Local LLM Agent for Browser Content Personalization
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Solution Overview
Problem
Large language models (LLMs) often provide suboptimal outputs and suffer from slow response times due to their reliance on remote data sources, failing to utilize user-specific data and leading to inefficient resource usage and user dissatisfaction.
Innovation Solution
Implementing a local LLM on user devices, fine-tuning or transfer learning using user-specific data to enhance output relevance and speed, while reducing network dependency and improving privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If LLMs are hosted on remote systems, then centralized processing and model updates are simplified, but response time to user queries increases and network dependency increases
Solution Approach 1:
The patent segments the LLM system into two parts: a lightweight local agent deployed on user devices for immediate query processing, and a remote server for centralized model management and updates. This segmentation enables fast local responses while maintaining simplified centralized control.
Solution Approach 2:
The local LLM agent acts as an intermediary between the user and the remote LLM system. It handles immediate queries locally and only communicates with the remote server when needed for model updates or complex tasks, reducing network dependency and improving response time.
2Device complexity
If LLMs rely on remote data sources, then centralized data management is simplified, but output quality decreases and user-specific customization is limited
Solution Approach 1:
The patent implements local quality by enabling each user device to store and utilize its own user-specific data locally through the local LLM agent. This allows the system to provide personalized responses tailored to individual user needs while maintaining simplified centralized data management for general model training data.
3Adaptability or versatility
If LLMs process requests from large numbers of user devices simultaneously, then service coverage is expanded, but response time slows down
Solution Approach 1:
The patent segments the processing load by deploying lightweight local agents on user devices that can independently handle query processing. This distributes the computational burden across many devices rather than concentrating it on a single remote server, maintaining fast response times even as service coverage expands to numerous users.
Data Source
AI summary
A system and method are configured to receive a pre-trained large learning model via the network interface, access user data stored locally, train the pre-trained large learning model using the locally stored user data to provide an enhanced local large learning model, detect that a browser hosted by the computer system is accessing a webpage, identify a webpage space configured to receive third-party content, examine the webpage to determine if content provided by the enhanced, local large learning model may be rendered at the webpage space, and causing content generated or selected by the enhanced, local large learning model to be rendered at the webpage space. The enhanced local large learning model may comprise a neural network.