# US 12,602,637 B2 · Systems and methods for client intake and management using risk parameters > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,602,637 B2 - Document ID: US12602637B2 - Status: Granted - Kind: B2 - Application number: 17/716,791 - Filed: 2022-04-08 - Published: 2023-01-12 - Granted: 2026-04-14 - Earliest claimed: 2021-07-09 - Assignees: Aci Holdings Ltd. - Applicants: ACI Holdings Ltd. - Inventors: Ahmed Farouk Shaaban; Venkat Thandra; Dino Eliopulos; Andrew Kenneth Blazaitis; Kennedy Muthukrishnan - Web page: https://patentgrep.com/patent/US12602637B2 ## CPC - G06Q 10/0635: Risk analysis of enterprise or organisation activities - G06F 16/282: {Hierarchical databases, e.g. IMS, LDAP data stores or Lotus Notes} - G06F 16/355: Creation or modification of classes or clusters - G06F 16/9035: Filtering based on additional data, e.g. user or group profiles - G06Q 50/18: Legal services - 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) ## Abstract Systems and methods for client intake and management are disclosed herein. In an. embodiment, a method for client intake and management by a first party includes receiving initial client data regarding a second party from at least one user terminal, retrieving additional client data regarding the second party from at least one of the second party, an existing second party database, or a third party data source, extracting target data from the additional client data, generating at least one risk parameter based on the target data, and determining a risk associated with the second party based on the at least one risk parameter. ## Claims ### Claim 1 A method for client intake and management by a first party, the method comprising: presenting, on a user terminal, a user interface enabling a user to enter a name of a second party that is a potential client; receiving, via the user interface, user input of initial client data including the name of the second party; transmitting the initial client data from the user terminal to a central server including a memory storing an extraction algorithm including a neural network and a matter database; searching the name of the second party in the matter database using a hierarchal searching system to calculate a risk assessment for the second party based at least partially on past hierarchal searching results; determining a conflict rating code based on subsequent search results from the searching the name of the second party and the past hierarchal searching results; determining a litigiousness factor weighted as part of the risk assessment for the second party by matching the user input of initial client data against the matter database on the central server; in response to not finding the name of the second party in the matter database, retrieving a document or website including additional client data regarding the second party from an existing second party database or a third party data source; based on classification of the document or website, using the extraction algorithm including the neural network to extract target data from the additional client data based on x and y coordinates defining a location on the document or website; receiving input regarding whether the target data at the x and y coordinates is accurate; when the input indicates that the extracted target data is accurate, using the extracted target data to further train the extraction algorithm including the neural network as a positive example of a location of x and y coordinates where target information is found on the classified document or website; when the input indicates that the extracted target data is not accurate, using the extracted target data to further train the extraction algorithm including the neural network as a negative example of the location of x and y coordinates where target information is found on the classified document or website; generating at least one risk parameter based on the target data; determining a risk associated with the second party based on the at least one risk parameter; updating the conflict rating code; and presenting the risk to the user using the user interface of the user terminal. ### Claim 2 (depends on 1) The method of claim 1, wherein retrieving additional client data includes retrieving additional client data from the existing second party database. ### Claim 3 (depends on 1) The method of claim 1, wherein retrieving additional client data includes retrieving additional client data from the third party data source. ### Claim 4 (depends on 1) The method of claim 1, comprising generating the at least one risk parameter includes using at least one reliability weight. ### Claim 5 (depends on 4) The method of claim 4, comprising adjusting the at least one reliability weight based on cross-referencing the additional client data and the initial client data. ### Claim 6 (depends on 1) The method of claim 1, wherein generating the at least one risk parameter includes using at least one category weight. ### Claim 7 (depends on 1) The method of claim 1, wherein the at least one risk parameter includes a numerical value. ### Claim 8 (depends on 1) The method of claim 1, wherein approving or rejecting the second party as a client based on the at least one risk parameter. ### Claim 9 A system for client intake and management by a first party, the system comprising: at least one user terminal including a user input device configured to receive initial client data regarding a second party, the at least one user terminal configured to present a user interface enabling a user to enter a name of a second party that is a potential client; and a central server including a processor and a memory, the memory storing an extraction algorithm including a neural network and a matter database, the processor programmed to execute instructions stored on the memory to cause the central server to: (i) receive the initial client data including the name of the second party from the at least one user terminal; (ii) searching the name of the second party in the matter database using a hierarchal searching system of conflict rating codes to calculate a risk assessment for the second party based at least partially on past hierarchal searching results; (iii) in response to not finding the name of the second party in the matter database, retrieve a document or website including additional client data regarding the second party from an existing second party database or a third party data source; (iv) based on classification of the document or website, using the extraction algorithm including the neural network to extract target data from the additional client data based on x and y coordinates defining a location on the document or website; (v) receive input regarding whether the target data at the x and y coordinates is accurate; (vi) when the input indicates that the extracted target data is accurate, use the extracted target data to further train the extraction algorithm including the neural network as a positive example of a location of x and y coordinates where target information is found on the classified document or website; (vii) when the input indicates that the extracted target data is not accurate, using the extracted target data to further train the extraction algorithm including the neural network as a negative example of the location of x and y coordinates where target information is found on the classified document or website; (viii) generate at least one risk parameter based on the target data; (ix) determine a risk associated with the second party based on the at least one risk parameter; (x) update a conflict rating code; and (xi) cause the risk to be presented to the user using the user interface of the at least one user terminal. ### Claim 10 (depends on 9) The system of claim 9, wherein the processor is programmed to retrieve the additional client data from the existing second party database. ### Claim 11 (depends on 9) The system of claim 9, wherein the processor is programmed to retrieve the additional client data from the third party data source. ### Claim 12 (depends on 9) The system of claim 9, wherein the processor is programmed to generate the at least one risk parameter using at least one reliability weight. ### Claim 13 (depends on 12) The system of claim 12, wherein the processor is programmed to adjust the at least one reliability weight based on cross-referencing the additional client data and the initial client data. ### Claim 14 (depends on 9) The system of claim 9, wherein the processor is programmed to generate the at least one risk parameter using at least one category weight. ### Claim 15 (depends on 9) The system of claim 9, wherein the at least one risk parameter includes a numerical value. ### Claim 16 (depends on 9) The system of claim 9, wherein the processor is programmed to approve or reject the second party as a client based on the risk parameter.