# US 2019/0378206 A1 · Computerized Relevance Scoring Engine For Identifying Potential Investors For A New Business Entity > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0378206 A1 - Document ID: US20190378206A1 - Status: Published application - Kind: A1 - Application number: 16/002,929 - Filed: 2018-06-07 - Published: 2019-12-12 - Earliest claimed: 2018-06-07 - Assignees: AINGEL Corp. - Applicants: Aingel Corp. - Inventors: Amr Shady; OmarEbnElkhattab Hosney; Prasant Sudhakaran; Hariraj Jayakumar - Web page: https://patentgrep.com/patent/US20190378206A1 ## CPC - G06Q 40/00: Finance; Insurance; Tax strategies; Processing of corporate or income taxes - G06F 16/248: Presentation of query results - 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 The embodiments described herein provide a mechanism for inputting a set of criteria regarding a startup technology company, analyzing all existing data regarding all known venture capital firms, generating a relevancy score for each of the venture capital firms as to that particular startup technology company, and providing a report identifying the venture capital firms who are most likely to invest in the startup technology company and identifying the relevant companies in which that venture capital firm has invested in the past. ## Claims ### Claim 1 A method of generating relevance scores for one or more potential investors for a company, comprising: receiving, by a relevance scoring engine running on a computing device, an input dataset, the input dataset comprising a textual description of the company; processing, by a keyword module running on the computing device, the input dataset to generate a company keyword dataset; generating, by a data acquisition module running on the computing device, an investor dataset comprising data on known investors; processing, by the keyword module, the investor dataset to generate an investor keyword dataset; and generating, by a data analysis module running on the computing device, an investor relevance score for each known investor, the investor relevance score generated based upon the company keyword dataset and the investor keyword dataset. ### Claim 2 (depends on 1) The method of claim 1, further comprising: generating a report indicating the investor relevance score for one or more of the known investors. ### Claim 3 (depends on 1) The method of claim 1, further comprising: generating a report comprising a ranked list of known investors based upon investor relevance scores. ### Claim 4 (depends on 1) The method of claim 1, further comprising: generating for each known investor, by the data analysis module, a company relevance score for each company in which the known investor has invested in the past; generating for each known investor, by the data analysis module, a recency score, the recency score indicating the recency of investments by the known investor in a company with a company relevance score above a predetermined threshold; generating for each known investor, by the data analysis module, a frequency score, the frequency score indicating the frequency of investments by each known investor in a company with a company relevance score above a predetermined threshold; generating, by the data analysis module, an overall score for each known investor based upon the investor relevance score, the recency score, and the frequency score for the known investor. ### Claim 5 (depends on 4) The method of claim 4, further comprising: generating a report indicating the overall score for one or more of the known investors. ### Claim 6 (depends on 4) The method of claim 4, further comprising: generating a report comprising a ranked list of known investors based upon overall score. ### Claim 7 (depends on 1) The method of claim 1, wherein the investor dataset comprises a textual summary of previous investments by the investor. ### Claim 8 (depends on 1) The method of claim 1, wherein the investor dataset comprises data on known investors obtained from one or more web servers. ### Claim 9 The method of claim, 1, wherein the investor dataset comprises data obtained from servers using APIs. ### Claim 10 (depends on 1) The method of claim 1, wherein the investor relevance score is normalized. ### Claim 11 A computing device comprising a processing unit and memory, the processing unit configured to execute instructions in memory for performing the following steps: receiving, by a relevance scoring engine running on the computing device, an input dataset, the input dataset comprising a textual description of the business entity; processing, by a keyword module, the input dataset to generate a company keyword dataset; generating, by a data acquisition module running on the computing device, an investor dataset comprising data on known investors; processing, by the keyword module, the investor dataset to generate an investor keyword dataset; and generating, by a data analysis module running on the computing device, an investor relevance score for each known investor, the investor relevance score generated based upon the company keyword dataset and, the investor keyword dataset. ### Claim 12 (depends on 11) The computing device of claim 11, wherein the processing unit is further configured to execute an instruction in memory for generating a report indicating the investor relevance score for one or more of the known investors. ### Claim 13 (depends on 11) The computing device of claim 11, wherein the processing unit is further configured to execute an instruction in memory for generating a report comprising a ranked list of known investors based upon investor relevance scores. ### Claim 14 (depends on 11) The computing device of claim 11, wherein the processing unit is further configured to execute instructions in memory for performing the following steps: generating for each known investor, by the data analysis module, a company relevance score for each company in which the known investor has invested in the past; generating for each known investor, by the data analysis module, a recency score, the recency score indicating the recency of investments by the known investor in a company with a company relevance score above a predetermined threshold; generating for each known investor, by the data analysis module, a frequency score, the frequency score indicating the frequency of investments by each known investor in a company with a company relevance score above a predetermined threshold; generating, by the data analysis module, an overall score for each known investor based upon the investor relevance score, the recency score, and the frequency score for the known investor. ### Claim 15 (depends on 14) The computing device of claim 14, wherein the processing unit is further configured to execute an instruction in memory for generating a report indicating the overall score for one or more of the known investors. ### Claim 16 (depends on 14) The computing device of claim 14, wherein the processing unit is further configured to execute an instruction in memory for generating a report comprising a ranked list of known investors based upon overall score. ### Claim 17 (depends on 11) The computing device of claim 11, wherein the investor dataset comprises a textual summary of previous investments by the investor. ### Claim 18 (depends on 11) The computing device of claim 11, wherein the investor dataset comprises data on known investors obtained from one or more web servers. ### Claim 19 (depends on 11) The computing device of claim 11, wherein the investor dataset comprises data obtained from servers using APIs. ### Claim 20 (depends on 11) The computing device of claim 11, wherein the investor relevance score is normalized.