Intermediary Cloud Service for User-Specific LLM Context
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Solution Overview
Problem
Conventional large language models (LLMs) face challenges in providing user-specific context due to privacy, security, and resource constraints, limiting their effectiveness in user-specific applications, especially on embedded devices with limited resources.
Innovation Solution
The system employs an intermediary cloud service that captures and aggregates user context from edge devices, converting it into user-specific embedding vectors to initialize and personalize LLM sessions, while implementing layered data handling and access control to manage resource allocation and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user-specific data is provided to cloud-based LLMs, then user-specific context and personalization are improved, but privacy and security concerns worsen
Solution Approach 1:
The patent introduces an intermediary cloud service layer between edge devices and LLMs. This intermediary service aggregates user context from multiple edge devices, converts it into embedding vectors, and manages secure access control. The intermediary acts as a mediator that enables user-specific personalization while maintaining privacy through centralized management and selective data sharing, rather than directly exposing raw user data to the LLM.
Solution Approach 2:
The patent extracts essential user context information from raw edge device data and converts it into compressed embedding vectors. This extraction process separates the valuable user-specific patterns from the raw data, allowing the LLM to access personalized context through the embedding vectors without direct access to the original user data, thus reducing privacy risks while maintaining personalization capability.
2Measurement precision
If LLMs are re-trained continuously for user-specific data, then user-specific accuracy is improved, but computational resources and time consumption worsen
Solution Approach 1:
The patent performs preliminary action by pre-converting user context into embedding vectors before the LLM needs to process queries. The intermediary service proactively aggregates and converts user context from edge devices, storing the embedding vectors for later retrieval. This eliminates the need for real-time re-training of LLMs for each user-specific query, significantly reducing computational resources while maintaining high user-specific accuracy.
Solution Approach 2:
Instead of re-training the LLM for each user, the patent creates a copy of user-specific information in the form of embedding vectors. These embedding vectors serve as compressed representations of user context that can be quickly retrieved and injected into LLM sessions without requiring actual re-training, thus achieving user-specific accuracy with minimal computational overhead.
3Quantity of substance
If user context is aggregated from multiple edge devices, then user-specific context completeness is improved, but data management complexity worsens
Solution Approach 1:
The patent merges data management functionality into a centralized intermediary service that handles aggregation from multiple edge devices. Instead of each edge device managing its own data independently, the intermediary service consolidates user context from all devices, converts it to embedding vectors, and manages the storage and retrieval. This merging approach simplifies individual device complexity while enabling comprehensive user context aggregation.
Solution Approach 2:
The patent changes the parameter representation from raw multi-device data to compressed embedding vectors. This parameter transformation simplifies data management by reducing the complexity of storing, transmitting, and processing user context. The embedding vectors serve as a standardized representation that can be efficiently managed by the intermediary service, eliminating the complexity of handling raw data from multiple edge devices directly.
4Productivity
If embedding vectors are converted from user context, then data processing efficiency is improved, but information loss may occur
Solution Approach 1:
The patent applies parameter changes by transforming user context into embedding vectors with optimized dimensionality. This transformation compresses the data while preserving the essential patterns and relationships needed for user-specific personalization. The intermediary service carefully controls the embedding vector dimensions to maintain sufficient information content while achieving efficient processing, balancing compression benefits with information preservation.
Data Source
AI summary
Network infrastructure for user-specific generative intelligence. Providing user-specific context to a generically trained LLM introduces a variety of complications (privacy, resource utilization, training costs, etc.). Various aspects of the present disclosure provide novel user-specific data structures, privacy and access control, layers of data, and session management, within a network infrastructure for generative intelligence. For example, user-specific embedding vectors may be used to provide user context to a generically trained foundation model. In some variants, edge devices capture multiple modalities of user context (images, audio; not just text). Privacy and access control mechanisms also allow a user to control information that is captured and sent to the foundation model. Session management further decouples a user's conversational state from the foundation model's session state. These concepts and others may be used to emulate e.g., a chatbot based virtual assistant that responds based on user context.