# US 12,216,683 B1 · Systems and methods for generating and implementing knowledge graphs for knowledge representation and analysis > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,216,683 B1 - Document ID: US12216683B1 - Status: Granted - Kind: B1 - Application number: 18/362,375 - Filed: 2023-07-31 - Granted: 2025-02-04 - Earliest claimed: 2016-05-12 - Assignees: Federal Home Loan Mortgage Corporation (Freddie Mac) - Applicants: Federal Home Loan Mortgage Corporation (Freddie Mac) - Inventors: Yuang Tang - Web page: https://patentgrep.com/patent/US12216683B1 ## CPC - G06F 16/282: {Hierarchical databases, e.g. IMS, LDAP data stores or Lotus Notes} - G06F 16/29: Geographical information databases - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract This disclosure relates to knowledge generation and implementation. A knowledge graph system comprises at least one processor, at least one database communicatively connected to the at least one processor, and a memory storing executable instructions. When executed, the instructions cause the at least one processor to aggregate, from the at least one database, entity data for a plurality of homes. Attribute information identifying geographic locations of the plurality of homes and relationships between pairs of the plurality of homes is extracted from the aggregated data. Knowledge graph data structures are populated with the extracted attribute information. A home knowledge graph is built, having nodes corresponding to the plurality of homes and edges corresponding to the identified relationships. A hierarchical cluster tree structure of the plurality of homes is outputted, wherein levels of the hierarchical cluster tree correspond to clusters of homes determined based in part on the knowledge graph edges. ## Claims ### Claim 1 A method for performing machine learning for a knowledge graph computer system, the method comprising: obtaining home location data from a knowledge graph; inputting the home location data into a machine learning model, the machine learning model configured to perform classification; training the machine learning model based on the home location data, the training comprising: learning one or more boundaries for a neighborhood; and determining a generalization based on the home location data; generating, based on the trained machine learning model, at least one of a neighborhood membership or an identified neighborhood; and updating the knowledge graph based on the machine learning model by changing one or more identified neighborhoods corresponding to one or more homes in the home location data. ### Claim 2 (depends on 1) The method of claim 1, further comprising classifying the neighborhood membership based on at least one of a longitude or a latitude. ### Claim 3 (depends on 1) The method of claim 1, further comprising processing, during the training of the machine learning model, one or more clusters by generalization. ### Claim 4 (depends on 3) The method of claim 3, wherein the processing comprises pruning a cluster in the one or more clusters. ### Claim 5 (depends on 4) The method of claim 4, further comprising generating a pruned hierarchical cluster tree structure. ### Claim 6 (depends on 1) The method of claim 1, wherein the machine learning model is configured to generate one or more boundaries for the one or more identified neighborhoods. ### Claim 7 (depends on 1) The method of claim 1, further comprising generating a classifier trained to classify leaf node membership based on an inputted longitude and latitude. ### Claim 8 A system comprising: a database; a processor in communication with the database; and a memory device, in communication with the processor, storing instructions that perform operations comprising: obtaining home location data from a knowledge graph; inputting the home location data into a machine learning model, the machine learning model configured to perform classification; training the machine learning model based on the home location data, the training comprising: learning one or more boundaries for a neighborhood; and determining a generalization based on the home location data; generating, based on the trained machine learning model, at least one of a neighborhood membership or an identified neighborhood; and updating the knowledge graph based on the machine learning model by changing one or more identified neighborhoods corresponding to one or more homes in the home location data. ### Claim 9 (depends on 8) The system of claim 8, further comprising classifying the neighborhood membership based on at least one of a longitude or a latitude. ### Claim 10 (depends on 8) The system of claim 8, further comprising processing, during the training of the machine learning model, one or more clusters by generalization. ### Claim 11 (depends on 10) The system of claim 10, wherein the processing comprises pruning a cluster in the one or more clusters. ### Claim 12 (depends on 11) The system of claim 11, further comprising generating a pruned hierarchical cluster tree structure. ### Claim 13 (depends on 8) The system of claim 8, wherein the machine learning model is configured to generate one or more boundaries for the one or more identified neighborhoods. ### Claim 14 (depends on 8) The system of claim 8, further comprising generating a classifier trained to classify leaf node membership based on an inputted longitude and latitude. ### Claim 15 A non-transitory computer-readable medium that stores software instructions that, when executed by one or more processors, are configured for and capable of performing and executing a comprising: obtaining home location data from a knowledge graph; inputting the home location data into a machine learning model, the machine learning model configured to perform classification; training the machine learning model based on the home location data, the training comprising: learning one or more boundaries for a neighborhood; and determining a generalization based on the home location data; generating, based on the trained machine learning model, at least one of a neighborhood membership or an identified neighborhood; and updating the knowledge graph based on the machine learning model by changing one or more identified neighborhoods corresponding to one or more homes in the home location data. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, further comprising classifying the neighborhood membership based on at least one of a longitude or a latitude. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, further comprising processing, during the training of the machine learning model, one or more clusters by generalization. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the processing comprises pruning a cluster in the one or more clusters. ### Claim 19 (depends on 18) The non-transitory computer-readable medium of claim 18, further comprising generating a pruned hierarchical cluster tree structure. ### Claim 20 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the machine learning model is configured to generate one or more boundaries for the identified neighborhood.