ML Message Summarization for Real-Time Communication Triage
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Solution Overview
Problem
Existing data processing systems struggle to efficiently handle and analyze complex data types such as photos, videos, and user-generated content, particularly in real-time communication scenarios, where users may not have the ability to fully engage with incoming data due to constraints like driving or being asleep.
Innovation Solution
Implementing a computing system with a neural network architecture, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and large language models (LLMs) to process and summarize communication data, such as voice mails, texts, and images, providing summaries or responses to user queries in a trusted execution environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing systems are used to handle complex data types (photos, videos, user-generated content), then data storage and basic organization are achieved, but efficient real-time analysis and processing capability deteriorates
Solution Approach 1:
The system segments the data processing task by separating raw data storage from processed information storage. Communication data is first stored in its raw form, then selectively processed by ML models to generate summaries and key information, which are stored separately as processed data. This segmentation allows efficient processing of only necessary data portions while maintaining system organization.
Solution Approach 2:
Machine learning models serve as intermediary components between raw communication data and user information needs. The ML models analyze raw data (messages, voice notes, images) and transform them into processed information (summaries, key points, responses), enabling efficient real-time analysis without requiring direct complex processing of all raw data by the user system.
2Loss of information
If users engage with all incoming communication data in detail, then complete information understanding is achieved, but user time and attention resources deteriorate
Solution Approach 1:
The system extracts only the most critical information from communication data using ML models. Instead of presenting users with all raw data, the system extracts and presents summarized key points, important messages, and relevant information first. This allows users to access essential information quickly without needing to review all original communications, reducing time loss while maintaining information completeness through available summaries.
Solution Approach 2:
The system performs preliminary analysis of communication data by ML models before user engagement. Summaries, key information extraction, and data organization are completed in advance, so when users need to access information, they receive pre-processed results rather than raw data. This preliminary action eliminates the need for users to spend time on basic data processing while ensuring complete information availability through the summaries.
3Speed
If communication data is processed and analyzed in real-time, then responsiveness to user needs is improved, but data processing resources and energy consumption increase
Solution Approach 1:
The system applies partial processing by analyzing only portions of communication data that are most relevant to user needs. Rather than processing all incoming data continuously, the system processes data selectively based on user interactions, priorities, and patterns. This partial action approach enables real-time responsiveness for critical communications while reducing overall energy consumption by avoiding unnecessary processing of all data types and messages.
Data Source
AI summary
According to the present techniques there is disclosed a system comprising a computing resource and a method performed at a computing resource, the method comprising: obtaining communication data of one or more messaging applications; executing a ML model to analyze at least a portion of the communication data; generating, responsive to the analysis of the at least a portion of the communication data, response data for the at least a portion of the communication data; and outputting the response data.