Generative AI Context Augmentation via Topic Segmentation
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Solution Overview
Problem
Existing generative AI systems lack the ability to effectively utilize historical user interactions to enhance the context and relevance of user inputs, leading to suboptimal responses.
Innovation Solution
A method that uses a machine-learning model to identify topics and contexts from user interactions with a generative AI system, augmenting user inputs with information from corresponding folders in a file system, thereby generating more contextually relevant responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical user interaction data is stored and organized in folders for context augmentation, then the relevance and quality of AI responses is improved, but the device complexity and data management overhead increases
Solution Approach 1:
The patent segments historical interaction data into topic-based folders, organizing information by subject matter rather than storing all data in a single repository. This segmentation enables efficient retrieval of contextually relevant information while maintaining manageable data structures that reduce complexity in data management operations.
Solution Approach 2:
The system performs preliminary organization of historical data into topic-specific folders before actual interactions occur. By pre-structuring the data repository with appropriate categorization, the system eliminates the need for complex real-time data organization during interactions, thereby reducing operational complexity while maintaining high response quality.
2Reliability
If a machine learning model is used to identify topics and contexts from user interactions, then the contextual relevance of responses is improved, but the processing time and computational resources increase
Solution Approach 1:
The machine learning model performs topic identification and context extraction as preliminary actions during data ingestion and storage phases. By pre-processing and categorizing interactions into topic folders before they are needed for response generation, the system minimizes real-time processing requirements while maintaining high contextual relevance in responses.
Solution Approach 2:
The system employs self-service mechanisms where the machine learning model automatically identifies topics and organizes data without requiring manual intervention or complex real-time analysis during user interactions. This automation reduces processing time by eliminating manual context analysis while maintaining accurate topic identification through trained models.
3Adaptability or versatility
If user interaction data is stored in organized folders by topic, then the adaptability and personalization of AI responses is improved, but the data storage requirements and system complexity increase
Solution Approach 1:
The patent implements segmentation by organizing user interaction data into topic-specific folders, creating a structured repository that enables personalized responses without requiring storage of all raw interaction data. This segmentation allows the system to retrieve only relevant contextual information for each interaction, reducing overall storage requirements while maintaining adaptability.
Solution Approach 2:
The system extracts only the essential contextual information from user interactions and stores it in organized folders, rather than preserving complete interaction transcripts. This extraction process retains the key elements needed for personalization and adaptability while significantly reducing the volume of data that must be stored and managed.
Data Source
AI summary
Technology embodied in a method that includes receiving, as an input to a machine-learning model, data indicative of user-interaction of a particular user with a generative artificial intelligence (AI) system. The machine learning model is trained to identify one or more topics associated with inputs provided to the machine-learning model. The method also includes identifying a first context associated with the data indicative of the user-interaction with the generative AI system, and parsing a file system to determine that one or more folders within the file system correspond to the first context. The file system includes multiple folders each corresponding to a separate topic as identified from historical interactions of the particular user with the generative AI system. The data indicative of the interaction is augmented and provided to the generative AI system for generation of a response to the interaction.