# US 12,111,857 B2 · Computerized assistance using artificial intelligence knowledge base > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,111,857 B2 - Document ID: US12111857B2 - Status: Granted - Kind: B2 - Application number: 18/154,551 - Filed: 2023-01-13 - Published: 2023-05-25 - Granted: 2024-10-08 - Earliest claimed: 2018-04-12 - Assignees: Microsoft Technology Licensing, LLC - Applicants: Microsoft Technology Licensing, LLC - Inventors: Vipindeep Vangala; Sundararajan Srinivasan; Rajesh Gunda - Web page: https://patentgrep.com/patent/US12111857B2 ## CPC - G06F 16/3329: Natural language query formulation - G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)} - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06N 3/04: Architecture, e.g. interconnection topology - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A computerized personal assistant includes a natural language user interface, a natural language processing machine, an identity machine, and a knowledge-base updating machine. The knowledge-base updating machine is configured to update a user-centric artificial intelligence knowledge base associated with the particular user to include a new or updated user-centric fact based on the computer-readable representation of the user input, wherein the knowledge-base updating machine updates the user-centric artificial intelligence knowledge base via an update protocol useable by a plurality of different computer services. ## Claims ### Claim 1 A computer system comprising: one or more computer processors; and computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations comprising: receiving a query; querying a user-centric artificial intelligence knowledge base comprising a graph data structure that stores a plurality of user-centric facts, the user-centric artificial intelligence knowledge base further comprising an application-agnostic data format that is associated with a node record format that supports a facet pointer to auxiliary application-specific data associated with application-specific facts of application-specific contexts where users interact with a plurality of different computer services; selecting a subset of user-centric facts that satisfy the set of constraints defined by the query, wherein the user-centric facts comprise one or more application-specific facts, the one or more application-specific facts are associated with corresponding auxiliary application-specific data having facet pointers that support retrieving and storing the auxiliary application-specific data; and caching the subset of user-centric facts responsive to the query as a cached response. ### Claim 2 (depends on 1) The system of claim 1, wherein the query is a slice query indicating a start node and a distance parameter, and wherein the subset of user-centric facts includes user-centric facts reached by starting at the start node and traversing a number of edges equal to at most the distance parameter away from the start node. ### Claim 3 (depends on 1) The system of claim 1, wherein the query is a rank query to rank the plurality of user-centric facts based at least on a confidence value associated with each user-centric fact, and the subset of user-centric facts is ranked in order according to the confidence value of each user-centric fact. ### Claim 4 (depends on 1) The system of claim 1, the operations further comprising caching the query as a cached query. ### Claim 5 (depends on 4) The system of claim 4, the operations further comprising: receiving a second query; determining that the second query corresponds to the cached query; and based on the cached query, outputting the cached response. ### Claim 6 (depends on 1) The system of claim 1, wherein a user-centric fact stored in the graph data structure is an application-specific fact, and the graph data structure stores for the application-specific fact an enrichment that includes an application-agnostic fact associated with the application-specific fact. ### Claim 7 (depends on 1) The system of claim 1, wherein the facet pointer supports multiple file types for storing different kinds of auxiliary application-specific data associated with the auxiliary application-specific facts of the application-specific contexts. ### Claim 8 One or more hardware computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations comprising: receiving a query; querying a user-centric artificial intelligence knowledge base comprising a graph data structure that stores a plurality of user-centric facts, the user-centric artificial intelligence knowledge base further comprising an application-agnostic data format that is associated with a node record format that supports a facet pointer to auxiliary application-specific data associated with application-specific facts of application-specific contexts where users interact with a plurality of different computer services; selecting a subset of user-centric facts that satisfy the set of constraints defined by the query, wherein the user-centric facts comprise one or more application-specific facts, the one or more application-specific facts are associated with corresponding auxiliary application-specific data having facet pointers that support retrieving and storing the auxiliary application-specific data; and caching the subset of user-centric facts responsive to the query as a cached response. ### Claim 9 (depends on 8) The media of claim 8, wherein the query is a slice query indicating a start node and a distance parameter, and wherein the subset of user-centric facts includes user-centric facts reached by starting at the start node and traversing a number of edges equal to at most the distance parameter away from the start node. ### Claim 10 (depends on 8) The media of claim 8, wherein the query is a rank query to rank the plurality of user-centric facts based at least on a confidence value associated with each user-centric fact, and the subset of user-centric facts is ranked in order according to the confidence value of each user-centric fact. ### Claim 11 (depends on 8) The media of claim 8, the operations further comprising caching the query as a cached query. ### Claim 12 (depends on 11) The media of claim 11, the operations further comprising: receiving a second query; determining that the second query corresponds to the cached query; and based on the cached query, outputting the cached response. ### Claim 13 (depends on 8) The media of claim 8, wherein a user-centric fact stored in the graph data structure is an application-specific fact, and the graph data structure stores for the application-specific fact an enrichment that includes an application-agnostic fact associated with the application-specific fact. ### Claim 14 (depends on 8) The media of claim 8, wherein the facet pointer supports multiple file types for storing different kinds of auxiliary application-specific data associated with the auxiliary application-specific facts of the application-specific contexts. ### Claim 15 A method comprising: receiving a query; querying a user-centric artificial intelligence knowledge base comprising a graph data structure that stores a plurality of user-centric facts, the user-centric artificial intelligence knowledge base further comprising an application-agnostic data format that is associated with a node record format that supports a facet pointer to auxiliary application-specific data associated with application-specific facts of application-specific contexts where users interact with a plurality of different computer services; selecting a subset of user-centric facts that satisfy the set of constraints defined by the query, wherein the user-centric facts comprise one or more application-specific facts, the one or more application-specific facts are associated with corresponding auxiliary application-specific data having facet pointers that support retrieving and storing the auxiliary application-specific data; and caching the subset of user-centric facts responsive to the query as a cached response. ### Claim 16 (depends on 15) The method of claim 15, wherein the query is a slice query indicating a start node and a distance parameter, and wherein the subset of user-centric facts includes user-centric facts reached by starting at the start node and traversing a number of edges equal to at most the distance parameter away from the start node. ### Claim 17 (depends on 15) The method of claim 15, wherein the query is a rank query to rank the plurality of user-centric facts based at least on a confidence value associated with each user-centric fact, and the subset of user-centric facts is ranked in order according to the confidence value of each user-centric fact. ### Claim 18 (depends on 15) The method of claim 15, the method further comprising caching the query as a cached query. ### Claim 19 (depends on 15) The method of claim 15, the method further comprising: receiving a second query; determining that the second query corresponds to the cached query; and based on the cached query, outputting the cached response. ### Claim 20 (depends on 15) The method of claim 15, wherein a user-centric fact stored in the graph data structure is an application-specific fact, and the graph data structure stores for the application-specific fact an enrichment that includes an application-agnostic fact associated with the application-specific fact; and wherein the facet pointer supports multiple file types for storing different kinds of auxiliary application-specific data associated with the auxiliary application-specific facts of the application-specific contexts.