# US 2024/0202584 A1 · Machine learning instancing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2024/0202584 A1 - Document ID: US20240202584A1 - Status: Published application - Kind: A1 - Application number: 18/129,726 - Filed: 2023-03-31 - Published: 2024-06-20 - Earliest claimed: 2022-12-19 - Assignees: Microsoft Technology Licensing, LLC - Applicants: Microsoft Technology Licensing, LLC - Inventors: Samuel Edward SCHILLACE; Umesh MADAN; Brian KRABACH - Web page: https://patentgrep.com/patent/US20240202584A1 ## CPC - G06N 20/00: Machine learning - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Aspects of the present disclosure relate to machine learning instancing, where an instance of an agent (e.g., including processing of user input by a machine learning model to generate model output) is encapsulated as an agent object. In examples, an agent object is stored as a file, as a document, and/or in a database, among other examples. An agent object includes a persona definition and/or an object embedding memory, thereby defining various aspects of the agent. Thus, an agent object permits portability the agent, for example between users, across contexts, and/or for a variety of subsequent processing, among other examples. ## Claims ### Claim 1 A system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, the set of operations comprising: obtaining user input to an agent, wherein a persona of the agent is defined by an agent object; extracting an intent for the user input; determining, based on the extracted intent, context from an embedding object memory of the agent object; obtaining, based on the determined context, model output corresponding to the user input; and providing the model output in response to the obtained user input. ### Claim 2 (depends on 1) The system of claim 1, wherein the intent for the user input is extracted based on an intent rewriting definition of the agent object. ### Claim 3 (depends on 2) The system of claim 2, wherein extracting the intent comprises generating a machine learning model processing request comprising the intent rewriting definition of the agent object. ### Claim 4 (depends on 1) The system of claim 1, wherein the set of operations further comprises updating the embedding object memory based on at least one of the user input or the model output. ### Claim 5 (depends on 4) The system of claim 4, wherein the embedding object memory is updated based on a memory extraction definition of the agent object. ### Claim 6 (depends on 1) The system of claim 1, wherein the persona of the agent is defined at least in part by a description definition of the agent object. ### Claim 7 (depends on 6) The system of claim 6, wherein: obtaining the model output comprises: generating an agent prompt based on a response generation definition of the agent object; and requesting the model output based on the agent prompt; and the agent prompt includes: the description definition; the user input; the determined context; and conversation history of the agent. ### Claim 8 (depends on 1) The system of claim 1, wherein the agent object is a document. ### Claim 9 (depends on 8) The system of claim 8, wherein: the document is stored by a collaboration platform; the user input is from a first user of the collaboration platform; and user input to the agent is further received from a second user of the collaboration platform. ### Claim 10 A method for generating an artifact based on an agent object, comprising: obtaining an agent object that defines a persona for an agent; receiving input to the agent; updating the agent object based on at least one of the received input or output of the agent; providing the agent object for processing by programmatic code, thereby generating an artifact based on the agent object; and providing an indication of the generated artifact. ### Claim 11 (depends on 10) The method of claim 10, wherein the programmatic code includes at least one of: an instruction to obtain output from the agent object based on an input; or an instruction to obtain information from an embedding object memory of the agent object. ### Claim 12 (depends on 10) The method of claim 10, wherein updating the agent object comprises: extracting an intent for the input; determining, based on the extracted intent, context from an embedding object memory of the agent object; obtaining, based on the determined context, model output corresponding to the user input; and updating, based on a memory extraction definition of the agent object, the embedding object memory based on at least one of the user input or the model output. ### Claim 13 (depends on 10) The method of claim 10, wherein user input is requested as part of generating the artifact based on the agent object. ### Claim 14 (depends on 10) The method of claim 10, wherein the artifact is at least one of a word processing document, a spreadsheet, a website, or a presentation document. ### Claim 15 A method, comprising: obtaining user input to an agent, wherein a persona of the agent is defined by an agent object; extracting an intent for the user input; determining, based on the extracted intent, context from an embedding object memory of the agent object; obtaining, based on the determined context, model output corresponding to the user input; and providing the model output in response to the obtained user input. ### Claim 16 (depends on 15) The method of claim 15, further comprising updating the embedding object memory based on at least one of the user input or the model output. ### Claim 17 (depends on 16) The method of claim 16, wherein the embedding object memory is updated based on a memory extraction definition of the agent object. ### Claim 18 (depends on 15) The method of claim 15, wherein the persona of the agent is defined at least in part by a description definition of the agent object. ### Claim 19 (depends on 18) The method of claim 18, wherein: obtaining the model output comprises: generating an agent prompt based on a response generation definition of the agent object; and requesting the model output based on the agent prompt; and the agent prompt includes: the description definition; the user input; the determined context; and conversation history of the agent. ### Claim 20 (depends on 15) The method of claim 15, wherein the agent object is a document.