Heterogeneous Communication Analysis Using Large Language Models

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Solution Overview

Problem

Individuals face difficulty in recalling and synthesizing information from multiple communication channels across various meetings, chats, and emails due to the volume and diversity of interactions, necessitating a more efficient method to summarize and analyze these records.

Innovation Solution

Utilizing an AI assistant and large language models (LLMs) to generate a heterogeneous analysis of communication records by summarizing and prioritizing information from different channels based on user-defined constraints, providing a concise summary of discussions across multiple platforms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If users manually review extensive communication records from multiple channels, then they can obtain detailed information, but the time required and effort increase significantly

Engineering Contradiction:

Improveinformation recall completenessVSAvoidtime to analyze records

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent introduces an AI assistant as an intermediary between users and communication records. The AI assistant automatically retrieves, processes, and synthesizes information from multiple communication channels (emails, chats, meeting transcripts) and presents it in a structured format, eliminating the need for users to manually review extensive records while maintaining information completeness

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the mechanical process of manual information review with an automated AI-based system. The AI assistant uses natural language processing and large language models to automatically analyze and synthesize communication records, substituting human cognitive effort with automated intelligent processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of information

If users review all communication channels to ensure comprehensive analysis, then information completeness improves, but the complexity of the analysis process increases

Engineering Contradiction:

Improveinformation completenessVSAvoidanalysis process complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent creates a universal AI assistant that can handle multiple types of communication channels (emails, chat messages, meeting transcripts) through a single interface. The system automatically adapts to different channel types and applies appropriate processing methods, eliminating the need for users to manually manage the complexity of analyzing each channel separately

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent changes the parameter of information presentation from raw, unstructured communication records to synthesized, structured summaries with varying levels of detail. The AI assistant dynamically adjusts the output format based on user needs, transforming complex multi-channel data into manageable information presentations

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If users manually synthesize information from diverse communication channels, then they can identify key points, but the productivity decreases due to the manual effort required

Engineering Contradiction:

Improveinformation synthesis qualityVSAvoidanalysis efficiency

Core Design Contradiction:

Loss of informationVSProductivity

Solution Approach 1:

The patent enables the AI assistant to perform self-service information synthesis by automatically retrieving, processing, and synthesizing data from multiple communication channels without requiring manual user intervention. The system independently identifies key points and generates structured summaries, dramatically improving productivity while maintaining synthesis quality

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent implements feedback mechanisms where the AI assistant continuously refines its information synthesis based on user interactions and preferences. The system learns from user feedback to improve the quality of synthesized information over time, maintaining high-quality output while preserving productivity gains

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250298986A1Heterogeneous analysis of communication records using large language models

Publication Date: 2025.09.25 ZOOM VIDEO COMM INC

AI summary

One example method includes receiving a request to generate an analysis of communication records, the communication records associated with a plurality of types of communication records; accessing a plurality of communication records associated with the request, each communication record of the plurality of communication records corresponding to one type of the plurality of types of communication records; for the communication records of a respective type of communication records, generating, using a trained large language model (“LLM”), one or more analyses of the respective communication records; for each type of communication record, generating, using the trained LLM, a homogeneous analysis of the one or more analyses of the respective communication records corresponding to the respective type of communication records; generating, using the trained LLM, a heterogeneous analysis of the homogeneous analyses of the types of communication records; and providing the heterogeneous analysis in response to the request.