Copilot Intent Routing for Long-Context Research Conversations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large language models (LLMs) are limited by linear chat sessions and context constraints, which hinder their ability to support complex, branching research activities that require multiple participants, extensive context management, and compliance with regulatory requirements.
Innovation Solution
A science platform integrating a copilot engine that infers user intent and provides prompts to LLMs, allowing for multistep responses, long-running sessions, and access to diverse data sources, including both public and private information, to facilitate research activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are used with linear chat sessions and fixed context limits, then the system is simple and easy to operate, but the system cannot support complex branching research activities requiring multiple participants and extensive context management
Solution Approach 1:
The patent segments the research activity support into distinct functional components: intent classification module that categorizes user inputs into different intent types, routing logic that directs different intents to appropriate response generation paths, and context management that handles different participant perspectives separately. This segmentation allows the system to support complex branching research activities while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent introduces an intermediary intent classification layer between the user input and the LLM response generation. This intermediary component analyzes the input message, determines the intent type (e.g., question about specific aspect, request for clarification, proposal), and routes it to appropriate handling logic. This mediator enables the system to manage complex research interactions without requiring the LLM to directly handle all decision-making, thus balancing adaptability with operational simplicity
2Duration of action of stationary object
If the LLM context is limited to a fixed number of tokens, then the system is efficient and fast, but the system cannot maintain extensive context for long-running research sessions
Solution Approach 1:
The patent extracts and separates different types of context information from the main LLM context window. It identifies and extracts participant perspectives, research objectives, and key findings as separate entities that can be managed independently. This extraction allows the system to maintain extensive context for long-running sessions by storing critical information outside the token limit while still making it accessible when needed, thus extending session duration without proportional increase in context window requirements
Solution Approach 2:
The patent applies local quality by providing different levels of context detail to different parts of the system. The LLM receives optimized context portions relevant to its specific task, while the overall system maintains comprehensive context including participant perspectives, research objectives, and historical interactions. This localized context distribution allows efficient LLM operation within token limits while the system as a whole maintains extensive context for long-running sessions
3Reliability
If the system provides detailed plans and visualizations for research activities, then the research support quality is high, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and structuring research data before it reaches the LLM. It pre-identifies relevant information, pre-organizes participant perspectives, and pre-prepares context summaries. This preliminary structuring reduces the computational burden during actual response generation, allowing the system to provide detailed plans and visualizations with reduced processing time while maintaining high research support quality
Data Source
AI summary
The present disclosure relates to methods and systems for using large language models to support research activities. The methods and systems include a copilot engine that creates input prompts to provide to the large language model to use in generating responses to input messages. The copilot engine infers an intent of the input messages and sends the intent with the input message in the input prompt to the large language model. The large language model generates different types of responses for different intents.