Systems and methods for artificial intelligence-based analysis of over-the-air client logs

Abstract

A method and system for analyzing over-the-air (OTA) client logs from vehicles includes receiving OTA client log files comprising error logs generated during OTA software updates, preprocessing the log files to generate cleaned log files by removing duplicated log lines and standardizing formats, segmenting the cleaned log files into log chunks, and analyzing the chunks using both rule-based and artificial intelligence (AI) approaches. The AI analysis includes generating vector embeddings for error-related log lines, comparing embeddings with an insignificant error database to filter out insignificant errors, retrieving error context information from a vector database, and generating error summaries using a large language model. The system displays analysis results via a failure analysis engineer (FAE) review interface and updates databases based on FAE feedback. This hybrid approach enables efficient processing of OTA client logs while maintaining accuracy through rule-based filtering and contextual AI analysis.