# US 2025/0147754 A1 · Multi-modal artificial intelligence root cause analysis > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0147754 A1 - Document ID: US20250147754A1 - Status: Published application - Kind: A1 - Application number: 18/500,211 - Filed: 2023-11-02 - Published: 2025-05-08 - Earliest claimed: 2023-11-02 - Assignees: Microsoft Technology Licensing, LLC - Applicants: Microsoft Technology Licensing, LLC - Inventors: Dmitry Valentinovich KHOLODKOV; Randee BIERLEIN - Web page: https://patentgrep.com/patent/US20250147754A1 ## CPC - G06F 8/70: Software maintenance or management - G06F 11/079: {Root cause analysis, i.e. error or fault diagnosis (in a hardware test environment G06F11/22; in a software test environment G06F11/36)} - G06F 40/20: Natural language analysis (semantic analysis of natural language G06F40/30) - G06N 3/042: Knowledge-based neural networks; Logical representations of neural networks - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/0475: Generative networks - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A data processing system implements obtaining build logs that include information associated with a software build problem; analyzing the logs to generate a knowledge graph identifying the relationship between various entities in the logs; extracting a signature of a candidate root cause of the build problem from the knowledge graph representing a subset of nodes and edges of the knowledge graph; providing the signature of the candidate root cause to a graphical language model to obtain a prediction of a category of root cause failure selected from among a plurality of root cause failures; constructing a prompt for a language model to generate a root cause failure analysis that describes the root cause of the build problem, the prompt including the category of root cause; receiving the root cause failure analysis from the language model; and performing one or more actions in response to receiving the root cause failure analysis. ## Claims ### Claim 1 A data processing system comprising: a processor; and a machine-readable storage medium storing executable instructions that, when executed, cause the processor alone or in combination with other processors to perform operations of: obtaining build logs that include information associated with a software build that experienced a build problem; generating a knowledge graph by analyzing the build logs using a knowledge graph generation unit, the knowledge graph identifying a relationship between various entities in the build logs; extracting a signature of a candidate root cause of the build problem from the knowledge graph, the signature representing a subset of nodes and edges of the knowledge graph; providing the signature of the candidate root cause to a graphical language model to obtain a prediction of a category of root cause failure, the graphical language model being trained to receive the signature of the candidate root cause and to predict a category of root cause failure from among a plurality of root cause failures based on the signature of the candidate root cause; constructing a prompt for a language model using a prompt construction unit, the prompt instructing the language model to generate a root cause failure analysis that describes the root cause of the build problem, the prompt including the category of root cause predicted by the graphical language model; providing the prompt as an input to the language model; receiving the root cause failure analysis from the language model; and performing one or more actions in response to receiving the root cause failure analysis. ### Claim 2 (depends on 1) The data processing system of claim 1, wherein extracting the signature of the candidate root cause of the build problem from the knowledge graph further comprises: filtering out nodes and edges of the knowledge graph that do not appear more than a threshold number of times in the build logs. ### Claim 3 (depends on 1) The data processing system of claim 1, wherein the language model is a Large Language Model (LLM). ### Claim 4 (depends on 1) The data processing system of claim 1, wherein constructing the prompt for the language model further comprises including instructions to the language model to include a description of the root cause failure and instructions for addressing the root cause failure. ### Claim 5 (depends on 1) The data processing system of claim 1, wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: providing the root cause analysis to a moderation service to analyze the root cause analysis for potentially objectionable content; and discarding the root cause analysis in response to the moderation service determining that the root cause analysis includes potentially objectionable content. ### Claim 6 (depends on 1) The data processing system of claim 1, wherein performing the one or more actions in response to receiving the root cause failure analysis further comprises: causing a user interface of an application of a client device to present the root cause failure analysis. ### Claim 7 (depends on 1) The data processing system of claim 1, wherein performing the one or more actions in response to receiving the root cause failure analysis further comprises: storing the root cause analysis and prompt in a build-related prompt datastore; and providing a user interface for searching the build-related prompt datastore. ### Claim 8 A method implemented in a data processing system for performing a root cause analysis, the method comprising: obtaining build logs that include information associated with a software build that experienced a build problem; generating a knowledge graph by analyzing the build logs using a knowledge graph generation unit, the knowledge graph identifying a relationship between various entities in the build logs; extracting a signature of a candidate root cause of the build problem from the knowledge graph, the signature representing a subset of nodes and edges of the knowledge graph; providing the signature of the candidate root cause to a graphical language model to obtain a prediction of a category of root cause failure, the graphical language model being trained to receive the signature of the candidate root cause and to predict a category of root cause failure from among a plurality of root cause failures based on the signature of the candidate root cause; constructing a prompt for a language model using a prompt construction unit, the prompt instructing the language model to generate a root cause failure analysis that describes the root cause of the build problem, the prompt including the category of root cause predicted by the graphical language model; providing the prompt as an input to the language model; receiving the root cause failure analysis from the language model; and performing one or more actions in response to receiving the root cause failure analysis. ### Claim 9 (depends on 8) The method of claim 8, wherein extracting the signature of the candidate root cause of the build problem from the knowledge graph further comprises: filtering out nodes and edges of the knowledge graph that do not appear more than a threshold number of times in the build logs. ### Claim 10 (depends on 8) The method of claim 8, wherein the language model is a Large Language Model (LLM). ### Claim 11 (depends on 8) The method of claim 8, wherein constructing the prompt for the language model further comprises including instructions to the language model to include a description of the root cause failure and instructions for addressing the root cause failure. ### Claim 12 (depends on 8) The method of claim 8, further comprising: providing the root cause analysis to a moderation service to analyze the root cause analysis for potentially objectionable content; and discarding the root cause analysis in response to the moderation service determining that the root cause analysis includes potentially objectionable content. ### Claim 13 (depends on 8) The method of claim 8, wherein performing the one or more actions in response to receiving the root cause failure analysis further comprises: causing a user interface of an application of a client device to present the root cause failure analysis. ### Claim 14 (depends on 8) The method of claim 8, wherein performing the one or more actions in response to receiving the root cause failure analysis further comprises: storing the root cause analysis and the prompt in a build-related prompt datastore; and providing a user interface for searching the build-related prompt datastore. ### Claim 15 A data processing system comprising: a processor; and a machine-readable storage medium storing executable instructions that, when executed, cause the processor alone or in combination with other processors to perform operations of: obtaining logs that include information associated with a problem with software; generating a knowledge graph by analyzing the logs using a knowledge graph generation unit, the knowledge graph identifying a relationship between various entities in the logs; extracting a signature of a candidate root cause of the problem from the knowledge graph, the signature representing a subset of nodes and edges of the knowledge graph; providing the signature of the candidate root cause to a graphical language model to obtain a prediction of a category of root cause failure, the graphical language model being trained to receive the signature of the candidate root cause and to predict a category of root cause failure from among a plurality of root cause failures based on the signature of the candidate root cause; constructing a prompt for a language model using a prompt construction unit, the prompt instructing the language model to generate a root cause failure analysis that describes the root cause of the problem with the software, the prompt including the category of root cause predicted by the graphical language model; providing the prompt as an input to the language model; receiving the root cause failure analysis from the language model; and performing one or more actions in response to receiving the root cause failure analysis. ### Claim 16 (depends on 15) The data processing system of claim 15, wherein extracting the signature of the candidate root cause of the problem from the knowledge graph further comprises: filtering out nodes and edges of the knowledge graph that do not appear more than a threshold number of times in the logs. ### Claim 17 (depends on 15) The data processing system of claim 15, wherein the language model is a Large Language Model (LLM). ### Claim 18 (depends on 15) The data processing system of claim 15, wherein constructing the prompt for the language model further comprises including instructions to the language model to include a description of the root cause failure and instructions for addressing the root cause failure. ### Claim 19 (depends on 15) The data processing system of claim 15, wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: providing the root cause analysis to a moderation service to analyze the root cause analysis for potentially objectionable content; and discarding the root cause analysis in response to the moderation service determining that the root cause analysis includes potentially objectionable content. ### Claim 20 (depends on 15) The data processing system of claim 15, wherein the logs include runtime error information associated with the software.