# US 2026/0099342 A1 · Generative artificial intelligence-based system to assist in the completion of a virtual process > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0099342 A1 - Document ID: US20260099342A1 - Status: Published application - Kind: A1 - Application number: 18/909,231 - Filed: 2024-10-08 - Published: 2026-04-09 - Earliest claimed: 2024-10-08 - Assignees: Intuit Inc. - Applicants: Intuit Inc. - Inventors: Isham RAJGARIAH; Parmanand MOURYA; Ram YERUBANDI; Pradeep Kurunimakki LAXMINARAYANA; Sandeep MEWARA; Sanjay GUPTA; Sanjay KUMAR - Web page: https://patentgrep.com/patent/US20260099342A1 ## CPC - G06F 9/448: Execution paradigms, e.g. implementations of programming paradigms - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Aspects of the present disclosure relate to automatic virtual process completion assistance. Embodiments include receiving data associated with a given virtual process. Embodiments further include retrieving, based on a semantic similarity comparison involving the received data and embedding representations of a set of rules, one or more rules. Embodiments further include retrieving one or more knowledge graphs of a set of knowledge graphs based on the received data, wherein each respective knowledge graph of the set of knowledge graphs represents a respective historical virtual process instance. Embodiments further include providing an input based on the received data, the retrieved rules, and the retrieved knowledge graphs to a machine learning model that is configured to evaluate virtual processes. Embodiments further include generating, using the machine learning model, an evaluation of the given virtual process based on the received data, the retrieved rules, and the retrieved knowledge graphs. ## Claims ### Claim 1 A method of automatic virtual process completion assistance, comprising: receiving data associated with a given virtual process; retrieving, based on a semantic similarity comparison involving the received data and embedding representations of a set of rules, one or more rules of the set of rules; retrieving one or more knowledge graphs of a set of knowledge graphs based on the received data, wherein each respective knowledge graph of the set of knowledge graphs represents a respective historical virtual process instance; providing an input based on the received data, the retrieved rules, and the retrieved knowledge graphs to a machine learning model that is configured to evaluate virtual processes; and generating, using the machine learning model, an evaluation of the given virtual process based on the received data, the retrieved rules, and the retrieved knowledge graphs. ### Claim 2 (depends on 1) The method of claim 1, wherein the one or more knowledge graphs are retrieved based on using a graph query language to identify the one or more knowledge graphs based on tokens within the received data. ### Claim 3 (depends on 1) The method of claim 1, wherein the one or more knowledge graphs are retrieved based on a semantic similarity comparison involving the received data and embedding representations of a respective knowledge graph. ### Claim 4 (depends on 1) The method of claim 1, wherein a particular knowledge graph of the one or more knowledge graphs is converted into a natural language description of a particular historical virtual process instance represented by the particular knowledge graph. ### Claim 5 (depends on 4) The method of claim 4, wherein the evaluation is generated based on providing the natural language description as input to the machine learning model. ### Claim 6 (depends on 1) The method of claim 1, further comprising creating a knowledge graph representation of the given virtual process, wherein the one or more knowledge graphs of the set of knowledge graphs are retrieved based on the knowledge graph representation of the given virtual process. ### Claim 7 (depends on 6) The method of claim 6, further comprising adding the knowledge graph representation of the given virtual process to the set of knowledge graphs. ### Claim 8 (depends on 1) The method of claim 1, wherein the virtual process comprises an electronic form, wherein the generated evaluation comprises a recommended order for completing fields of the form. ### Claim 9 (depends on 8) The method of claim 8, wherein the generated evaluation further comprises one or more of a predicted amount of progress in completing the form or a predicted time required to complete the form. ### Claim 10 (depends on 1) The method of claim 1, wherein the embedding representations of the set of rules are created based on providing multiple rules as input to an embedding model, wherein the semantic similarity comparison further involves embedding representations of one or more tokens within the received data. ### Claim 11 A system for automatic virtual process completion assistance, comprising: one or more processors; and a memory comprising instructions that, when executed by the one or more processors, cause the system to: receive data associated with a given virtual process; retrieve, based on a semantic similarity comparison involving the received data and embedding representations of a set of rules, one or more rules of the set of rules; retrieve one or more knowledge graphs of a set of knowledge graphs based on the received data, wherein each respective knowledge graph of the set of knowledge graphs represents a respective historical virtual process instance; provide an input based on the received data, the retrieved rules, and the retrieved knowledge graphs to a machine learning model that is configured to evaluate virtual processes; and generate, using the machine learning model, an evaluation of the given virtual process based on the received data, the retrieved rules, and the retrieved knowledge graphs. ### Claim 12 (depends on 11) The system of claim 11, wherein the one or more knowledge graphs are retrieved based on using a graph query language to identify the one or more knowledge graphs based on tokens within the received data. ### Claim 13 (depends on 11) The system of claim 11, wherein the one or more knowledge graphs are retrieved based on a semantic similarity comparison involving the received data and embedding representations of a respective knowledge graph. ### Claim 14 (depends on 11) The system of claim 11, wherein a particular knowledge graph of the one or more knowledge graphs is converted into a natural language description of a particular historical virtual process instance represented by the particular knowledge graph. ### Claim 15 (depends on 14) The system of claim 14, wherein the evaluation is generated based on providing the natural language description as input to the machine learning model. ### Claim 16 (depends on 11) The system of claim 11, wherein the instructions further cause the system to create a knowledge graph representation of the given virtual process, wherein the one or more knowledge graphs of the set of knowledge graphs are retrieved based on the knowledge graph representation of the given virtual process. ### Claim 17 (depends on 16) The system of claim 16, wherein the instructions further cause the system to add the knowledge graph representation of the given virtual process to the set of knowledge graphs. ### Claim 18 (depends on 11) The system of claim 11, wherein the virtual process comprises an electronic form, wherein the generated evaluation comprises a recommended order for completing fields of the form. ### Claim 19 (depends on 18) The system of claim 18, wherein the generated evaluation further comprises one or more of a predicted amount of progress in completing the form or a predicted time required to complete the form. ### Claim 20 (depends on 11) The system of claim 11, wherein the embedding representations of the set of rules are created based on providing multiple rules as input to an embedding model, wherein the semantic similarity comparison further involves embedding representations of one or more tokens within the received data.