Digital Memory Tagging for Personalized AI Assistant Context
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Solution Overview
Problem
AI-based digital assistant tools lack efficient mechanisms to consider user context, leading to irrelevant or non-personalized responses due to limitations in short-term memory and general knowledge, and existing methods like training Large Language Models (LLMs) on enterprise data are costly, complex, and challenging to implement.
Innovation Solution
A digital memory system that collects, extracts, and tags user data from various sources, storing it in a user-specific repository, and integrates user context information into prompts for the digital assistant AI, enhancing response relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI-based digital assistant tools use short-term memory and general knowledge, then they can perform general tasks effectively, but they cannot provide contextualized or personalized responses
Solution Approach 1:
The system performs preliminary actions by collecting user data from multiple sources and generating user data elements before they are needed for response generation. The information extraction component creates structured representations of user information in advance, which are then stored and retrieved when needed, eliminating the need for real-time context reconstruction.
Solution Approach 2:
The patent introduces user data elements as intermediary structures between raw user data and AI model processing. These elements serve as a mediator that transforms unstructured user information into a format suitable for contextualizing AI responses, enabling personalization without directly modifying the AI model itself.
2Measurement precision
If enterprise data is used to train Large Language Models for better contextual understanding, then response relevance improves, but cost and implementation complexity increase significantly
Solution Approach 1:
The system extracts only the necessary contextual information from enterprise data through the information extraction component, rather than training the entire model on all enterprise data. This extraction approach isolates relevant user context from the broader data corpus, achieving response relevance without the complexity of full model training on enterprise data.
Solution Approach 2:
Instead of training the AI model directly on enterprise data, the system creates copies or representations of user context in the form of user data elements. These copied contextual representations are then integrated with AI prompts, providing relevant information without requiring the model to be retrained on the original enterprise data.
3Adaptability or versatility
If comprehensive user data is collected and stored for better context understanding, then response personalization improves, but data management complexity and storage requirements increase
Solution Approach 1:
The system segments comprehensive user data into discrete user data elements, each representing a specific piece of extracted information. This segmentation divides the complex task of managing all user data into manageable units that can be independently processed, stored, and retrieved, reducing overall data management complexity while maintaining comprehensive context understanding.
Solution Approach 2:
The patent applies local quality by creating user data elements with specific properties and characteristics tailored to their particular information type. Each element is structured with appropriate attributes for its specific purpose, allowing optimized storage and retrieval strategies for different types of user information rather than applying a uniform management approach to all data.
Data Source
AI summary
A digital assistant system includes a digital memory configured to collecting data pertaining to a user from a plurality of user information sources, to extract relevant pieces of information from the collected data, and to generate new user data elements using an information extraction component, the new user data elements including the extracted relevant pieces of information. The new user data elements are tagged with content classification tags which indicate a type of content of the extracted pieces of information in the new user data elements and retention period tags which indicate retention periods for the new user data elements. Information from the user data elements is retrieved in response to receiving a prompt from a digital assistant artificial intelligence (AI) component. The retrieved information is provided to the digital assistant AI as user context information.