Neural Network Message Summarization with Intent and Domain Analysis
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Solution Overview
Problem
Conventional message processing approaches often result in erroneous or inappropriate meanings being attributed to portions of original messages, especially when reducing word volume for concise communication on mobile devices.
Innovation Solution
A neural network-based message communication framework that uses machine learning-based natural language processing techniques to determine intent-related and domain-related information, generate summarizations, and convert them into audio format for on-demand playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If text is cropped to reduce word volume, then message conciseness is improved, but meaning accuracy deteriorates
Solution Approach 1:
The patent extracts and processes the entire original message through neural network-based NLP techniques to determine intent-related and domain-related information, rather than simply cropping text. This extraction approach maintains meaning accuracy while enabling concise summarization that preserves the original message's intent.
Solution Approach 2:
The patent introduces neural network-based NLP processing as an intermediary between the original message and the user. This intermediary analyzes the full message content, extracts key intent and domain information, and generates accurate summarizations, thereby maintaining meaning accuracy while reducing the information presented to the user.
2Reliability
If neural network processing is applied to maintain meaning accuracy, then meaning accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the neural network processing into distinct functional components: intent-related information determination, domain-related information determination, and summarization generation. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high meaning accuracy.
Solution Approach 2:
The patent employs a universal neural network-based NLP processing framework that handles multiple functions (intent determination, domain analysis, summarization) within a single integrated system. This multi-functionality reduces the need for separate processing systems for each task, thereby managing complexity while maintaining accuracy across different message types and domains.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for a neural network-based message communication framework with summarization and on-demand audio output generation are provided herein. An example computer-implemented method includes obtaining message communication content; determining intent-related information and domain-related information in the obtained message communication content by processing at least a portion of the obtained message communication content using one or more machine learning-based natural language processing techniques; generating a summarization of the obtained message communication content by processing, using at least one neural network, at least a portion of the obtained message communication content in connection with at least a portion of the determined intent-related information and at least a portion of the domain-related information; converting the generated summarization from a text format to an audio format; and performing at least one automated action based at least in part on the generated summarization in the audio format.