Communication Session Copilot for Context-Aware Transcript Assistance
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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, due to incomplete or context-lacking live transcripts.
Innovation Solution
A network-based communication session copilot utilizing generative language models provides personalized assistance by answering participant questions, summarizing sessions, and proactively suggesting queries or actions, with features like private and collaborative copilots, iterative processing of transcripts, and context preservation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If live transcripts are provided for network-based communication sessions, then participants can reference session content, but the transcripts lack context and completeness making them difficult to use
Solution Approach 1:
The patent introduces an intermediary processing system that receives raw transcript data and enriches it with contextual information from multiple sources (session metadata, participant profiles, shared content). This intermediary layer transforms incomplete transcripts into context-aware summaries without requiring changes to the underlying transcript generation technology.
Solution Approach 2:
The system automatically generates context-aware summaries by self-service mechanisms including iterative processing of transcripts, automatic identification of key decisions and action items, and dynamic updating as new session data becomes available. This eliminates the need for manual transcript annotation while maintaining high quality.
2Loss of information
If detailed transcripts of all session content are recorded, then complete information is preserved, but participants struggle to find specific information efficiently
Solution Approach 1:
The patent extracts only the most relevant information from complete session transcripts, identifying key decisions, action items, and critical discussion points. This extraction process creates condensed summaries that preserve essential information while removing redundant content, enabling rapid information retrieval without losing important details.
Solution Approach 2:
The system performs preliminary processing of transcripts during or immediately after sessions, pre-identifying and organizing key information before users need to search. This includes automatic categorization of content, creation of searchable indexes, and generation of executive summaries that prepare information for efficient access.
3Measurement precision
If manual review and annotation of transcripts is performed, then context and accuracy are improved, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical review processes with automated machine learning systems that analyze transcripts, identify context, and extract key information. These systems use natural language processing and pattern recognition to achieve accuracy previously only attainable through human review, while operating at speeds thousands of times faster.
Solution Approach 2:
The system performs self-service annotation by automatically identifying context, decisions, and action items without human intervention. The iterative processing mechanism continuously refines its understanding of session content, improving accuracy over time while maintaining automated operation.
4Measurement precision
If iterative processing of transcripts is implemented, then context awareness and accuracy are improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial iterative processing by focusing computational resources on the most critical portions of transcripts that require context awareness. Rather than uniformly processing all content through multiple iterations, the system identifies high-value sections and applies iterative analysis selectively, achieving sufficient accuracy with reduced computational overhead.
Data Source
AI summary
A system for providing a personalized assistant within a network-based communication service includes one or more processors and a memory storage device storing instructions thereon. During a network-based communication session, the system receives a query from a computing device of a first communication session participant and processes the query by determining that a second communication session participant has shared content via a content sharing feature of the network-based communication service. In response, the system provides the query and at least a portion of the shared content as input to a model, which dynamically constructs a prompt for use as input with a generative language model. The system then presents the prompt as input to the generative language model, receives a response as output from the generative language model, and causes presentation of the response to be presented to the communication session participant.