Multi-Party Communication Response Timing Using LLM Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Technical support systems face challenges in determining when to respond to multi-party communications, especially when the communication is not directly addressed to the technical support service.
Innovation Solution
The system employs an instruct-based Large Language Model (LLM) to determine whether the technical support system should respond to a multi-party communication, and a generative-based LLM to generate an appropriate response when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the technical support system monitors all multi-party communications to ensure accurate response timing, then the reliability of support responses is improved, but the device complexity and computational resources required increase significantly
Solution Approach 1:
The system segments the communication monitoring task by identifying specific response indicators (such as direct questions, explicit requests for support, or technical error codes) that trigger a response. Instead of analyzing all communication content equally, the system divides the communication stream into segments containing these specific indicators, reducing the overall complexity while maintaining response accuracy for relevant communications.
Solution Approach 2:
The patent introduces an intermediary classification layer between communication reception and response generation. This intermediary component analyzes incoming communications to determine whether they require a technical support response, acting as a filter that reduces the burden on the main support system while ensuring reliable identification of response-worthy communications.
2Reliability
If the technical support system responds to all communications in multi-party conversations, then the completeness of support coverage is improved, but the loss of time for determining appropriate response timing increases
Solution Approach 1:
The system performs preliminary analysis of communications to identify response indicators before committing to a full response generation process. By pre-identifying which communications require responses based on specific criteria (direct address to support system, explicit technical questions, error messages), the system reduces the time needed for final response determination while ensuring comprehensive coverage of all relevant communications.
Solution Approach 2:
The system applies partial action by responding only to communications that meet specific criteria rather than all communications. This selective approach reduces the overall time investment while maintaining sufficient coverage of all communications that genuinely require technical support intervention, avoiding waste of time on communications that do not need responses.
3Measurement precision
If human oversight is used to determine when to respond in multi-party communications, then the measurement precision of response timing is improved, but the productivity of the technical support system decreases
Solution Approach 1:
The system implements self-service by automatically analyzing communications and determining response timing based on predefined indicators and patterns. The technical support system serves itself by identifying which communications require responses and when to respond, eliminating the need for human oversight in timing decisions while maintaining high precision through trained analysis algorithms.
Solution Approach 2:
The patent replaces the mechanical system of human oversight with an automated computational system that analyzes communications using pattern recognition and indicator detection. This substitution maintains measurement precision in response timing by using consistent, rule-based analysis while dramatically increasing productivity by handling multiple communications simultaneously without human intervention.
4Manufacturing precision
If the technical support system analyzes every communication in detail to determine response necessity, then the manufacturing precision of response quality is improved, but the use of computational energy increases
Solution Approach 1:
The system segments the analysis process into two stages: a quick filter stage that identifies obvious response indicators (direct addresses, explicit questions) and a detailed analysis stage that applies only to communications passing the filter. This segmentation maintains high response quality for relevant communications while reducing computational energy by avoiding detailed analysis of communications that clearly do not require responses.
Solution Approach 2:
The system applies partial analysis action by performing detailed quality analysis only on communications that meet specific criteria, rather than analyzing every communication in full detail. This approach maintains manufacturing precision for responses that are actually needed while reducing overall computational energy consumption by using lighter-weight analysis for filtering the majority of communications.
Data Source
AI summary
A technical support system obtains a communication addressed to the technical support system and at least one other recipient and transmits first information associated with the communication to a first Large Language Model (LLM) to determine whether the technical support system is to respond to the communication. In response to determining that the technical support system is to respond to the communication, the technical support system transmits second information associated with the communication to a second LLM with a request to generate a response to the communication and transmits the response to the communication.