AI Conversation Summaries Tailored to User-Relevant Points
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Solution Overview
Problem
It is difficult for individuals to efficiently catch up on missed oral conversations, especially in business settings, as separating relevant information from irrelevant information in audio or video recordings is challenging, requiring extensive review of lengthy transcripts.
Innovation Solution
Utilizing artificial intelligence (AI) and machine learning (ML) to analyze recorded conversations and generate customized summaries based on user-specific inputs, such as keywords, user profiles, or audience interests, to highlight relevant points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If full audio or video recordings of conversations are reviewed to extract relevant information, then complete information is obtained, but significant time is lost and the process becomes inefficient
Solution Approach 1:
The patent extracts only the relevant information from full conversation transcripts by using AI models to identify and summarize key points based on user-specified criteria, topics, or interests. This extraction process creates condensed summaries that retain essential information while removing unnecessary content, directly resolving the contradiction between information completeness and review time.
Solution Approach 2:
The system performs preliminary analysis of conversation transcripts using AI models to pre-identify and organize relevant information before the user needs it. By pre-processing the full transcripts into structured summaries with key points, topics, and entities, the system prepares the information in advance, eliminating the need for users to manually review entire recordings when they need specific information.
2Productivity
If customized summaries are generated using AI models with multiple input parameters, then information retrieval efficiency is improved, but system complexity increases
Solution Approach 1:
The patent employs a universal AI model architecture that can handle multiple types of input parameters (user profiles, keywords, topics, interests) and generate customized summaries for different purposes. This multi-functional approach allows the same core system to serve various information retrieval needs without requiring separate specialized systems, managing complexity while maintaining versatility.
Solution Approach 2:
The system introduces an intermediary AI model layer between the raw conversation transcripts and the user. This intermediary process automatically processes the transcripts, applies user-specific criteria, and generates customized summaries, mediating the complexity of transcript analysis while delivering simplified, tailored results to users without requiring them to directly manage the complex processing operations.
Data Source
AI summary
A method includes receiving text corresponding to a conversation between two or more people, receiving information for use in customizing a summary of the conversation that is to be generated, and generating the summary of the conversation that is customized according to the received information. The summary of the conversation is generated by feeding the received text and the received information into an artificial intelligence model that uses the received text and the received information as inputs. A system includes a network interface and a processor-based system configured to receive text corresponding to a conversation between two or more people, receive information for use in customizing a summary of the conversation that is to be generated, and generate the summary of the conversation that is customized according to the received information.