Adaptive User Representation for Context-Based AI Personalization
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Solution Overview
Problem
Current AI assistant systems struggle to determine which user information is most relevant for generating personalized responses, leading to inefficient use of resources and ineffective personalization.
Innovation Solution
An adaptive user representation (AUR) system that processes user queries to determine context and retrieve relevant user information from a repository, generating instructions for the AI assistant to provide personalized responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If extensive user information is directly integrated into AI assistants for every query, then personalization capability is improved, but computational cost and resource consumption increase significantly
Solution Approach 1:
The patent extracts only the relevant user information needed for each specific query from the extensive user profile, rather than integrating all user information. The query context determining model identifies what information is necessary, and the information retrieval model extracts only those specific details, thereby reducing computational cost while maintaining personalization capability.
Solution Approach 2:
The patent segments the user information processing into distinct components: query context determination, relevant information identification, and information retrieval. This segmentation allows the system to process only necessary portions of user information for each query type, reducing overall computational burden while preserving personalization.
2Adaptability or versatility
If all available user information is included with each prompt, then response personalization is improved, but AI assistant processing time increases
Solution Approach 1:
The patent extracts only the specific user information relevant to each query from the complete user profile. The information retrieval model identifies and extracts only necessary details based on query context, reducing the amount of data the AI assistant must process while maintaining response personalization.
Solution Approach 2:
The patent performs preliminary processing by determining query context and identifying relevant user information before the main AI assistant processing. This preliminary action filters and prepares only the necessary information in advance, reducing the processing time required by the AI assistant while preserving personalization capability.
3Measurement precision
If extensive user information is processed for every query, then personalization accuracy is improved, but system resource efficiency deteriorates
Solution Approach 1:
The patent extracts only the relevant user information needed for accurate personalization in each specific context. Rather than processing all user information, the system identifies and extracts only those details that contribute to personalization accuracy, thereby maintaining precision while improving resource efficiency.
Solution Approach 2:
The patent dynamically changes the parameters of information processing based on query context. The system adjusts which user information parameters are retrieved and processed depending on the specific query requirements, optimizing the balance between personalization accuracy and resource efficiency for each interaction.
4Loss of information
If the AI assistant accesses comprehensive user information, then response relevance is improved, but the complexity of information management increases
Solution Approach 1:
The patent introduces intermediary models (query context determining model and information retrieval model) that mediate between the user's query and the comprehensive user information repository. These intermediaries filter and select only the relevant information needed for response relevance, reducing information management complexity while maintaining comprehensive access capability.
Solution Approach 2:
The patent segments the information management process into distinct functional components: query analysis, context determination, relevant information identification, and retrieval. This segmentation simplifies the overall information management complexity by breaking down the complex task into manageable, specialized sub-tasks.
Data Source
AI summary
An adaptive user representation (AUR) system for use with generative artificial intelligence (AI) receives queries meant for the generative AI and utilizes one or more AI models to process each query to determine query context and to identify user information from a user information repository which is relevant to the query. The system generates instructions based on the query, query context, and the relevant user information for causing the generative AI to generate a response to the query which is personalized to the user. The AUR system transforms the raw data of the query and relevant user information into a set of instructions for the generative AI which describe how to personalize the response or required searches to ensure the final response is personalized.