Generative AI Code Lookup Engine for Automotive Diagnostics
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Solution Overview
Problem
Conventional item listing systems lack comprehensive logic and infrastructure for effective generative AI code lookup management in the automotive domain, leading to less accurate diagnoses, especially with ambiguous codes, and struggle to adapt to evolving standards and new codes, resulting in outdated information dissemination.
Innovation Solution
Implementing a generative AI code lookup engine integrated into the item listing system, utilizing image generation models and Large Language Models to perform training, generating, deploying, and controlling operations, which provides nuanced interpretations of Diagnostic Trouble Codes (DTCs), estimates repair efforts and costs, and recommends relevant products based on historical data and user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional code lookup systems are used, then the system structure is simple, but the diagnostic accuracy deteriorates especially with ambiguous codes
Solution Approach 1:
The system segments the code lookup process into multiple specialized components: a generative AI model for interpreting ambiguous codes, a training module for continuous learning, and a lookup engine for rapid retrieval. This segmentation allows each component to specialize in specific tasks, improving overall diagnostic accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces a generative AI model as an intermediary between the user's code input and the final diagnostic result. This intermediary layer processes ambiguous codes, provides contextual interpretations, and bridges the gap between simple code input and complex diagnostic outcomes, thereby improving accuracy without directly increasing system complexity.
2Adaptability or versatility
If traditional code lookup methods are used, then the implementation is straightforward, but the system cannot adapt to evolving standards and new codes
Solution Approach 1:
The system performs preliminary actions by pre-training the generative AI model on extensive automotive code databases and evolving standards before deployment. This preliminary training enables the system to adapt to new codes and standards without requiring complex real-time updates, maintaining implementation ease while improving adaptability.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously learns from new diagnostic data, user interactions, and evolving automotive standards. This feedback loop allows the generative AI model to adapt to changing requirements automatically, enhancing versatility without significantly complicating the implementation through automated learning processes.
3Loss of information
If comprehensive code lookup data is provided, then the information completeness is high, but the data becomes outdated quickly
Solution Approach 1:
The system changes the parameter of information freshness by implementing continuous training and updating mechanisms. Instead of providing a static large dataset that becomes outdated, the generative AI model processes smaller batches of new data regularly, maintaining information currency without requiring proportionally large data volumes.
Solution Approach 2:
The patent ensures continuity of useful action through ongoing model training and data updates. The system continuously incorporates new codes and standards into the generative AI model, maintaining information currency over time without requiring the accumulation of ever-increasing data volumes.
Data Source
AI summary
Methods, systems, and computer storage media for providing generative artificial intelligence (AI) code lookup management using a generative AI code lookup engine in an item listing system. A generative AI code lookup engine supports generative AI code lookup management based on an automotive code lookup platform including code lookup training operations, code lookup engine operations, and code lookup interfaces associated with a generative AI model and an automotive domain for improved personalization and presentation of code lookup data and interfaces. In operation, a request associated with a diagnosis code of a vehicle is accessed. Based on the request, code lookup data associated with a generative AI model is accessed. The generative AI model is associated with code lookup training operations and a code lookup data structure that support code lookup guidance in the item listing system. The code lookup data is communicated for display via an item listing system client.