Generative AI Query System for Virtual Meetings
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Solution Overview
Problem
Users face challenges in efficiently obtaining relevant information from missed virtual meetings, as they need to sift through chat history or recordings, which can be time-consuming and inefficient.
Innovation Solution
A communication platform employs generative AI models for automatic answer generation and follow-up query suggestion, allowing users to interactively query virtual communication sessions through a GUI, with trained models using consented communication data to provide timely and relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually review chat history or recordings to obtain information from missed virtual meetings, then they can access the information, but the process is time-consuming and inefficient
Solution Approach 1:
The system enables self-service by having the communication platform automatically process virtual communication data and generate answers to user queries without requiring manual review of chat history or recordings. The AI model autonomously searches, processes, and retrieves relevant information, freeing users from manual review tasks.
Solution Approach 2:
The patent replaces the mechanical manual review process with an AI-based automated system. Instead of users manually scrolling through chat histories or watching recordings, the system uses machine learning models to automatically process the data and generate relevant answers, substituting human manual labor with automated intelligent processing.
2Loss of information
If users manually sift through chat history or recordings, then they can find relevant information, but the process is complex and labor-intensive
Solution Approach 1:
The AI model serves as an intermediary between the user and the vast amount of virtual communication data. Instead of users directly sifting through raw chat history or recordings, the AI model processes the data, filters relevant information, and presents it as structured answers, simplifying the information retrieval process.
Solution Approach 2:
The system extracts only the relevant information from the vast amount of virtual communication data. The AI model identifies and separates meaningful content from unnecessary data, presenting users with condensed, relevant answers rather than requiring them to manually filter through entire chat histories or recordings.
3Extent of automation
If the communication platform processes virtual communication data to generate answers, then user queries are answered automatically, but data privacy and security concerns arise
Solution Approach 1:
The patent applies local quality by processing data locally on user devices rather than centrally. The AI model can execute on the user's local device or within their private environment, ensuring that sensitive virtual communication data remains localized and does not need to be transmitted to external servers, thereby reducing privacy and security risks.
Data Source
AI summary
Example methods and systems for facilitating queries about a virtual communication session are provided. A communication platform receives an initial query about the virtual communication session from a user. The communication platform accesses virtual communication data associated with a virtual communication session. The communication platform generates an initial response to the initial query based on the virtual communication data using a first pre-trained generative artificial intelligence (AI) model. The communication platform generates a first set of follow-up queries based on the initial response using a second pre-trained generative AI model. The communication platform receives a selection of a first follow-up query out of the first set of follow-up queries. The communication platform provides a first response to the first follow-up query using the first pre-trained generative AI model.