / or additional sensing capabilities, for example, via an environmental sensing management system and / or a care processing system. Such an approach can be used to determine whether an event is a single event within a context, such as forgetfulness, a response to a stakeholder request or other minor fluctuation, or an attempt by a stakeholder to exploit or manipulate the situation in a way that is beneficial to one stakeholder and harmful to another stakeholder and / or the care village system.

[0145] Advanced and / or intensive sensor, device, and / or system data can partially form a determining factor in evaluating the response to an event. For example, if a sensor, device, and / or system captures an audio signal that may be the result of a PUM falling, a camera sensor, as well as other sensors such as a motion detector, may be enabled to verify that dataset, thereby creating a more comprehensive dataset representation of the situation. For example, if a caregiver was in the same room as the PUM and the fall was confirmed, the caregiver's actions may be considered, especially if the caregiver was near the PUM at the time of the fall. Because cameras often can capture images but cannot transmit those images to any other systems, the system may be able to replay the event to determine whether the caregiver was assisted during the fall or acted to prevent or mitigate the fall.

[0146] While this approach may be computationally and administratively burdensome and unsustainable, the use of multiple levels of configuration and patterns, represented for example by tokens, and deployment within a known context, such as the HCPs within a care village, allows the care village system to effectively evaluate any variations and deviations, thereby distinguishing between different strategies utilized by stakeholders in legitimately performing their duties or actions, and strategies that represent an attempt to exploit the situation in a malicious and / or harmful manner towards another stakeholder and / or the care village.

[0147] Stakeholder interactions may be represented by games that can be evaluated using game theory. Within these games, strategies exist that accurately represent the stakeholders' intentions for most of the interactions and can be used to determine the stakeholders' compliance with specifications, patterns, or other care village systems. This may include, for example, determining common strategies by specification and / or evaluation that may be utilized by the stakeholders in the game. For example, this may include a preferred strategy, i.e., a strategy that achieves the most beneficial outcome for both stakeholders, as determined by the care village system. For example, a caregiver may visit a PUM to administer medication and spend a period of time with the PUM to ensure there are no unexpected medication side effects. The caregiver's incentive to shorten the visit may be influenced by the number of other visits the caregiver has scheduled, and the PUM's incentive may be to extend the visit if the caregiver requests company. In a game representing this interaction, there may be several strategies utilized, one of which represents an equilibrium for each stakeholder. Such strategies may then be prioritized, and any deviation from these strategies may be considered a deviation from compliance with the prioritized outcomes, to a lesser extent with greater preference.

[0148] Such games can form part of a pattern in that the pattern can represent the sensed behavior of stakeholders and the game can represent the interactions of those stakeholders. The combination of sensor-based data for early detection / reaction / monitoring of emerging events and the variations, if any, that form preferred strategies can provide detection of the pattern and its variations. These variations can be evaluated both for compliance with specifications that may govern these interactions and for providing data regarding changes in the state of stakeholders, including PUM and their wellness state.

[0149] The patterns and their context in the HCP provide continuously evolving detection capabilities with real-time / near-real-time responses. The HCP provides structure to the PUM's estimated health trajectory based on their diagnosed health and / or wellness at the time of initiating this monitoring. This structure may be represented by a set of patterns that can be displayed as tokens that are, at least in part, representations of the PUM's state in the environment across time, experience, health state, and environment. The HCP, at least in part, provides context to the states, patterns, tokens, and much of the data they represent, and thus can effectively provide context for any incentive misalignment, particularly any misalignment of incentives shaped at least in part by the operations of one or more stakeholders. For example, if one stakeholder has a set of policies that are, in whole or in part, contradictory or orthogonal to the interests and incentives of another stakeholder, incentive misalignment is likely, potentially leading to behaviors that mitigate, negate, or otherwise affect such policies. FAPS systems utilizing game management systems, machine learning / artificial intelligence, CVDT, and other care village systems can operate to minimize such misalignment.

[0150] In some embodiments, measurements of deviations from operating patterns can indicate trends, which, for example, when estimated using one or more CVDTs, can indicate that the balance of incentives within a game representing interactions between stakeholders is changing, such as decaying. This can inform the care village system and stakeholders involved in such trends to generate configurations that can be passed to stakeholders, devices, sensors, and / or systems. This can be in the form of compliance-based specifications and / or instructions that may involve transactions or other consequences. For example, if a caregiver becomes increasingly late to appointments over a series of visits to the PUM or shortens each visit, the timing and / or duration of the visits can be changed, for example.

[0151] Responses to variations in compliance or non-compliance may be implemented in some embodiments in a rigid or flexible manner. A rigid response may be, for example, threshold-based, in that if a service is not provided, there is no payment for that service. This is typical of rule-based systems, which often result in inflexible outcomes that do not meet the needs of the stakeholders involved and, in some circumstances, can cause difficulty, distress, and / or other negative outcomes that impact health and wellness.

[0152] A further response may be a flexible response, in which the incentives of the stakeholders within the interaction are evaluated and one or more variations in those incentives are formulated that balance each stakeholder's incentives, so that no stakeholder is completely disadvantaged, and the impact and / or any disadvantage to the stakeholder in terms of the stakeholder's wellness, financial impact, or other impact is minimized and / or mitigated. For example, changing an appointment time so that a caregiver can take their child to school, or selecting a caregiver with whom the stakeholder has a favorable attitude. This may be achieved in some embodiments by a compliance specification that incorporates one or more degrees of flexibility and incorporates incentives and their values ​​as part of such compliance specification.

[0153] The data sets provided by devices, sensors, and / or systems in conjunction with the patterns, behaviors, and predictions support the use of adaptive variations in compliance criteria expressed as specifications, e.g., triggered by contextual changes. For example, if a monitoring system predicts a change in a pattern, e.g., which may be represented by one or more tokens, that indicates a change in the wellness of a PUM, this may include the generation and / or propagation of alerts / events to support the transition and / or new pattern, which may in turn affect contextual variations, e.g., including scheduling, supply chain, stakeholder selection, etc. In this example, the compliance specifications may respond to these changes automatically and / or with human and / or machine learning intervention.

[0154] Outcome and Response System

[0155] The Care Village may incorporate one or more response systems that may be configured to respond to situations that are determined to require a response from the Care Village. These responses may be automated and / or manual and may involve any form of external system, including the Care Village system and / or emergency services.

[0156] The response system may be configured to interact with, for example, environmental sensing, care processing, token systems, incentive misalignments, devices, sensors, systems, stakeholders, external to the Care Village system, and / or any other entities or parties designated in any manner.

[0157] Response systems interacting with the Care Village system may utilize encrypted tokens and other tokens, encrypted messages, secure communications, and / or messaging systems configured for operation within the Care Village, which may include the use of APIs and authentication, authorization, and / or access control to ensure the security, privacy, and / or authenticity of communications, including any privacy and confidentiality standards deemed appropriate for this type of messaging and communications.

[0158] In some embodiments, these messages and communications may be recorded, in whole or in part, on at least one distributed ledger. In some cases, these communications may form part of a smart contract. Such communications may utilize one or more tokens.

[0159] One aspect of the Care Village system is the instantiation of responses to various situations, where response time is critical to the wellness of one or more stakeholders, such as PUM. In this example, the response system includes a prioritization capability that can use multiple communication methods to convey messages, including, for example, obtaining an acknowledgment, to one or more stakeholders and / or external services, such as emergency services. Care Village is not intended to replace national, state, county, or city-mandated emergency systems; rather, Care Village intends to provide communications to those systems when appropriate. The response system may, for example, enable specific access to sensors, devices, and / or other Care Village systems for such emergency services while the response system is activated and operational with respect to one or more stakeholders. The selection and use of such provided data may be subject to pre-agreed arrangements with such services to enable those services to operate in a manner beneficial to the stakeholders whose wellness is at issue.

[0160] The use of game theory in the system combined with machine learning allows for the implementation of at least one decision matrix incorporating multiple decision combinations, e.g., game rewards, alternatives based on different strategies, temporary states, different inputs, etc.

[0161] In some embodiments, based on the type of game deployed and the number of stakeholders involved in the game, there may be a set of reward matrices, which may be represented in the form of at least one decision-making matrix. Such decision-making matrices may, in some embodiments, form part of a decision-making engine module, which incorporates one or more weightings, parameters, attributes, or other characteristics that may be used in whole or in part to and / or inform a risk assessment system, such as a risk assessment module. In this manner, potential responses, particularly with respect to influencing one or more stakeholders, may be considered for their impact, thereby potentially changing their actions in light of their offered or intended incentives and mitigating and / or counteracting any incentive misalignments.

[0162] In some embodiments, the resulting decision matrix may be represented by a lattice, with nodes and edges of the lattice incorporating the decisions and stakeholders and their relationships, the stakeholder incentives for the game, and the stakeholder strategies, including their deployed and potential rewards. This decision matrix may be used, for example, as a corpus for machine learning to determine feature sets and / or may be the result of machine learning applied to stakeholder interactions, including those represented by CVDTs of the stakeholder's current state and by potential CVDTs predicting the potential state of one or more stakeholders. Using multiple game types using different or the same inputs with potential strategies evaluated in terms of stakeholder incentives, when represented as a decision matrix, provides a dataset that can be used to determine a set of most likely outcomes of stakeholder interactions and, therefore, subsequently, to configure one or more systems, devices, and / or sensors in anticipation of and / or response to one or more wellness events.

[0163] In some embodiments, such decision matrices may be created in anticipation of actual or future events, and thus comparisons between actual states represented by stakeholder interactions, devices, sensors, and systems can be evaluated in relation to these decision matrices to identify whether these outcomes match specified, predicted, and / or published outcomes.

[0164] The predicted decision matrix and any variation decisions from the actual decisions can then be published to formulate further game-theoretic representations of those interactions, evaluate incentives and their values ​​in terms of those decisions, identify other incentives that may be relevant to those interactions, etc.

[0165] This approach, when combined with HCPs and their use of patterns, provides those patterns to more accurately represent possible actual interactions between stakeholders within the care village. One aspect of this is the identification and representation of new patterns that are significantly different from the patterns currently in operation and therefore may represent shifts in incentives and values, PUM wellness, changes in stakeholder circumstances, or other factors that led to the identification of such new patterns.

[0166] Combination, cooperation, collaboration, collusion

[0167] One aspect of the system is the determination, through real-time and / or predictive monitoring, of a sequence of events within and across multiple patterns, which may be represented as tokens, and which may include multiple parties. For example, the set of stakeholders involved in a particular situation may include a PUM, a caregiver, a medical professional, and an insurance organization. In this example, there may be multiple games representing the interactions between the parties, and, for example, a hierarchy of games may exist that may include various collaborative strategies being utilized. For example, the monetary cost of providing a service is likely borne by the insurance organization, and therefore, the combination of a PUM and a caregiver collaborating may result in payment for services not provided. Furthermore, collaboration between a medical professional and a service provider, such as a caregiver, may result in a claim to the insurance organization for services not provided or not needed.

[0168] The use of patterns that represent the actual and predicted behavior of stakeholders individually and collectively, combined with representations of stakeholder interactions in the form of a game, resulting in a reward matrix that represents one or more decision matrices, can provide unique insights regarding the intentions, justifications, and / or compliance of such stakeholders within a care village when analyzed through the lens of incentives and their values.

[0169] The evaluation of data provided by devices, sensors, and / or systems that at least partially form both operational and predicted patterns, combined with outcomes represented by rewards from a game in the form of a decision matrix, provides a unique approach for detecting and identifying data patterns across sensing, interaction, and stakeholder interaction outcomes, based at least in part on the recurring occurrence of such data. One aspect of this is determining variations from existing operating patterns; while such variations may be minor, the recurring nature of the data that form such variations can alert one or more systems and / or humans involved in monitoring to the patterns created by these variations. When compared to incentives and their values, these data may be used to deploy at least one response to identify, correct, and / or mitigate any incentive misalignments. In some embodiments, this may include new patterns that may be an overlay on one or more existing patterns.

[0170] For example, such a variation may be in the stakeholder's time management, e.g., the stakeholder continues to be increasingly late (3 minutes late / 5 minutes late / 10 minutes, etc.) Patterns may be persistent or cyclical, e.g., seasonal patterns, recurring health patterns, etc., and such variations may be indicators of such changes.

[0171] In some embodiments, one or more CVDTs may be used, at least in part, to determine a set of predicted interactions based, at least in part, on the games employed and the players involved. Such an approach may include creating a non-deterministic predictive interactive system model that may, at least in part, represent one or more incentives of the players involved, as expressed by their strategies in one or more games.

[0172] In some embodiments, interactions between stakeholders, whose incentives influence all or part of the interactions, may be represented by nodes, such as nodes in a graph database. These nodes may include, for example, sets of interactions represented as a series of events or actions for each of the stakeholders, where each of these actions is, for example, a move in a game in which each of the stakeholders is a player. These interaction nodes may form sets, which represent sequences of interactions, represented, for example, as patterns, potentially automated processes. In this manner, each stakeholder's incentives, represented by the stakeholder's actions, may be played out in the game to achieve outcomes for the game, represented by rewards for the game. In some embodiments, events detected by sensors result in the formation of sequences of events, and each sequence may traverse, for example, a set of node interactions, as represented, for example, in a graph database, as a transaction unfolds.

[0173] Smart Contracts and Patterns

[0174] A smart contract (SC) refers to a computer protocol described as computer code that executes in the protected computing environment of a distributed ledger (DL) virtual machine, such as the Ethereum VM, and digitally facilitates the verification, control, and / or execution of transactions according to pre-established, immutable agreements and rules. SC behavior cannot be modified, and data resulting from their execution can be directly and securely stored in the DL, making it impossible or extremely difficult to perform any external manipulation.

[0175] In some embodiments, an activity, such as a transaction, can be combined with DL storage in a manner that it is only validated and / or can continue once the associated SC has been executed, and that execution includes storing data for that activity in the DL. As a result, an immutable trace of key stakeholder activity is kept in the DL and can be used to validate appropriate patterns and / or sequences and to identify inappropriate patterns and / or sequences. In some embodiments, this SC and DL tracking mechanism can be applied to real activities, in other embodiments, DT-simulated activities can be used, and in other embodiments, a combination of real and DT-simulated activities can be used.

[0176] Some embodiments may use SC and DL tracking mechanisms to store identified patterns, generated predictions, decisions made from them, and execution activities based on those decisions, resulting in an immutable trace of the operation of the inconsistency avoidance system, which may be used for reporting purposes, audits, forensics, etc.

[0177] In some embodiments, one or more SCs may be used in such a way that an activity, e.g., a transaction, is validated only if one or more associated SCs are executed. This set of SCs can take inputs from the activity, real signals / inputs or patterns, digital twins such as CVDTs, simulated signals / inputs or patterns, or a combination of real and simulated. They can also use data stored in DL and / or traditional storage methods, as well as signals and data from external inputs. SCs can apply conditions, perform calculations, and generate results that enable or disable an activity. SCs can also include mechanisms that automatically perform operations as a result of inputs, such as activating a payment, generating a positive or negative review or score, notifying interested parties, or triggering the start of another activity.

[0178] Tracking and verification using SCs and DLs can occur at different levels of granularity within care village activities. For example, a generic / high-level SC set can be used to verify high-level activities without examining the details or sub-activities within those activities. If a particular pattern is identified in one of those high-level activities and that pattern is associated with a higher risk of incentive misalignment, a second SC set can be activated to verify sub-activities that are part of the higher-level activity that pose a higher risk. This dynamic change in granularity for verification and tracking using SCs and DLs can be applied to real, simulated, or combined scenarios. Different levels of risk, incentive misalignment, and other conditions may require different methods for verification and / or tracking; therefore, different SC sets may exist to cover the needs of such different conditions, and a combination of selection and / or decision systems can be used to select the appropriate SC set to use in a particular case.

[0179] In some embodiments, the determination of whether high-level activity indicates a higher risk of incentive misalignment, whether to activate lower-level tracking and validation based on SC, and which set of SCs to activate can be made based on an ML-enabled system. In such cases, an ML-enabled system, such as a neural network, can be trained and used to obtain inputs related to high-level activity and generate outputs that relate the inputs to known patterns using segmentation, classification, detection, and / or other methods. This output can be used by a decision subsystem, for example, based on a decision matrix, to determine whether any sub-activity of the high-level activity should be subjected to lower-level validation and / or tracking using SCs and DLs, and which set of SCs is most suitable for such validation and / or tracking. Some embodiments can apply game theory games and elements thereof, such as reward matrices and / or game trees, to select the need for SC and DL-based validation and / or tracking and the set of SCs to use.

[0180] Smart contracts, as well as pattern specifications and their segments, may be kept in a repository, and each SC may have a relationship to a pattern or its segments. This ability to integrate patterns and SCs and automate the SC creation process at an appropriate level of granularity is particularly useful when the PUM is in a quiescent state, and that state is used as a formalization, e.g., as behavior tokens (Bevoked), for events and event sequences to occur, thus providing the ability to immutably write such event occurrences to a distributed ledger.

[0181] An SC cannot be modified but can be replaced, and in the case of a pattern, there may be multiple SCs or parts thereof that have relationships with the pattern or its segments, so that by replacement, extension, addition, and / or accumulation, the SC and DL records can accurately represent the event.

[0182] In some embodiments, SCs and pattern matching can be configured to an appropriate granularity to accommodate the degree of flexibility needed in establishing audit trails for patterns of and within HCPs. For example, SCs may be derived or extracted for patterns, including segments of patterns, such that when a dataset satisfies the pattern configuration, SCs are generated and the results are written to an appropriate DL. This can include the use of one or more systems to ensure the accuracy and provenance of patterns, datasets, SC relationships, and any conditions within these entities and their satisfaction.

[0183] In some embodiments, DLs can be used as records of training of either human stakeholders and / or machine learning systems. For example, if a corpus of material is evaluated by one or more machine learning systems to, for example, at least partially determine the unstated incentives of a set of stakeholders, and the data under evaluation is a record of stakeholder interactions, this training can be represented as a quantized outcome in one or more distributed ledgers. The same approach can be taken for human stakeholders, such as when a person is assisting a qualified caregiver in acquiring appropriate skills. Such records can include or exclude various stakeholders, for example, by using tokens to anonymize the identity of one or more stakeholders.

[0184] A game strategy, like a game reward / decision matrix, can be instantiated in a smart contract to create an immutable record of the execution and / or results of one or more games. For example, select from a set of games A and execute games A1, A2, A3 in order.

[0185] In some embodiments, at least one protected processing environment (PPE) may be utilized in place of, in support of, and / or in conjunction with at least one smart contract and / or distributed ledger. For example, the protected processing environment may be used to process incentive specifications associated with stakeholder interactions and their values. The outcomes of these interactions may be processed by the PPE and written directly or indirectly to at least one digital ledger. For example, one or more tokens may be utilized in this manner.

[0186] The previous description of the embodiments is provided to enable any person skilled in the art to practice the present disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and features disclosed herein.

Claims

1. 1. A system for matching a token representing a condition of a care recipient with at least one stakeholder represented by a further token, comprising: a plurality of environmental sensors configured to monitor a series of interactions of the care recipient to generate a resulting detected data set and provide a first token; the first token comprises the detected dataset representing a behavior of the at least one stakeholder in an environment; a plurality of environmental sensors, each of said behaviors being represented by a set of multi-dimensional features forming part of a healthcare profile of said care recipient; a fraud avoidance prediction system; Equipped with The fraud avoidance prediction system comprises: a transceiver configured to receive the first token from the plurality of environmental sensors; a non-transitory computer-readable storage medium configured to store a digital twin token representing the care recipient's previously peaceful behavior in the environment; and at least one hardware processing unit that compares the first token with the digital twin token and matches the first token with the digital twin token using game theory; system.

2. 2. The system of claim 1, wherein the alignment between the first token and the digital twin token is through a resulting incentive to the at least one stakeholder.

3. The system of claim 2 , wherein the matching of the first token and the digital twin token matches a configuration of the plurality of environmental sensors.

4. The system of claim 3 , wherein the game theory is a cooperative game.

5. The system of claim 3 , wherein the game theory is a standard game or an extensible game.

6. The system of claim 3 , wherein the game theory is a simultaneous-move game or a sequential-move game.

7. The system of claim 3 , wherein the game theory is a constant-sum game, a zero-sum game, a non-zero-sum game, a symmetric game, or an asymmetric game.

8. A system for matching a token representing said condition of a care recipient with at least one stakeholder represented by a further token, the system comprising: a transceiver configured to receive detected data sets from a plurality of environmental sensors, the detected dataset represents a behavior of the at least one stakeholder in an environment; a transceiver, each of said behaviors being represented by a set of multi-dimensional features forming part of a healthcare profile of said care recipient; at least one hardware processing unit configured to receive the detected data set from the transceiver and generate a first token including the detected data set; a non-transitory computer-readable storage medium configured to store a digital twin token representing the care recipient's previously peaceful behavior in the environment; and Equipped with the at least one hardware processing unit is further configured to compare the first token and the digital twin token and match the first token and the digital twin token using game theory. system.

9. 10. The system of claim 8, wherein the alignment between the first token and the digital twin token is through a resulting incentive to the at least one stakeholder.

10. 10. The system of claim 9, wherein the matching of the first token and the digital twin token matches a configuration of the plurality of environmental sensors.

11. The system of claim 10 , wherein the game theory is a cooperative game.

12. The system of claim 10 , wherein the game theory is a standard game or an extensible game.

13. The system of claim 10 , wherein the game theory is a simultaneous-move game or a sequential-move game.

14. The system of claim 10 , wherein the game theory is a constant-sum game, a zero-sum game, a non-zero-sum game, a symmetric game, or an asymmetric game.

15. 1. A non-transitory computer-readable storage medium encoded with data and instructions, comprising: The instructions, when read by a computer, For the computer, receiving, via a transceiver, detected data sets from a plurality of environmental sensors; receiving the detected dataset representing behaviors of the at least one stakeholder in an environment, each of the behaviors being represented by a multidimensional feature set forming part of a healthcare profile of the care recipient; receiving, via at least one hardware processing unit, the detected data set from the transceiver and generating a first token including the detected data set; storing, via the non-transitory computer-readable storage medium, the digital twin token representing the care recipient's previously undisturbed behavior in the environment; comparing, via the at least one hardware processing unit, the first token and the digital twin token and matching the first token and the digital twin token using game theory; A non-transitory computer-readable storage medium that causes the

16. 16. The non-transitory computer-readable storage medium of claim 15, wherein the alignment between the first token and the digital twin token is through a resulting incentive to the at least one stakeholder.

17. 17. The non-transitory computer-readable storage medium of claim 16, wherein the match between the first token and the digital twin token is a match of configurations of the plurality of environmental sensors.

18. 20. The non-transitory computer-readable storage medium of claim 17, wherein the game theory is a cooperative game.

19. 20. The non-transitory computer-readable storage medium of claim 17, wherein the game theory is a standard game or an extensible game.

20. 20. The non-transitory computer-readable storage medium of claim 17, wherein the game theory is a simultaneous-move game or a sequential-move game.

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