Debt repayment probability score based on capability-driven financial sustain ability.

Abstract

A computer implemented method and system to efficiently create a transparent, naturally biased, calculation of an individual's present and future ability to: 1) earn revenue and 2) pay debt based on: 1) the demand of their capabilities (skills, talent, expertise, and / or knowledge credibility); 2) their individual pattern of adopting to those demands; and 3) maintaining their health.

Description

Title: Debt repayment probability score based on capability-driven financial sustain ability;CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This invention includes, as a new use, a previous invention “Method and system to calculate and display knowledge credibility values” International Application (PCT) Ser. No. PCT / US25 / 12826 submitted Jan 24, 2025 invented by the same inventor, Lowell S. Stadelman and is contained herein in its entirety.BACKGROUND OF THE INVENTION

[0002] A big problem for the credit industry is understanding lending risks. Past methods use credit history as an indicator of an individual’s risk of making loan payments. Loan history does not take into account an individual’s ability to create income. Further, an analysis of an individual’s income potential is unreliable without trustable original source data. It is easy to fake data, and common to skew or misrepresent one’s capabilities. As a result, finance professionals must be skeptical in situations where they do not know the other individual’s professional capabilities (knowledge credibility, skills, experience, or talent) and background. This impacts lending in several dimensions. 1) New entrants such as young professionals, or professionals who’ve had financial difficulties find it difficult to borrow money. Understandably, the current financial credit industry has little information to analyze risk beyond credit history of payments and credit usage.2) Professionals with a good history of borrowing still present a risk to lenders. Historical information does not predict future employment problems for individuals. 3) Contract or ‘Gig’ workers do not have a steady income flow are higher risk than employed workers with steady incomes. 4) Professionals who’ve had a bad turn in their economic ability may have derogatory factors that ruin their ability to borrow, and improvements in their ability to produce income are not factored into their current scores. 5) Lending to borrows whos’ professional capabilities are waning in demand is risky, and lending institutions have little trustable resources for understanding what that risk is. Conversely, understanding borrowers whos’ professional capabilities is growing in demand is equally difficult for lending and credit institutions to understand. 6) Another important factor is an individual’s likelihood to continue to be productive by taking preventative health precautions such as maintaining a physically active pattern of life. Debt cannot be repaid if an individual cannot work due to health issues.

[0003] To prevent unnecessary ambiguity, we’ve provided context for the important terms of this document at the end of the spec.SUMMARY OF THE INVENTION

[0004] This invention provides a computer implemented method and system to efficiently create a transparent, naturally biased, calculation of an individual’s present and future ability to: 1) earnrevenue and 2) pay debt based on: a) the demand of their capabilities (skills, talent, expertise, and / or knowledge credibility); b) their individual pattern of adopting to change in said demand, c) It further includes the ability to use health and activity data to signal the borrowers long term commitment to remain healthy for the duration of the loan.

[0005] To solve the credit industries problems in understanding risk or opportunity of not just new job entrants who may not have any credit history from the credit bureaus, but also to avert risk with individuals who have excellent borrowing history - This invention, based on categories of trusted origins; through the comparison of demand for knowledge, talent, skills and expertise; an individual’s rank; and their demonstrated agility to remain sufficiently relevant, provides a method to forecast an individual’s financial viability.

[0006] One embodiment of this invention provides the credit industry a trusted indication of an individual’s financial sustainability, or ability to continue to earn an income based on their proven knowledge, talent, expertise and skills as well as an understanding of the demand for those skills.

[0007] Another embodiment of this invention provides a transparent explanation / understanding of how an individual’s Financial Sustainability scores are derived by displaying an indication of the depth of knowledge, skills, expertise, and / or talent of an individual as well as their ranking in comparison to other organizations, demand of their capabilities, and the companies hiring individuals with similar knowledge depth. The exemplar uses Knowledge Credibility, but would not be exclusive, and could also use supporting data from calculations for expertise, talent, and skills rankings and scorings.

[0008] A third embodiment of this invention provides a loan payment risk analysis based on an individual’s financial sustainability / economic viability and health.

[0009] Prior art focuses on financial information, history of payments, heuristic models, and even graph like models to predict risk. This invention focuses on the root problem. Can an individual, or entity continue to make money in the future based on their capabilities.BRIEF DESCRIPTION OF DRAWINGS

[0010] Fig. 1 is a diagram of an example computing system in which the claims of this invention may be implemented but the invention may be used by electronic systems with more advanced features.

[0011] Fig 2 / 1 is an illustration of the system components of the invention when a 3rdparty application is running on the user’s computing device.

[0012] Fig. 2 / 2 is an illustration of the system components relationship when a 3rdparty application is a virtual application.

[0013] Fig. 3 / 1 is an example of the visual display of this invention either in a browser or an application.

[0014] Fig. 3 / 2 is an example display of selectable options that are used for calculations of this invention.

[0015] Fig. 3 / 3 is an example display of the data selection used in calculations for this invention.

[0016] Fig. 3 / 4 is another example display of the data selection used in calculations for this invention.

[0017] Fig. 4 is a background / daemon application on the user’s machine that records and uploads data used by this invention for analysis.

[0018] Fig. 5 displays the server component of the invention.

[0019] Fig. 6 is a diagram of an interface such as an API that communicates metrics data from external software such as from third party companies.

[0020] Fig. 7 is a diagram of an application that calculates and displays Fig. 3 / 1.

[0021] Fig. 8 is a diagram of the domain identification or classification process that displays how Al, Human in the Loop HITL, or a 3rdparty API could be used, or bypassed if it is already provided by a trusted source.

[0022] Fig. 9 / 1 is an exemplar of the display of the data from this invention.

[0023] Fig. 9 / 2 is an exemplar of an alternative to the earnings section where the amounts are abstracted

[0024] Fig. 9 / 3 is a 3rdexemplar of an alternative to the display where the understanding is displayed as a score with a circle chart around it.

[0025] Fig. 10 is a diagram of the earnings comparator, knowledge demand, and loan risk system.

[0026] Fig. 11 is a diagram of a model used in the calculations for financial sustainability.

[0027] Fig. 12 / 1 is an exemplar of the flow chart for the viability calculations of this invention.

[0028] Fig. 12 / 2 is a continuation of the flow chart started in Fig. 12 / 1.

[0029] Fig. 13 is a model used for the calculating a domain’s value.DETAILED DESCRIPTION OF THE INVENTION

[0030] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number appears. Wherever convenient, the same reference numbers are used throughout the drawings and spec to refer to the same or like parts. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the disclosed example embodiments. However, it will be understood by those skilled in the art that the principles of the example embodiments may be practiced without every specific detail. Well- known methods, procedures, and components have not been described in detail so as not to obscure the principles of the example embodiments. Unless explicitly stated, the example methods and processes described herein are neither constrained to a particular order or sequence nor constrained to a particular system configuration. Additionally, some of the described embodimentsor elements thereof can occur or be performed (e.g., executed) simultaneously, at the same point in time, or concurrently. Reference will now be made in detail to the disclosed embodiments, examples of which are illustrated in the accompanying drawings.

[0031] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of this disclosure. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate several exemplary embodiments and together with the description, serve to outline principles of the exemplary embodiments.

[0032] This disclosure may be described in the general context of customized hardware capable of executing customized preloaded instructions such as, e.g., computer-executable instructions for performing program modules. Program modules may include one or more of routines, programs, objects, variables, commands, scripts, functions, applications, components, data structures, and so forth, which may perform particular tasks or implement particular abstract data types. The disclosed embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in local and / or remote computer storage media including memory storage devices.

[0033] The knowledge on how to implement these embodiments / exemplars may be found in text-books and under-graduate courses available online, and also through web searches. With the explanations below, a graduate of software engineering would understand how to implement these as they are understood by someone who is familiar with the art.

[0034] For the remainder of the description, references to 3rdparty software or application could be any embodiment where the software is external. The third party may be the same entity or another entity that is providing interaction with knowledge and where its’ data is sent to this invention.

[0035] For the purposes of clarity. There are multiple individuals that interact with this invention. The observed-user is the individual whose knowledge credibility is displayed. The observing user or requesting user is the user who is viewing the display of the metrics. Other individuals are generally users who have interaction with the observed-user within a domain of knowledge.

[0036] Fig. 1 provides a generalized example of a computing system that is capable of the innovations described by the claims of this invention. The computing system is not intended to suggest a limitation of functionality. The invention described may be implemented in a wide range of general-purpose computing systems including personal devices such as phones laptops and web servers.

[0037] Fig. 2 / 1 is an embodiment that displays an example of the system elements of this invention and the relationship with the other elements wherein a 3rdparty application / software Fig. 2 / 1 (201) is interacting with this invention on the user’s personal computing device. Thisinvention will operate with one to any number of personal computing devices. The UI (User Interface) (300) of the personal computing device would likely be either a browser or an application (700) depicted further in Fig. 7 (700). If it is a local software application running in the same personal computing device, then it may receive its data from local storage (404) and may receive data and calculations from remote cloud like services. If it is a browser, the preferred method is to receive the processed information from cloud like services. Cloud like services include a server system Fig. 2 / 1 (500), depicted further in Fig. 5 (500), and would process the information from raw data or organized data from the storage system (516). The following is the data needed from the 3rdparty / extemal software Fig. 2 / 1 (200) such as a text processor, Software Development Tool, or any software that is related to a field or domain of knowledge that is run in the local environment. The third-party software would include its’ Vendor ID (406) and if needed a Project ID (407) as well as the source data (201). Depending on the purpose of the software, and its method of data capture, it would receive a Category of Trust rating (301) shown as an association. Importantly, the vendor should not control the Category of Trust that is assigned to it. Further data, as shown in the Software Interface Fig 6, as needed, would be provided.

[0038] Data from the 3rdparty / extemal software Fig. 2 / 1 (201) is sent either to the cloud using the Software Interface API (600), or to a daemon / background application (400), further depicted in Fig. 4 (400) which then stores the data in local storage on the personal computing device (404). The daemon / background application may also synchronize data with the cloud when connected.

[0039] Fig. 2 / 1 further shows that the User Interface (300) may be displayed either in a browser, or as a part of an application (700).

[0040] Fig. 2 / 2 displays another embodiment of system elements where the 3rdparty application Fig. 2 / 2 (221) is running in a virtual environment and is hosted in a 3rdparty cloud (220). Users interact with the data virtually which is usually through a browser. The 3rdparty virtual application / cloud (220) sends the necessary data to this invention’s servers (500) that may operate in the same cloud. Here, the Category of Trust (301) is also shown as an association, and likewise would not be controlled by the 3rdparty application in most conditions. Data, including the Vendor ID (406), the Project ID (407), the source data (201) would be included with all other needed data of the Software Interface (600) which is sent to the server (500). When access is desired by a user, they may observe the metrics through the user interface (300) that would be provided by a browser.

[0041] Fig. 3 / 1 (300) depicts an example of the UI (User Interface) for the results of a request based on the users ID Fig. 4 (405). This exemplar shows a series of rows containing gauges and information that explains an observed-user or entities knowledge metrics and Knowledge Credibility within a Domain. At the top Fig. 3 / 1 (301) is an example that allows the observer / user to select one or more categories of information that is used in the analysis and calculations conducted per category. Categories are an enumeration that represents the concreteness, or trust that the data source is free of errors and related to the observed-user. The number of categoriesmay be beyond the five that are shown. The following four categories are an example of how data may be divided and used in a Hierarchy-of-Trust.

[0042] Category I of Fig. 3 (301) would be concrete information that is collected actively, or concretely, by software that observes the actual interaction with knowledge and can provide metrics directly due to its involvement in the practice or interaction within a knowledge domain. Such interaction may be the use of an instrumented baseball bat and integrated camera’s where the user of the instrumented system is measured in performance and in numbers of hits vs. strikes, and time such as hours of practice. It may be from a learning system where the software observes, in real time, the user’s interaction as they learn. These metrics can originate from 3rdparty software such as Software Engineering Tools like an IDE where the software is used to write software. It may also, for example, be tools that are used to search for errors such as a peer review system, in real time. It may further be from a video chat system where a user is holding a discussion within a domain-of-knowledge. The user is known and the interaction is observed at the moment it happens.

[0043] Category II of Fig. 3 / 1 (301) would be non-concrete information such as an acknowledgement from others, but where an observer, participant, record has a stake or loss in its reputation in the validity of its source connected to the data. An example would be a research paper where its creation was not observed but has been published in a digital library such as IEEE or Psychology publications. In this category, there is an implantation of accountability for original authorship and accuracy of records, and agreement is shown, for example, by the number of citations and or peer reviews. These acknowledgements provide strong authority for knowledge credibility but could contain inaccuracies as to who was the original creator.

[0044] Category III of Fig. 3 / 1 (301) is a non-concrete source of information, where a rating is related to a scarce resource such as sales or captured business. Where there is greater opportunities for data manipulation or errors, but there is reputation gained and lost if the information is inaccurate or the source is incorrect. An example of this may be where a manager has provided a review that contains the information used for knowledge credibility. It is far weaker than categories I & II but still holds stronger authority than if reported by the observed-user themselves.

[0045] Category IV of Fig. 3 / 1 (301) is a weak source of information, where the rating comes from acknowledgement from others such as votes from a website or social media platform. There is little at risk for the rater when they give a false vote and frequently votes are paid for by the thousands. However, through volumes of votes in relation to its origin and accuracy provides no stronger of an authority than if reported by the observed-user themselves. This would be a rating for a social media influencer.

[0046] In Fig. 3 / 1 (307) displays rows of gauges and data per domain. Rows, as shown, are configurable based upon the selected categories above. Shown is the configuration with categories I and II selected. The configuration of rows and columns are as necessary to display the domains,and the necessary values used in the knowledge credibility score. Each row displays the domain (302) relevant to the row of gauges / graphs and information. The domain is always shown, and we show physics for this row’s domain. (303) is the value of a scarce resource for that domain that the observed-user or entity has interacted with or practiced. Value exists in all four categories. In the example, value is related to a time e.g. a year, 3 weeks, 2 months but is optional. (304) is representative of effort in a measurement in time and is available in all 4 categories. Here we have shown a donut chart with the total time in hours and percentages given with the type of interaction. Shown is Practice, Study Time, Discuss, and Create. The third column at (305) is for testing knowledge and displays points. Here is displayed the sum for all scores within the domain and it is shown in a donut chart with percentages towards the type of testing and learning. E.g. free recall vs. a standard multiple -choice test. The fourth column is (306) is an example of an optional column based upon the selected categories. For category I only it would not be displayed since it includes methods of capture that are not concrete. For categories II through IV it would represent the number of citations for category II or votes in category III and IV. In the case that categories II and III are selected where both votes and citations are visible, they would each be represented by their own distinct column. The fifth column is (308) Percentile, and (309) a link to more data. Percentile may also be displayed as a percentile or score. Refer to Fig. 3 / 2 (308b), an exploded view of the percentile, where a menu is provided allowing other entities (universities in this case) to be selected and where the user’s percentile Fig. 3 / 2 (308a) can be given in relation to that entity. In the case that an observed-user attends the University of Texas at Austin; by selecting a different university in the menu (308b), the observed-user’s percentile (308a) is given in relation to the entity that is selected in the menu. Here is a demonstration that an apples-to-apples comparison can be made for individuals in the system in relation to the data that is captured rather than being restricted to only a single entity. As the observer / user selects new entities, the rating changes at Fig 3 / 2 (308a). ‘Entity’ is considered broadly and is shown by different universities or by a company such as ‘UpWork’ or even ‘overall’ as shown in the 4throw in Fig. 3 / 1 (308).

[0047] Finaly at (310) a percentage of the knowledge industry has requested and that an observed-user has interacted with is given. This is represented by Fig. 5 (514). Further at (310) are the companies that hired individuals with similar metrics discussed further with Fig. 5 (515).

[0048] One exemplar for use of the percentile is the display provided in Fig. 3 / 2(308a). This percentile discussion is around Fig. 5 (511) Percentile by Domain. Discussion and interaction data captures the knowledge within a domain between the multiple individuals. To transparently show the relationship, it is displayed along with the understanding that individual A has interacted with top n% of individuals in said domain as shown in Fig. 3 / 4 (332) the other individuals name, and (338) their rating. Discussion, and interaction data alone, without calculations from other individuals, may be used. It is convenient and makes reliance on key individuals unnecessary. However, there are cases where information would be unlikely to be captured by this invention. An exemplar would be Robert Oppenheimer and the teams that worked with him to end WWII.Individuals who had interacted with him, or were a part of that organization, would receive no knowledge credibility outside of their organization for the unique depth of knowledge they could provide. This is discussed further in Percentile Calculation Strategies

[0121] ,

[0049] For transparency purposes, details of the data that is used in a calculation is also shown in the user interface by clicking on a link labeled ‘More’. In Fig. 3 / 1 (309) the area of data used in calculations may be selected by a second menu Fig. 3 / 2 (309a). The displayed information would be shown, for example, in another page that provides a plurality of information related to that domain and the area that was collected. In the example in Fig. 3 / 3 of the displayed data for Entities. The relevant domains to the row in Fig 3 / 1 (309) would be provided as they are at (317). A table would display information about the related entities, knowledge, people, and projects within the selected domain. Each row would display information about each element. A preferred organization of a row would show, the relationship with the observed-user (person A), and other relevant data so that the percentile and values are transparent. The columns preferably would be organized to show the entity name (312), the relation with the observed-user (313). The time involved with the entity (314). The value from a scarce resource (315) or in this case ‘income’ that was exchanged for the knowledge. The sub-domains (316) that were involved with that entity. The entities rating (318) data provided for the rating of the entity. And a description for the entity in relation to the domain (319). Multiple rows of entities are displayed and could be scrolled through as shown at (320). The displayed information may be changed by selecting another area within a menu as shown at (311). Each area would display similar data to provide transparency and understanding of the percentile or score that an observed-user is given per entity choice shown in Fig. 3 / 1 (308) and Fig. 3 / 2 (308a).

[0050] Another example of a details page is when ‘People’ is selected in Fig. 3 / 2 (309a) or Fig.3 / 3 (311). Fig. 3 / 4 is the ‘people’ page that show the other individuals within a domain, or in other words “people 1 - n” that person A has interacted with. The layout is similar to Fig. 3 / 3. At Fig. 3 / 4 (331) is the other area’s selection menu. (332) is the individual’s name. (333) the related domain. (334) the time in the domain. (335) the income over the duration. (336) the related subdomains. (337) The selected domain. (338) The individuals rating as shown or optionally may be a percentile. (339) the description or reason for the rating, and (340) the rows with all individuals.

[0051] The background, or daemon application Fig. 4 (400) is an embodiment showing a user’s computing device, alternative embodiments may be as provided in a “virtual environment”. The daemon / background application captures, translates, and stores metrics from the data source (201) which may be a 3rdparty application, for use by the invention’s other components as shown in Fig 2 / 1 and Fig. 2 / 2. The background application does not need a display and is called by 3rdparty software when needed or may be running in an environment that contains the domain ID process (800) and allows the third-party software, containing the data source (201), to run inside of the environment (not shown). The background application stores outputs from the data source (201)through the use of a bridge interface (402), or alternatively may use the API software interface (600), that may be used to interface with a variety of purposes such as an IDE software development tool, text editing program, accounting software, drafting software and or learning software where the necessary variables are recorded. The data captured from the data source is then stored locally and or remotely so that it may be combined and used in the calculations for the scoring systems Fig. 5 (511) Percentile by Domain; (507, 508, 509, 510) Time, Points, Value, and Votes respectively; (514) Knowledge requested by industry comparison; (515) Users with equivalent metrics work entity; as well as the timeline (521). An optional bridge shown at Fig. 4 (402) is used to convert, properly format, 3rdparty / extemal software, the data source (201), variables if necessary. The data is then checked to ensure its integrity at (401) by checking that it is correctly formatted, checking for proper vendor codes, or by a hashing algorithm and encrypted with a key. The data is then passed to the storage system (404) which may be a typical database, block-chain connected through an API, or simply storing the variables in memory such as in a string, object code, byte code or even binary. The user’s unique id is stored (405). The third-party software vendor ID is stored (406). The project ID if any is stored (407). The total time of effort is recorded for this session is combined with the accrued effort time (408) and categorized as creating (408a), reading, observing, or listening (408b), discussing (408c), or practicing (408d). If any monetary value or value from a scarce resource has been exchanged it is combined with the accrued value (409) along with the type of value (410) e.g. dollars, bitcoin, EU for example. The Data Source (201) which may be a 3rdparty app, provides the domain IDs through the Domain ID Process (800) which may be provided through an API, or through the 3rdparty apps own processes. The domains are recorded for the session (411). Any people associations are stored (512c). The category of data certainty / trust is stored (301). The description of the 3rdparty software at (412), a score is stored if any (413), and acknowledgements such as votes (414). Additionally, if connected to a network, the data is synchronized (404) with remote data and sent through security which provides encryption and data assurance (403) through the network to the servers of the invention shown in Fig 5 (500).

[0052] Fig. 5 illustrates the server component of the invention and the elements that it communicates with. At Fig. 5 (500) is the server. The preferred implementation is in a cloud like environment where there would likely be multiple copies of the server running simultaneously. Implementations of this part of the invention would conduct the following: 1) Receiving data from 3rdparty / extemal software systems or the daemon / background application (400). 2) Processing the information so that it may be stored and retrieved efficiently at high access rates. And 3) Sending the processed data to a browser or application for display or sending the processed data for other uses.

[0053] Received data first enters through security Fig. 5 (501). If necessary, received data is translated into usable data through abridge (502). Received data includes: Data (519) from 3rdparty Bank and Pay Systems (518) to retrieve transactions and aggregate the necessary monetaryvalues directly from the trusted source; credit data from; source data (201) related to a domain of knowledge from the various sources, such as 3rdparty or external software systems; Credit System data (1030); rank and capability source data that may come from other capability ranking systems (1051); Medical data (1053) and sports fitness device data (1054) are used in health risk calculations (1021).

[0054] Received data, when necessary is checked for data-integrity to ensure it can be trusted (503), then synchronized (504) and stored in a data storage system (516) along with the category of data certainty / trust Fig. 3 / 1 (301).

[0055] The data storage system Fig. 5 (516) is preferably a database but could also be blockchain provided through an API to further provide data integrity, or it could simply store CSV, string, object code, machine code, or ultimately binary data in memory.

[0056] Requests (517) to the server endpoints must include the necessary information to retrieve it in a SQL query. A common request would be to see individual ‘A’s Financial Sustainability, or capabilities such as Knowledge Credibility. Such a call could come from a link, a QR-Code, a browser form, and or a web-crawl request. Responses would preferably be protected, if so chosen by the observed-user or for other purposes, and either return a message that the data is not available, or the user’s metric data as an explanation for their Financial Sustainability - Fig. 3 / 1 Fig 3 / 3 Fig 3 / 4, or for the Financial Sustainability data - Fig 9 / 1, 9 / 2 and or 9 / 3 depending on the request, authorization, and permission settings.

[0057] A browser or application (700) would display the information after the server (500) is called by the correct endpoint and providing the necessary information to retrieve individuals / entities or groups and display them as shown in Fig. 3 / 1. The call containing the entity id or user id (405) is received by the server (500) and may optionally contain the category of data certainty / trust (301) to be used. If the category is not included, the system will use a default category. It may also contain the domain (411) the user is interested in or other data as needed.

[0058] For Capability Data, the following example is to retrieve the explanatory Knowledge Credibility Data that serves as the basis for Financial Sustainability when Knowledge Credibility is used. Knowledge Credibility Data is retrieved from the storage system (516) preferably using a SQL request. It is organized by its category / s of trust (301) and by each relevant domain (411) and contains calculations for interaction time (507). It further contains: Calculations for Points (508) from testing; Calculations for Value (509) are from a common scarce resource such as the US Dollar and is an indicator that interactions are relevant to industry and in demand.Acknowledgements such as votes and or citations, if available by the category selected, would be included in the calculation (510). Finally, a complex calculation is made to provide the percentile of the observed-user / entity by domain (511). ‘Knowledge requested by industry comparison’ (514) is included as well as ‘Users with equivalent metrics work entity’ used to show how an observed-user compares with other individuals who were hired by other companies (515).

[0059] Interaction time Fig. 5 (507) comprises of the elements shown in Fig. 4 create (408a), study (reading, observing, listening) (408b), discuss (408c), and practice (408d). Create time (408a) is the activity of creating knowledge for the purpose of consumption by others and where the effort may be captured by a trusted system. Study time (408b) are passive and active actions similar to observing videos, reading, using flash cards taking tests or self-tests where the activity may be captured by a trusted system. Discuss time (408c) is the activity of discussing information which may be in a lecture, a tutoring system, or in a business environment such as an engineering meeting, department meeting, or similar discussions where the discussion may be captured by a trusted system. Practice time (408d) is the activity of being actively engaged in working with information within a domain-of-knowledge and the activity may be captured by a trusted system.

[0060] The percentile by domain calculation Fig 5 (511) is a complex calculation that may displayed as it does in Fig 3 / 1 (308). The data would change as a user selected different entities Fig. 3 / 2 (308b). Upon a change, a possible implementation would make a request to the server for another calculation in relation to the new entity and a percentile in relation to the observed-user would be displayed at Fig 3 / 2 (308a). The Percentile by Domain Process Fig. 5 (512) also considers the category of data certainty / trust (301) in its calculation. It further depends on the calculation strategy (513). The Percentile by Domain Process includes a selection from time Fig. 5 (507), points (508), value (509), votes and or citations (510), percentiles from entities that an observed-user has worked with (512a), their own knowledge and depth (512b), the associations of people (512c), the projects (512d), their significant achievements (512e), and positions of responsibility (512f).

[0061] The ‘Knowledge requested by industry comparison’ provides the percentage of the information that the observed-user has interacted with and has an understanding of. The output is as shown in Fig. 3 / 1 (310) on the top row.

[0062] The ‘Users with equivalent metrics work entity’(515), finds similar individuals based on their metrics. Discovers the companies they were hired by or are working for, and provides this as output as shown in Fig 3 / 1 (310) on the bottom row.

[0063] Fig. 5 (520) browser or (700) application represents the user’s device where the preceding outputs would be displayed.

[0064] Financial Sustainability reference Fig. 5 (1000) calculates Earnings (1001), Capabilities Demand and Risk (1011), Health Risk (1021), and Eoan Risk (1031), and is explained in detail in Fig. 10. These calculations, when requested by a user (517) would be output and displayed in the user’s device either in the browser or app (520).

[0065] Fig. 6 is an exemplar of a software interface used for the explanatory Knowledge Credibility metrics, Fig. 6 shows the API, for 3rdparty or external software or communication through a bridge. 3rdparty software, after passing testing and meeting standards for assurances of the accuracy and protection from tampering would receive a vendor number and the proper instructions to communicate with this invention. The vendor number along with an associatedcategory of trust (301) would be stored in the database. The following is the preferred information included: The user id (405), vendor / entity id (406), project id (407), total time of effort for this session (408) and is further identified as Create Time (408a), Reading, Observing or Listening time (408b), discussion time (408c), and practice time (408d). If it is a banking or payment software it may include the sum of a value (409) and the type of value (410). It would further use the domain process Fig. 8 (800) to provide the domain (411). The project description if any (412), a score if any (413), and acknowledgements from others such as votes, citations, an academic grade or score, and or a peer review if any (414). Further included would be any entity associations (512a), people associations (512c), and any positions of responsibility if available (512f). The captured data would then be sent to the server Fig. 5 (500) or the local background application Fig. 4 (400). Data sent to the server would include processing for integrity assurance (401).

[0066] Fig. 7 (700) is an alternate embodiment that represents an application for displaying the explanatory Knowledge Credibility metrics of this invention and is shown on a user’s device. The elements of this application may also exist as a part of another larger application with broader purposes. In the shown embodiment, the application communicates with local storage which may be a database) and local storage communicates with the background application (400). The application, may if connected to the internet, make a request to the server (500) with the user id (405) and trust category (301). The server would then return the requested data, and calculations as requested either individually or at the same time. The calculations would include (514) ‘Knowledge requested by industry comparison’, (515) ‘Users with equivalent metrics work entity’, and (511) ‘Percentile by domain’. The calculations would include the previous mentioned calculations for the server: (512a) Entities calculation; (512b) Knowledge Calculations; (512c) People calculations; And (512d) Project calculations and are calculated per domain (411) and per category of data certainty / trust (301). Locally stored information would include: the time calculation (507); points calculation (508); value calculation (509); and votes calculation (510) that are also conducted per domain (506) and per category (301). The server may also provide (521) Timeline information. These data would be formatted by the Application (700) and displayed in the user interface (300) in the preferred display similar to Fig.s 3 / 1, 3 / 2, 3 / 3 ,3 / 4 and 9. All communications to the server would be through security (701).

[0067] Fig. 8 depicts an exemplar of determining the knowledge domain from data that is provided from Fig. 8 (601) where data may be provided from multiple source types that are either classed or unclassed. Where class is referring to data being provided with a domain ID or it hasn’t. One exemplar of a classed system would be an instrumented baseball system that would include a bat, baseball, and a baseball field that could report the performance of a baseball player. In this case the data would be preformatted with domain specific information because of its relationship directly with the performance of a baseball player and thus their knowledge and experience.Another exemplar of classed information may be from a leaming / study application where thedomain ID is provided from an institution or by multiple individuals who are unrelated but in agreement. The Category of Trust is provided outside of the data based on the vendor ID. An exemplar of unclassed information would be data from text that is an output from a video-chat conversation, or from text that is written from an observed-user. If needed, a bridge (502) would serve to convert data from the source to the properly formatted data needed for this invention. API’s provided by this invention, where they are used by 3rdparties, would also serve to correctly format data for use by this invention. The two cases for the data that comes from inputs is classed or unclassed. If it has a trusted domain identification that is provided already then it “is classed” (801), or it is not. In the case that the domain ID is provided and is trusted, the domain ID is considered “classed” (803) and is stored (411). In the case that the domain ID is not provided, or is not trusted, then the domain ID is considered unclassed (802) and the domain ID may be derived through an internally provided Al content classifier (804); a “Human in the Loop” or HITL, or through an API provided by a commercial Al company. After the domain ID has been properly identified it is then stored at (411). An indicator may also be included that may indicate the level of certainty that the domain has been correctly provided. If Al is used, and depending on the model’s accuracy because of its implementation, it may, for example, provide that the model is .98 out of 1.0 confident of the correct domain ID. Data that is provided may not be limited to only 1 domain. For example, if text data is provided and if the subject is physics, there may be several related domains and sub-domains such as Particle-Physics, Nuclear-Physics, and Calculus.

[0068] Depth of knowledge, for the purposes of the explanatory Knowledge Credibility metrics of this invention, is depth within a domain. It is not the DOK framework. Depth of knowledge would be discovered through the same process of identification as the domain ID discussed in paragraph

[0060] , An example, the identification of an individual’s understanding of physics could be shown through the multiple levels of dependent parent domains. Depth of knowledge in physics may be shown by time value, and depth in multiple sub-domains and the depth within them such as for classical mechanics and momentum, along with the required algebra to solve for problems. In the exemplar display of metrics Fig. 3 / 1 (300), Software Engineering is given an amount of time, a value, and or scoring. The subdomains, such as ‘Software Architecture’ are also displayed with a row and ‘Data Structures’ would also have a row. Each subdomain includes time spent in that domain and a link to the supporting data. The metrics of parent domains do not need to be a sum of the subdomains. Rather they are initially mapped as a key value association. During a period of time, while the data is collected, each increment in value is added to the domains and subdomains that are related. Another example, not shown, is Marketing. Where it’s subdomains such as Digital Marketing would have a row. Digital marketing is a parent to Search Engine Optimization and if the observed-user had effort, its metrics would be displayed similarly depending on the Category of Trust the Observer has selected.

[0069] Knowledge needed by industry Fig. 3 / 1 (310) is derived from individuals who are associated as working for an industry. A comparison is made between the observed-user who isthe target subject of a query and other individuals who are similar to the target. The returned data would include the industry and companies that the similar individuals are working for and a percentage of the knowledge the individuals have. Alternatively, data may be collected from job postings and a comparison may be made with the knowledge of the target subject and the knowledge requested in the job postings.

[0070] Achievement or accomplishment, selection of the key events may be through the aggregation of data that points to the events and the individuals who are connected through it. The same methods as explained in Fig. 8 could be used to identify or validate these dates and events if they are provided by the observed-user or another individual. Where the aggregation is sufficient enough to provide evidence that it occurred. Thus, it may not be a category I since it is not observed but reported by a category II or III data and would be displayed based upon the selected categories of trust. Achievement or accomplishment, in this exemplar, would be stored in the database and associated with the users unique ID, a date, and other relevant columns for description and data as needed. Achievement or accomplishment may be shown similar to the data in the tables Fig.s 2 / 3 and 2 / 4.

[0071] Positions of responsibility may be entered similarly through the use of a webform entry by a user that may have restricted access to the observed-user’s employer, through web-scraping, through datamining, or through an automated system for example where the employer is connected to a 3rdparty data mediator. Positions of responsibility, in this exemplar would be stored in the database and associated with the users unique ID, a start date, and an end date. Positions of responsibility may be shown similar to the data in the tables Fig.s 2 / 3 and 2 / 4.

[0072] Value of knowledge, skill, and expertise may be determined by analyzing contracts between, for example, employers and persons employed to conduct work. The agreements such as employment or work contracts Fig. 13 (1303) and supplements and verifies the data using bank payment data (519). When a user is contracted by an entity such as a small business or an individual for their knowledge skills and or expertise, the contract is submitted to the invention through the server input. The contract data and metadata is routed to the correct server endpoint. Then parsed by a parsing method to extract the body of the contract, the duration or time (507), a means to identify the user such as by their email address, hash or ID (405), a value such as a US dollar amount (509). The body of the contract is processed by the domain classification or ID process (800) and the resulting domains are identified with the rest of the contract data (1300). The contract data is then stored in the database. Supplemental payment information or an alternate means of obtaining the value is from bank payment data (519). The payment data is submitted to the server input to the correct router endpoint. The payment data is then parsed (1303) into the time the payment was made (507), the value in for example US dollars (509), along with a means to identify the user (405), and vendor or entity ID (406). Uater, when value data is called. The value is calculated by domain, time, and total value for display.

[0073] Fig. 9 / 1 is an exemplar of the outputs of this invention. It displays the calculation of the observed-user’s potential earnings (904). For the observed-user’s understanding, where appropriate, the earnings calculations are given relative to a time period. The actual earnings output, if available, are shown (1004). The earnings output of the observed-user’s geographic peers (1003a), as well as the average earnings of all peers (1003b). The supporting data for these calculations is provided on a separate page and the exemplar shows a link to that page (901). Note that the actual earnings of the observed-user may be abstracted. The preferred output Fig. 9 / 2 (1001), due to legal and privacy reasons, is simplified to the analysis that the observed-user is undervalued, equal, or overvalued in comparison to their peers geographically and / or by all peers in their industry. Similarly, there is a link to the analysis information as may be provided (902b).

[0074] Fig. 9 / 1 (905) displays the demand for the observed-user’s capabilities where capabilities are knowledge and or skills.

[0075] Fig. 9 / 1 (1012a) is the Competitive Positioning or “Percentile” of the observed-user in comparison to their peers, in this case by positions of responsibility. E.g. by job title, industry, entity, or by domains. The exemplar show’s them in a percentile grouping. Calculation of the percentile is discussed starting paragraph

[0121] Percentile calculation strategies.

[0076] The observed-user’s Impact Positioning (1012a) usually qualified by the quantifiable value of their contributions in comparison to their peers. An individual may be highly competitive in knowledge, but due to their position-of-responsibility, for example a mechanical engineer working on a project sub-component, may not be able to show a dollar amount of their contributions. In contrast, a marketer or a manager may be able to show the dollar amount for the contributions of their efforts. Impact is important to a company’s bottom line and may be a consideration for promotions retention and pay. Like Competitive Positioning, Impact Positioning may also be rated in a percentile as shown.

[0077] Data for Impact Positioning (1012a) would be categorized according to the Categories of Trust discussed in this invention. The data may be derived from, for example, a cash reward. If the reward were tracked by an ID assigned by a contract, the system would recognize the reward as impact. Or alternatively if the observed-user is a Gig worker, the implementors of this system may include a payment system such as what Stripe offers. In such a case, the cash reward could be similarly identified as such and this invention would recognize the id and assign the reward as impact. For a Category III credit, the observed-user’s employer or supervisor may enter the data into the system.

[0078] Improvement Pattern Fig. 9 / 1 (1012c) is an important metric. An observed-user’s capabilities may be recognized as obsolete and providing little value to an employer. In such cases the observed-user faces a risk not just losing their employment, but not able to find employment with other employers. If an observed-user maintains their capabilities so they remain relevant or improve their positioning, this indicates they have a great chance at either remaining at the same employer, receiving a promotion, or finding employment with better pay. The data for the user’simprovement pattern may be captured from a number of sources including learning applications that report data, through the use of the API to this invention or the system that implements this invention.

[0079] Historical Demand, Fig. 9 / 1 (1013), provides an understanding that the capabilities of the observed-user may be increasing, have peaked, or are decreasing. This helps to provide some indication as to the risk of unemployment and the opportunity of better employment and pay. Understanding Job Availability (1014) helps with determining the observed-user’s situation in the job market. An observed-user’s capabilities / job may be decreasing in demand, but the job availability is still large which helps with stability. If in contrast the job availability is a low number and historical demand is decreasing, this signals potential for unemployment and risk. Data for historical demand may be stored in data-structures related to the relevant capabilities. This data may be captured by scraping job posting boards, through industry hiring manager inputs directly to the system that contains this invention, or through a number of other mechanisms.

[0080] To calculate the improvement pattern and the historical demand mentioned above, a timeline may be used and further displayed. A timeline is accomplished using a database query that includes the dates of the relevant activities.

[0081] A root cause for economic risk is obsolescence - Including where skills, knowledge, and expertise is replaced by disruptive technologies or techniques. Another consideration is the change adoption rate (1015) within an industry. Some industries are more immune to change than others.

[0082] Fig. 9 (1042) A top-level analysis may be provided that makes the information easier to quickly understand. The analysis provides the observer a summary of the information from the earning potential and the capability demand.

[0083] Fig. 9 / 1 (902) A link to the supporting data is also displayed for transparency purposes.

[0084] Fig. 9 / 1 (906) Health Risk is also important to understand for financial sustainability.Medical status (1023) information, when available, as well as Fitness Activity (1022) is an indicator of an individual’s health and helps to provide an understanding of the observed-user’s ability and determination to remain capable for Financial Sustainability.

[0085] Fig. 9 / 1 (907) loan Risk is an exemplar of how the Financial Sustainability analyses would be used. This is not meant to be a restrictive example. A user in the credit industry, such as a financial analyst, would enter the observed-user’s identifiable data, a loan amount or payment amount (1033), and the duration of the loan (1034). The Financial Sustainability provides an understanding of the amount the observed-user earns and depending on the loan type if the user can currently afford the payment and provide an assessment for the duration. For example, for a 30-year home mortgage, would the observed-user be able to afford payments for the mortgage until there was enough equity in the home that the loan institution would be able to recover if the observed-user defaulted. In the exemplar, at Loan Risk (1043), it would provide a duration and a risk. Example response “Low risk for 7 years” or “High risk” may be given depending on the analysis. This is based upon: If the user’s capabilities were sufficiently in demand; If the usercould find another job with their capabilities; and if they have a positive improvement pattern in the case that they may face a job loss.

[0086] Fig. 9 / 2 depicts an alternative exemplar for the Earnings Potential (904). (1001) is abstracted to an analysis. The link (901) is optional but would show the calculations of earnings potential, possibly in an abstract form.

[0087] Fig. 9 / 3 depicts another alternative exemplar where the output from all calculations is abstracted to a single score. In this exemplar the calculations are further subdivided into their corresponding area’s and rated with a “Good”, “Excellent”, “Fair”, “Poor”, or “Bad” or as needed. The displayed score may be a division of 1000 points, where each division represents some number of points of the total. E.G. Capability Demand (905) may be valued at 250 points, Earnings Potential (904) Valued at 250 points, Loan Risk (907) valued at 300 points and Health and Fitness (906) valued at 200 points. Each actual value is represented in the circle graph as a percentage of the total number of points. The descriptions of each may provide an abstracted grade such as “Excellent”, “Good”, or “Fair”. These calculations are performed by Financial Sustainability architecture in Fig 10, and the matrices in Fig. 11.

[0088] Fig. 10 (1000) depicts Financial Sustainability. Forthis invention, the top-level explanation for financial sustainability is: An individual’s, or entity’s long term financial stability and adaptability to change in demand. Financial sustainability is calculated through understanding: 1) Earnings Potential (1001), and 2) Knowledge / Skills Demand (1011).

[0089] If the server Fig. 5 (500) receives a request for an individual’s financial stability (517), the server calculates, or may look up calculations performed earlier, the individual’s ranking from Knowledge Credibility as previously described, or through other methods of calculating an individual’s professional capabilities (skills, expertise, talent) as would be provided by Rank and Capability Source Data Fig. 10 (1051). Likewise, understanding an individual’s efforts to improve or maintain their professional capabilities is provided from education training source data (1052), or both sources may be derived from the metric data in Fig. 6 (601) if through knowledge credibility. Importantly, Categories of Trust (301) provides the understanding of the original source data’s reliability to be free of errors.

[0090] Earning Potential Fig. 10 (1001) is calculated by comparing the observed-user’s actual earnings (1004), with the earnings of other individuals who are similar in capabilities (1003). To calculate the observed-user’s actual earnings, it receives income data from banks (519) and value data (509) from applications where they may be paid fortheir goods and services, and or from contract data as shown in Fig. 13 and discussed in paragraph

[0072] , Value data Fig. 10 (509) would be from trusted sources based on their reliability to be free of errors. To calculate how the observed-user compares with other individuals who are similar in capability, the observed-user’s specific capabilities and their rank would be compared with other individuals who are ranked near equally in the same specific capabilities. This could be accomplished through a SQL query on the tables that contain this data. This gives the result of Similar Earnings (1003). The comparator(1005) then compares the observed-user’s actual earnings (1004) with their peer’s earnings (1003). The resulting value would include the necessary data for the calculations in Viability (1042). This would likely include the actual amount, and a positive or negative difference amount.

[0091] Knowledge / Skills Demand Fig. 10 (1011) is determined by calculating an individual’s sustainability (1012), knowledge / skills historic data (1013), availability of employment (1014) and specifically the vacancies in the observed-user’s vicinity (1014a), threats (1015) and specifically innovative disruption through technology (1015a) and its’ rate of adoption (1015b). The outlook (1016) which gives a timeline based on if the job posting history (1013a) is showing that demand is growing or receding and if there is current demand (1013b) as well as demand from other signals for the skill / knowledge such as RFP’s (1013c) and current RFPs (1013d). If demand is growing and not seasonal, then the outlook is positive, the inverse is true as well. Outlook considers threats from technological disruptions and the adoption rate. For instance, if a technological disruption is against academia, the adoption rate would be different than from a disruption against industry.

[0092] Health Risk Calculation (1021) is important to understanding. Many individuals will face medical issues that can be mitigated through healthy activities. Medical information may be provided from medical companies when authorized. It may be sent encrypted, and or ambiguated simply to a health report such as “Healthy” or may be more detailed. While medical records can report medical threats (1023), sports and fitness data can report an individual’s patterns of fitness. Activity history (1022) provides signals that the individual is active and healthy and helps to reduce creditor risk. Further, fitness activity is unopinionated, has little economic gain or loss, and is difficult to manipulate thus increasing its Category of Trust to the highest levels while medical data has a high probability for error and a low category of trust. Activity history can be derived from many of the instrumented devices (1054) such as the Apple Watch or Garmin Fitness Watch. Activity may be ambiguated by the originating source or in this invention to ensure appropriate privacy. This information is used to calculate outlook (1024) which is then sent to Viability (1042) when requested.

[0093] Viability Fig. 10 (1042) considers individual sustainability (1015) as a risk factor.Sustainability is derived from the competitive positioning comparator (1015a) which considers if the observed-user is overvalued or undervalued in comparison to earnings of equivalent individuals in capabilities, if any, locally. An individual may be competitive enough to advance if his positioning and pay are lower than his peers in knowledge credibility. The inverse would signal a lack of competitiveness to viability and risk. As mentioned previously, not all domains are the same. What is important to STEM domains is not as important to other domains. Impact (1012b) may be more important to many non-STEM fields. Impact in this case may be shown from projects or from a contract amount that the observed-user was responsible for capturing, but it is not the observed-user’s personal revenue. If the impact of the observed-user’s capabilities is lower than his peers-by-pay, this signals risk. If, however, the individual’s impact is far greaterthan his peers-by-pay, this signals growth opportunity and sustainability. Viability (1042) also considers self-improvement patterns, for Improvement and Growth (1012c). If the observed-user has a frequent improvement pattern in valuable knowledge, then this improves the observed-user’s competitiveness score.

[0094] Loan Risk Calculation (1031) is derived from historical payment risk data (1032), the amount that the individual will be required to pay (1033), and the duration of the loan (1034). The historical risk data (1032) receives credit data (1055) from the credit industry or credit bureaus. It may be in the form of a credit score, or payment and loan data. It may take into account an individual’s bill and rent payments, and late or unpaid payments.

[0095] Economic risk (1041) is calculated through an understanding of an individual’s viability (1042), and loan payment risk (1043). By understanding an individual’s capabilities and how they compare with a capabilities market space. If that capability is currently in demand, if the demand is growing or waning, if there are threats to the capability, the scarcity of the capability, and if the individual has a positive improvement pattern. If there are threats to a capability, such as disruption due to technology, then the estimated time for the adoption of the threat is also estimated. Earnings potential (1001) estimates an individual’s ability to continue to earn and forecasts this ability based on if they are over-valued or under-valued. If they are over-valued geographically, but not as a whole this is different than if they are over-valued as a whole and is considered a threat, unless they have significant impact.

[0096] Fig. 11 computations and calculations for Financial Sustainability.

[0097] To provide the output of Earning Potential (1001), we use the Earning Comparator represented as a flow chart (1005). The pay for the observed-user’s capabilities is shown simply as “user”. The peers by capabilities pay is shown as “peer”. We are determining if the “user pay” is: Equal to “peer pay”, greater than “peer pay”, or less than “peer pay”. If it is not equal, we test if the difference is a high or low amount. For example, we allowed a variance of pay when it is “equal” to be within 4% higher or lower. If it is 12% above or below, then it is a high amount. If it is less, then it is a low amount. The results (1001) are used later in the viability calculation (1042). The amounts, .04 and .12, are variable based on the model discovery but are shown as an example.

[0098] Fig. 11 (1111) Sustainability is calculated using two matrices. In the example matrix (1112), we determine the observed-users Competitive Positioning in increments of 10% and is represented by the Y axis. If the user is between 80% and 90% then the correct row would be 80%. Impact is on the X axis. Depending on an observed-user’s competitive positioning and impact, we output the results based on the column and row. In the example, each element is an adjusted rating from 0.5 to 0 based on its risk where 0 is higher risk. Next, matrix (1113) uses the product of Improvement Pattern calculations (1012c) that determine the X axis based on the output being improving, sustaining, or none. The Y axis is the output from Matrix (1112) or Competitive and impact positioning that are rated good, moderate and low. Good may be equal toor greater than 0.4, low may be equal to or less than 0.1, and moderate everything between. In the example, the sum of the two matrices is the output multiplier. If Sustainability’s total possible value is 250 points, then the result would be the multiple of the two values given in Sustainability Output (1012). The results are used in Viability (1042).

[0099] Fig. 11 (1121) Knowledge / Skills Demand is calculated using matrices. The example matrix (1122) shows Knowledge Skills History (1013) on the Y axis with points based on if capabilities are growing in demand, sustaining, or declining in demand. The X axis is current Job Availability (1014). Similar to the above, the elements are adjusted weightings. The output from Matrix 1122 is output to Knowledge / Skills Demand (1011). Matrix (1123) calculates risk from Innovative against time. Innovative Disruption (1015a) is represented on the Y axis. It is divided into a No threats or “None”, “Weak” threats, or “Strong” threats. The X axis is the Adoption Timeline (1015b) which indicates that adoption is occurring rapidly “Now” and the observed-user may soon be at risk of unemployment unless they are improving their retainability. Adoption is then divided into years 1 through 7. With 7 years being 7 years or more. The output is provided directly in Knowledge / Skills Demand (1011).

[0100] Finally, Fig. 11 (1131) provides matrices for Health Risk (1011). Fitness Activity (1022) is represented by matrix (1132). Row data is frequency of activity and is divided into “Active”, “Marginal”, and “Inactive”. Column data represents if the observed-user is “Improving” in fitness, “Sustaining”, or “Declining” and is represented as such. Matrix 1132 is output to Health Risk (1011) of Viability (1042). Medical Assessment data (1023) is represented by a single column matrix (1133) and is represented as “Healthy”, “average” and “surviving”. Matrix (1133) is output to Health Risk (1011) of Viability (1042).

[0101] Fig.s 12 / 1 and 12 / 2 are a flow chart for Viability Fig. 10 (1042). This is an example of the decision process. Implementation may be different in practice and take advantage of data structures and parallel processing for performance improvement.

[0102] The output from Earning Potential, Fig. 10 (1001) also Fig. 11 (1001) is compared in the comparator Fig 12 / 1 (1005). The results may be that the observed-user is underpaid by some amount in comparison to his peers by capability, neutral, or overvalued. This description walks through the “Overvalued” steps, but the other two or more steps (more since the comparator may be more granular and include more divisions than is displayed) would be similar.

[0103] If the observed-user is overvalued, the next step is to check their sustainability. Being that they are overvalued, if the output from Sustainability is greater or equal to 12 we next check if the output from Demand is growing and is greater than or equal to 3. If the result is false or no, then they are at risk of unemployment and the analysis is terminated. If the result is true or yes, then we move to the loan assessments. The next step asks if it is a Mortgage or is there an asset used as collateral that can be used in the case of a loss. The results move us into Fig. 12 / 2.

[0104] Fig. 12 / 2, If the results are that the loan type is a Mortgage or has an asset, the next step is to check if the output from Adoption is greater than or equal to 9. If not, the next step is tocheck if the output from Retainability and Growth is equal to “Improving”. If not, then the observed-user is a “RISK”. If “Adoption” is greater or equal to 9, then the next step is to check Fitness output. If the results are yes, then the analysis passes. If not, then it is a “RISK”.

[0105] The following are supplemental information on how to complete the tasks described above and may be implemented by someone familiar in the art.

[0106] Data-Integrity Fig. 5 (503) is common and may be accomplished through hashing algorithms of the data and sending the hash using encryption along with the data. This ensures that the data is sent from a trusted source, and that it was not changed between its source and the server.

[0107] Data Synchronization Fig. 5 (504) ensures that data stored is correct at all points.Provides the necessary actions to store the information in database tables so it may be efficiently stored and easily retrieved according to the needs of this invention.

[0108] A search by a specific domain or sub-domain may be conducted on this platform using an implementation where the observed-users were scored based upon the metrics and methods of this invention. This could be through a number of implementations such as a webform or an options list that connects to the webserver. The server would conduct a search using a SQL query to the database, or through a software program specifically developed for this purpose, or through Al.

[0109] An enumerated list of observed-users and entities may be created based on the calculations and methods of this invention that may be transmitted to other entities that would be interested in reporting ratings of observed-users such as web-based search engines such as Google or Bing.

[0110] Observed-users would have the option to elect what data is available to be viewed by an observer user or if they may be viewable at all. Further they may elect to remove all data that is related to them.

[0111] Calculations: The following provides examples for the calculations required by the explanatory Knowledge Credibility metrics of this invention. They are not intended to limit this invention to these methods of calculations but to provide a working example to someone reasonably familiar with software engineering and computer science concepts.

[0112] The “users with equivalent metrics work at xxxx” Fig. 3 / 1 (310) and calculated in the server, Fig. 5 (515), are derived from the SQL database with a reference to the observed-user, the domains that are associated with observed-user, as well as their ratings derived from the other calculations of this invention. The SQL tables would further associate the observed-user with the companies they have worked for. A comparison of individuals who are currently employed by we 11 -respected companies and the observed-user would be made. This embodiment would derive the domains of the observed-user and base calculations on various aspects of critical knowledge domains. Depending on the category of trust that is selected, for example, for Category I, onlyinformation that is known to be free of errors, it would include individuals with similar knowledge domains, depth of knowledge, and a minimum time in practice, study, discussion, and creating within these domains. If the user selects a lower / weaker category of trust, the data that is used for that category is included in the calculation. Another calculation strategy may be to rate individuals according to the category of trust within relevant domains and compare the observed-user with other individuals who share a similar percentile, then return the companies with the largest earnings and scale that has a relevant minimum number of individuals that are similar.

[0113] The ‘Knowledge requested by industry’, shown in Fig. 3 / 1 (310) and calculated in the server, Fig. 5 (514), is derived from job requests and an association with knowledge, time and the metrics that are in a category of trust. Some skillsets may require an individual who may be a social media marketer to have demonstrated experience as a social media influencer. The skills needed may be video editing, sound editing, understanding of cameras and lighting, an understanding of communications using appropriate language for the audience, and an understanding of video and image composition to name a few. The comparison of the requested skills and experience in industry versus the observed-user’s skills and experience is shown. The calculation can be accomplished using SQL where job postings have been scanned from websites like Linkedln, GlassDoor or uploaded to this invention directly through queries made by hiring managers for example. Normalization of required knowledge could be accomplished through data structures that provide a one-to-many and many-to-one relationship such as provided by a hashtable with a linked list that may be built through the use of Machine Learning where models are trained on large data-sets of job postings. As a job posting is uploaded to storage, the skills are compared to required knowledge forthose skills. The nodes in the linked-list contain a variable for experience as time in months. When the request for the observed-user is made, a comparison can be made by requesting the skill needed by industry, and time with the observed-user’s knowledge and time. A percentage or measurement is returned and displayed.

[0114] The value of knowledge Fig. 3 (303) and Fig. 5 (509) is an important metric because it helps to prove the significance of the observed-user and the knowledge. Included is a time metric since a value is difficult to understand without a time metric. The value may come from multiple sources such as bank account deposits from an employer or a contractee that the observed-user is working with. The value may come through the API Fig. 6 (600) provided to applications that, for example, allow the sale of study materials or course videos. The values along with a time stamp would be stored in a database table along with the source and if available the domain-of- knowledge. When the observed-user’s metrics are displayed, a query for the indicated time frame, the amount, and the domain, is requested from the observed-user’s unique id. The query would return one line for each domain with the sum from the combined amounts under that domain.

[0115] The hours of effort Fig. 3 (304) and fig. 5 (507) is an important metric because it represents the total number of captured hours of effort within a domain-of-knowledge. It can be a positive indicator of an observed-user’s dedication and work ethic. The metric of time is brokeninto sub-elements and in this example, they are study, create, discuss, and practice and there is a total time. The preferred method for calculating time is to sum the total time along with a SQL query to the same database as the previous queries. The reported hours would be included as an entry from devices that connected through an API or directly through the user’s on-board app that may be running in the background. The query would sum the time in each sub-metric and then the total sum may be conducted in the query or in the presentation software. The query would request the time by domain and by the unique ID of the observed-user.

[0116] The points from study, fig. 3 (305) and included in fig. 5 (508) is an important metric because it indicates interaction with knowledge, or work and displays learning within a domain- of-knowledge. This is also an observable sign of understanding and an important metric for new entrants to the work force. In the example the sub-metrics are related to the types of questions and retrieval. More points may be awarded that provides a preference to the use of logic and using different memory types. The two shown sub-metrics are testing and free-recall, but others may also be included. Similar to previous queries, the preferred method would be to store information from study applications and advanced learning systems that are connected through the use of an API or that may embed software that would store the information directly into the Database. The query based on the users ID would return the information by its sub metrics as a sum of each domain, and then sum the totals as the overall score.

[0117] The optional section for citations, votes, etc... fig. 3 (306) and fig. 5 (510) are displayed based upon the categories of trust. The information may be introduced by the observed-user, captured and validated through a scan of websites such as the IEEE or other professional publication, or reported directly from publications through the API. Depending on the credibility of the publication and the cost to reputation and or economic cost of inaccurate reports, the category of trust would be determined. For example, the website may report a citation request as a citation, this would be a poor and inaccurate method. A superior form of citation is from a known source where the citation is reported and can be accounted for separately. E.G. another publication cited report xxxxx article from the observed-user. These reports are considered class II or lower since they are not a direct observation that the user has knowledge. Rather, they are an acknowledgement.

[0118] Similarly, data from other forms of confirmation of effort and knowledge may come from other sources. E.g. from Facebook as a vote, or as a follower. Social Media’s business model does not reward factual information, it rewards a growth of users, and usage since advertisers need more views and engagement. These types of confirmation are not believable except through abstraction such as that the observed-user has followers and engagement. This type of understanding is an important metric to some domains such as marketing and communications. Although the metrics can be faked, thus they are not believed to be free of errors. Similar to the previous calculations, the data would be captured through an API provided from this invention, or by providing direct access to store memory from the software that is providing the information.For instance, Linkedln may have direct access to the databases referred to earlier. A publication such as IEEE may access through the API or is scanned by software for this invention.

[0119] Assigning Categoric s-of-Trust to sources is carried out by creating a white list of approved origins. E.g. if an originating source is third party software, and is directly connected through an API, and the originating source observes the user creating, editing, discussing, practicing, studying, or testing in a domain-of-knowledge, then it would be category I since it is unquestionably from a user and is unquestionably in a domain-of-knowledge. The assignment of the category to the sources organization may be based upon human approval, through a questionnaire given to the third-party vender when they signup to use the API, or through an automated testing and auditing system that ensures that standards are met and assigns a category based on the results. When a third-party company, or vendor, fdls in a form to access the inventions API’s. An entry could be provided that decides the category-of-trust. The database would then associate the category-of-trust with the vendor. Later after data from a user of the third party’s software is uploaded to this invention. The vendor as a source is queried in the database and their category-of-trust is used during calculations and display.

[0120] The ‘Knowledge Requested by Industry Comparison’ Fig. 5 (514) is collected from industry users as they search for users who have certain knowledge. It is also collected by a voting system, through direct questioning, surveys, and by a comparison of individuals in similar job positions. This data is then stored for later use to calculate and show the amount of knowledge that the user has interacted with that is relevant to a particular industry. The observed-user’s data is then compared to a standard of the individuals that the requester-user sets. The SQL request returns a comparison value against the knowledge requested by the individual. A generic, or default average may also be used.

[0121] Percentile calculation strategies:

[0122] There are a few strategies that may be used. Multiple calculations are made based on a plurality of data from a plurality of sources and dependent on the selected category-of-trust Fig.3 / 1 (301). The Percentile by Domain Process is calculated from time Fig. 5 (507), points (508), value or significance (509), Votes / Citations if available (510), entities (512a) that the observeduser has worked with or for; their knowledge (512b) metrics, depth of knowledge, and rarity; the people (512c) they have interacted with and their score or percentile; the projects (512d) they have been involved with; their achievements (512e), and their positions of responsibility (512f).

[0123] The knowledge metrics may be derived from multiple relevant sub-domains and their sub-metrics. Hours is divided into the sub-metrics as described in this invention and shown in Fig 3 / 1 and calculated in Fig 5.

[0124] If an Al is used it may report its accuracy and similarly if all categories are used in the calculation, it may report the possible error rate such as 5% error. The error may be calculated by the entirety of a category of trust. If Category 4 is used, an unobserved category, and it is unknown if the data contains errors. E.g. if the votes on a Linkedln post were paid for, the voteswould be fake or considered erroneous. Thus, the error rate would be by the amount that the votes contributed to the whole of the scoring or percentile.

[0125] Significance. E.g. Trinity for Oppenheimer and his team, set them apart from the rest of all other nuclear physicists. This may be a multiplier or an addition of a set number of points within the field. For most individuals, significance may be measured by value and may add a percentage of the value they’ve brought in comparison to the value of others. An exemplar would be for a top contributor a ‘.1’ multiplier by the points in that domain.

[0126] Calculating knowledge credibility gained from interactions with other individuals. There are a few strategies that may be used.

[0127] The first strategy, the observed-user gains from only the interactions they’ve made between the other individuals. This is explained in this invention and the observed-user gains no real advantage except through the direct interactions with the other individual and is more of a pure calculation.

[0128] The second strategy, the observed-user gains a fraction from individuals they interact with, within a domain of knowledge. Fig. 5 (513) to use other individual’s percentile as a part of person- A’s positioning would be: Step 1. Restrict the calculation to only persons within the domain. Step 2. Ensure there are interactions related to the domain. Step 3. Calculate all dependent areas. Step 4. Score persons 1 - n based on dependent information. Step 5. Provide some percentage of the top individuals score to those they are connected with. Step 6. Then recalculate step 5. By providing a small percentage from each higher person. If person A attended a top university and was taught by top professors, they would gain percentages from these position scorings. Next if individual A worked for a top organization, they would gain further points from that organization. As they worked with new individuals who are top individuals in that domain, they gain further points. In this strategy, the points gained from each entity and individual could be a small fraction. Over time, due to the number of top people and entities they’ve interacted with, these percentages would add up to a significant points gain. Strategy 1 works because the discussions would capture interaction with knowledge. Strategy 2 requires tuning the percentage gains to prevent wild or unrealistic gains but allows for interactions that are not captured by hardware or software. In the case that person B is J.R. Oppenheimer on the Manhattan Project. The conversation and effort may not be captured, but the relationship between person A and B could still be shown.

[0129] This calculation can be performed once without much difficulty, but problems begin when there are multiple people, and the scoring / positioning calculations must be repeated. Since person A’s score depends on person B and C. and person B relies on A and C, and C relies on both A and B, etc... .

[0130] Strategy 2 creates a problem. This is a complex calculation because the scoring depends on other individuals who also depend on each other. A naive implementation, even for a small number of individuals, would cause a thread lock problem. Advanced techniques to block, forinstance an individual’s (person A) scoring that is dependent on other individuals (persons 1 - n) scoring. Persons 1 - n are dependent upon person A’s scoring. Thus persons 1 - n would need to be completed either with or without person A’s data and may be completed asynchronously using special libraries such as provided in Java by Rice University by the PCDP library. To make this calculation, strategies would block, remove, or delay ‘person A’ from being scored until all other person’s 1 - n are completed. Note the problem here is that persons 1 - n are also person A. A solution for this is Pascal’s triangle using a recursive task and implementing Java Futures and Memoization. Memoization stores temporary results needed per calculation and provides locks and releases. Futures organizes and provides a mechanism to coordinate the complex calculation efficiently by performing a partial calculation, then stopping and waiting until a thread is called sometime in the future, when the needed data for the calculation is completed. The knowledge to implement this is explained in more detail by courses in multi-core parallel-processing and specifically by a course available online through Rice EDU. This method is designed to quickly process large amounts of data efficiently, and without errors.

[0131] Definitions: The following are to provide further context and clarity.

[0132] Knowledge credibility is a measurement of the probability of correctness or infallibility of an individual, an organization of individuals (such as those at a company), or software (such as Al) when they provide a statement, make a decision, or deliver work within a domain-of- knowledge. An individual may be said to have skills, and the individual may have knowledge in order to have the skills, but skills and knowledge are not the same. A professional mountain-bike racer may have the ability to race down a rocky and difficult path at near verticle inclines but not possess the ability to explain how force is applied to maintain stability and direction. Eikewise, expertise and talent may refer to the application to do work without understanding the underlying information needed for such work. They refer to the ‘about’ rather than ‘how’. Example, a software developer may have the ability to write software and use API’s to develop a product but not possess the understanding of how processors work, how data may be manipulated using advanced data structures, nor how information is stored in memory. The developer may be considered an expert and to possess skills, but not possess a deeper theoretical understanding.

[0133] A domain is a division, sub-division / sub-domain, or section of a sub-division within an area of knowledge, practice, concentration, field, or other area of knowledge as recognized by academia or industry. It refers to both STEM and non-STEM fields and would be recognized in industry or academia as such. Some examples would be Mathematics to Calculus to MultiVariable Division, Law to Divorce Law, Marketing to SEO Optimization, or Physics to Quantum Mechanics. Etc... .

[0134] Interaction is the activity of creating, reading, observing, listening, or discussing, and practicing.

[0135] Practice is the activity of being actively engaged within a domain of interest such as practicing business or conducting research and where it is a captured activity by software or othermeans that is related to a category. Examples of practicing is solving problems, writing software, marketing, communicating, banking, and other practices that are related to STEM (science, technology, engineering, and mathematics) and non-STEM but in the day-to-day conduct of one’s profession or in solving problems such as may be done in academia.

[0136] Study time represents where a user has actively studied the domain be it through reading, observing, listening, or studying for example using a flashcard like system, and where the activity can be captured and represented by its category.

[0137] Discuss represents discussions and may be time spent in conversation or videochat or similar systems where the discussion may be recorded by a category of capture.

[0138] Create is where the user is actively creating knowledge for consumption by others within the domain-of-knowledge.

[0139] Percentile displays the user’s related percentile within a domain-of-knowledge and related to an entity Fig. 13 (1301) such as a university or employer.

[0140] Observed is when the software that is connected to this invention, creates data for this invention while the user is 1) practicing, 2) creating, 3) discussing, 4) studying.

[0141] Positional and positional data is ranking, hierarchal, and rating data that compares individuals with each other based on metrics or performance.

Claims

Claims1. A computer-implemented method comprising of:a. providing an understanding of an individual or entity’s financial sustainability; b. wherein said financial sustainability is derived from data / information;c. wherein said data / information may be rated based on trust,i. wherein said rating is based on the originating source being free of incorrect data;ii. wherein said understanding originates from a plurality of sources such as external data-mining searches, 3rd party software, or hardware which have a plurality of purposes that are categorized by said rating;1. wherein said originating sources may be from data that is collected actively or from existing sources; andiii. wherein said rating may be divided into segments, “categories”, based on the data’s probability of being error free;d. wherein said data provides, or is used to provide, an understanding of an individual’s capabilities,i. wherein capabilities comprises of talent, skills, expertise, or knowledge credibility; andii. wherein capabilities is used in the calculation of earnings potential; and e. providing a display, or communicating the said understanding of an individual or entity’s financial sustainability.

2. The method of claim 1 wherein the understanding of an individual’s financial sustainability may further include health and fitness;a. wherein said understanding of health and fitness may be provided using data from instrumented sports devices;b. wherein said health understanding may be understood using medical data; and c. wherein the said understanding of the individual’s health and fitness may be displayed or communicated including in a generalized form; and3. The method of claim 1 wherein the understanding of an individual’s financial sustainability further includes demand for the said capabilities; anda. wherein an understanding of the demand for the individual or entity’s capabilities is displayed or communicated.

4. The method of claim 1, wherein the said financial sustainability, is combined with historical credit data, and may be used to understand an individual or entity’s future ability to repay debt; anda. wherein an understanding of the calculation and result of the said ability to repay debt is displayed or communicated.

5. A system comprising of:a. providing an understanding of an individual or entity’s financial sustainability;b. wherein said financial sustainability is derived from data / information;c. wherein said data / information may be rated based on trust;i. wherein said rating is based on the originating source being free of incorrect data;ii. wherein said understanding originates from a plurality of sources such as external data-mining searches, 3rd party software, or hardware which have a plurality of purposes that are categorized by said rating; and iii. wherein said rating may be divided into segments based on the data’s probability of being error free;d. wherein said data provides, or is used to provide, an understanding of an individual’s capabilities,i. wherein capabilities comprises talent, skills, expertise, or knowledge credibility;andii. wherein capabilities is used in the calculation of earnings potential; and e. providing a display, or communicating the said understanding of an individual or entity’s financial sustainability.

6. The system of claim 5 wherein the understanding of an individual’s financial sustainability may further include health and fitness;a. wherein said understanding of health and fitness may be provided using data from instrumented sports devices;b. wherein said health understanding may be understood using medical data; and c. wherein the said understanding of the individual’s health and fitness may be displayed or communicated including in a generalized form; and7. The system of claim 5 wherein the understanding of an individual’s financial sustainability further includes demand for the said capabilities; anda. wherein an understanding of the demand for the individual or entity’s capabilities is displayed or communicated.

8. The system of claim 5, wherein the said financial sustainability, is combined with historical credit data, and may be used to understand an individual or entity’s future ability to repay debt; anda. wherein an understanding of the calculation and result of the said ability to repay debt is displayed or communicated.

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