# US 12,002,096 B1 · Artificial intelligence supported valuation platform > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,002,096 B1 - Document ID: US12002096B1 - Status: Granted - Kind: B1 - Application number: 17/883,351 - Filed: 2022-08-08 - Granted: 2024-06-04 - Earliest claimed: 2018-12-03 - Assignees: Massachusetts Mutual Life Insurance Company - Applicants: Massachusetts Mutual Life Insurance Company - Inventors: David Lipke; Nailong Zhang - Web page: https://patentgrep.com/patent/US12002096B1 ## CPC - G06Q 40/06: Asset management; Financial planning or analysis - G06F 16/951: Indexing; Web crawling techniques - G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06F 18/2148: {characterised by the process organisation or structure, e.g. boosting cascade} - G06F 18/24147: {Distances to closest patterns, e.g. nearest neighbour classification} - G06N 3/045: Combinations of networks - G06N 3/0499: Feedforward networks - G06N 3/09: Supervised learning - G06N 3/098: Distributed learning, e.g. federated learning - G06N 20/20: Ensemble learning - G06N 3/08: Learning methods - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Disclosed are method and systems to program a server to identify the value of a fund comprising shares of multiple private entities. The server receives transaction data associated with a fund where the transaction data identifies a proportion of shares within the fund associated with each private entity, price per share of each private entity, and other relevant data. The server then executes multiple artificial intelligence models to identify comparable public entities to each private entity. The server then retrieves stock price data for each public entity and calculates a value for each private entity in real time. The server also displays a value of the fund in real time where identification of each private entity is anonymized. ## Claims ### Claim 1 A method comprising: executing, by a processor, a set of artificial intelligence models to identify a plurality of comparable public entities comparable to a plurality of private entities, the set of artificial intelligence models comprising at least a first artificial intelligence model utilizing a distance k-nearest algorithm to identify the plurality of comparable public entities and a second artificial intelligence model utilizing a linear regression algorithm to identify the plurality of comparable public entities, wherein the set of artificial intelligence models are trained in accordance with a training dataset comprising known attributes of a set of public entities and a corresponding valuation for each public entity within the set of public entities; determining, by the server, a value for each private entity based upon its respective identified comparable public entity; and displaying, by the server on a graphical user interface, an indicator of a value of a fund comprising the plurality of public entities, the graphical user interface comprising a value of each private entity of the fund where an identity of each private entity is anonymized. ### Claim 2 (depends on 1) The method of claim 1, wherein the set of artificial intelligence models further comprises: a third artificial intelligence model utilizing a boosting tree regression algorithm to identify the plurality of comparable public entities. ### Claim 3 (depends on 1) The method of claim 1, further comprising: updating, by the server, the indicator in real time in accordance with modifications in financial data associated with at least one identified comparable public entity. ### Claim 4 (depends on 3) The method of claim 3, wherein the graphical user interface displays a trend associated changes associated with the updated indicator for a predetermined time. ### Claim 5 (depends on 1) The method of claim 1, wherein the identified comparable public entities are also displayed on the graphical user interface. ### Claim 6 (depends on 1) The method of claim 1, wherein an application programming interface protocol notifies the server when financial data of an identified comparable public entity has been modified. ### Claim 7 (depends on 1) The method of claim 1, further comprising: crawling, by the server, one or more databases to identify one or more web documents associated with at least one private entity; and displaying, by the server, an indicator on the graphical user interface indicating identification of at least one web document associated with at least one private entity. ### Claim 8 (depends on 1) The method of claim 1, further comprising: generating, by the server, a value associated with the fund for a period of time. ### Claim 9 (depends on 8) The method of claim 8, wherein the period of time is customizable by a user interacting with the graphical user interface. ### Claim 10 A system comprising: a server in communication with a non-transitory storage medium having a set of instructions that, when executed, cause a processor of the server to: execute a set of artificial intelligence models to identify a plurality of comparable public entities comparable to a plurality of private entities, the set of artificial intelligence models comprising at least a first artificial intelligence model utilizing a distance k-nearest algorithm to identify the plurality of comparable public entities and a second artificial intelligence model utilizing a linear regression algorithm to identify the plurality of comparable public entities, wherein the set of artificial intelligence models are trained in accordance with a training dataset comprising known attributes of a set of public entities and a corresponding valuation for each public entity within the set of public entities; determine a value for each private entity based upon its respective identified public entity; and display, on a graphical user interface, an indicator of a value of a fund comprising the plurality of public entities, the graphical user interface comprising a value of each private entity of the fund where an identity of each private entity is anonymized. ### Claim 11 (depends on 10) The system of claim 10, wherein the set of artificial intelligence models further comprises: a third artificial intelligence model utilizing a boosting tree regression algorithm to identify the plurality of comparable public entities. ### Claim 12 (depends on 10) The system of claim 10, wherein the set of instructions further cause the processor to: update the indicator in real time in accordance with modifications in financial data associated with at least one identified comparable public entity. ### Claim 13 (depends on 12) The system of claim 12, wherein the graphical user interface displays a trend associated changes associated with the updated indicator for a predetermined time. ### Claim 14 (depends on 10) The system of claim 10, wherein the identified comparable public entities are also displayed on the graphical user interface. ### Claim 15 (depends on 10) The system of claim 10, wherein an application programming interface protocol notifies the server when financial data of an identified comparable public entity has been modified. ### Claim 16 (depends on 10) The system of claim 10, wherein the set of instructions further cause the processor to: crawl one or more databases to identify one or more web documents associated with at least one private entity; and display an indicator on the graphical user interface indicating identification of at least one web document associated with at least one private entity. ### Claim 17 (depends on 10) The system of claim 10, wherein the set of instructions further cause the processor to: generating, by the server, a value associated with the fund for a period of time. ### Claim 18 (depends on 17) The system of claim 17, wherein the period of time is customizable by a user interacting with the graphical user interface. ### Claim 19 A system comprising: a set of artificial intelligence models comprising at least a first artificial intelligence model utilizing a distance k-nearest algorithm to identify at least one comparable public entities comparable to a private entity and a second artificial intelligence model utilizing a linear regression algorithm to identify at least one comparable public entity to the private entity; and a server having a processor, the server in communication with the set of artificial intelligence models, the server configured to: execute the set of artificial intelligence models to identify a plurality of comparable public entities comparable to a plurality of private entities, wherein the set of artificial intelligence models are trained in accordance with a training dataset comprising known attributes of a set of public entities and a corresponding valuation for each public entity within the set of public entities; determine a value for each private entity based upon its respective identified public entity; and display, on a graphical user interface, an indicator of a value of a fund comprising the plurality of public entities, the graphical user interface comprising a value of each private entity of the fund where an identity of each private entity is anonymized. ### Claim 20 (depends on 19) The system of claim 19, wherein the set of artificial intelligence models further comprises: a third artificial intelligence model utilizing a boosting tree regression algorithm to identify the plurality of comparable public entities.