Communication Session Copilot for Late-Joiner Catch-Up
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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 actions, managing differing recollections, and catching up on missed content, especially in long or dynamic sessions.
Innovation Solution
A network-based communication session copilot utilizing generative language models provides personalized assistance by answering questions, summarizing content, and proactively suggesting queries or summaries to enhance participant engagement and information management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If participants join network-based communication sessions after the start time, then they can participate in the session, but they struggle to catch up on missed content and rapid-fire discussions
Solution Approach 1:
The copilot proactively generates summaries and key point recaps of missed content before the late-arriving participant needs to catch up. The system anticipates the participant's need for context and prepares personalized summaries in advance, allowing the participant to quickly grasp essential information without interrupting the ongoing discussion.
Solution Approach 2:
The copilot acts as an intermediary between the rapid-fire discussion and the late-arriving participant. It processes the complex, fast-paced conversation and translates it into simplified, digestible summaries that bridge the gap between what was said and what the participant needs to understand, eliminating the need for the participant to wade through the entire original discussion.
2Productivity
If the communication session is long and dynamic, then more content can be covered, but participants struggle to remember decisions and actions
Solution Approach 1:
The copilot continuously monitors the conversation and provides real-time feedback to participants in the form of automated summaries and key point recaps. This feedback loop reinforces memory by periodically reiterating decisions and actions, ensuring that even in long sessions, participants retain critical information without requiring active recall.
Solution Approach 2:
The system provides self-service memory assistance by automatically generating and delivering summaries without requiring participants to actively recall or take notes. The copilot serves the participants' memory needs autonomously, freeing them to focus on the conversation while the system handles the burden of remembering and organizing information.
3Measurement precision
If participants ask for clarification during the session, then understanding improves, but it is disruptive and embarrassing especially for shy participants
Solution Approach 1:
The copilot serves as an intermediary that participants can consult for clarification without direct social interaction. Instead of shy participants having to verbally ask questions in front of peers, they can privately query the copilot, which processes their questions and provides answers without the social pressure or embarrassment of direct human interaction.
Solution Approach 2:
The system provides self-service clarification by allowing participants to privately query the copilot about confusing points without requiring them to publicly ask for help. This self-service approach eliminates the social barrier to seeking clarification, enabling participants to understand complex content at their own pace without disruption to the session flow.
4Ease of operation
If the copilot provides real-time summaries and personalized responses, then participant engagement improves, but the system complexity increases
Solution Approach 1:
The copilot is designed as a multi-functional system that handles multiple tasks through a single integrated platform: generating real-time summaries, providing personalized responses, offering clarification, and maintaining engagement. By consolidating these functions into one universal system rather than separate tools, the patent manages complexity through integration rather than multiplication of components.
Data Source
AI summary
A system for providing a personalized assistant within a network-based communication session includes a processor and a memory storage device storing instructions. The system determines when a first communication session participant joins the network-based communication session after a threshold duration of time subsequent to the start time of the session. Upon determining the first participant has joined, the system obtains content associated with the session and creates request data for a pre-trained generative language model. The request data includes an instruction requesting a predetermined number of suggested utterances not present in the content, each utterance relating to one or more topics corresponding to the content. The system transforms the request data to a command based on a command template and provides the command to the generative language model. The system receives a response from the model, including the predetermined number of suggested utterances, and presents them to the communication session participant in a graphical user interface while the session is in session.