Communication Session Copilot for Transcript-Guided Query Answers
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Solution Overview
Problem
Network-based communication sessions face challenges such as participants struggling to keep up with rapid-fire discussions, remembering decisions, and catching up on missed content, especially in long sessions, leading to inefficiencies and miscommunication.
Innovation Solution
A network-based communication session copilot utilizing generative language models provides personalized assistance by answering participant questions, summarizing content, and suggesting queries to enhance session management and information retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If participants manually search through transcripts to find information, then they can access detailed content, but it increases time consumption and reduces session efficiency
Solution Approach 1:
The copilot system automatically processes transcripts and generates summaries, action items, and answers questions without requiring participants to manually search through the full transcript. The system serves itself by autonomously analyzing communication data and providing valuable information on demand.
Solution Approach 2:
The copilot performs preliminary processing of transcripts by continuously analyzing communication data, identifying key information, generating summaries, and preparing answers to potential questions before participants need them. This advance preparation eliminates the need for manual searching during critical moments.
2Reliability
If participants ask for clarification during rapid-fire discussions, then they can ensure understanding, but it disrupts the flow of communication and reduces productivity
Solution Approach 1:
The copilot provides real-time feedback by monitoring communication patterns, identifying when clarification is needed, and offering suggested questions or summaries that help participants understand the discussion flow without interrupting the natural rhythm of the conversation. The system adapts to communication dynamics and provides timely insights.
Solution Approach 2:
The copilot acts as an intermediary between participants and the communication data. Instead of participants directly analyzing complex transcripts or interrupting to seek clarification, the copilot mediates by providing pre-processed information, key takeaways, and contextual summaries that maintain flow while ensuring understanding.
3Productivity
If the copilot processes and analyzes communication data in real-time, then it can provide timely summaries and answers, but it increases computational complexity and resource requirements
Solution Approach 1:
The copilot system segments the communication data into manageable units such as individual messages, topics, and action items. By processing data in discrete segments rather than handling the entire transcript at once, the system reduces computational complexity while maintaining real-time responsiveness and providing timely summaries.
Data Source
AI summary
A system for providing a personalized assistant for network-based communication services utilizes one or more processors and memory to enhance user interaction through intelligent query processing. The system receives queries from computing devices and processes them using an intermediate model that analyzes communication session transcripts, user data, and session metadata alongside shared content from the communication service. The intermediate model generates prompt templates with content selection criteria to identify relevant transcript portions and shared content, constructing targeted prompts for a generative language model. The system handles various content types including files, screen sharing, and chat messages through rule-based engines, while employing transcript partitioning and rolling summary techniques for extended sessions. Advanced features include predictive follow-up query generation with response caching, role-based prompt customization, and feedback-driven learning for continuous improvement. The generative language model output is translated into personalized responses and presented to users.