Image-Based Message Comprehension Using Semantic Word Group Analysis
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Solution Overview
Problem
Current electronic communication methods require users to read and process large volumes of text-based electronic messages, which can be time-consuming, especially when reviewing multiple messages in an activity stream.
Innovation Solution
A method and system that identify word groups in electronic messages and automatically select images corresponding to their meanings, allowing users to view images instead of text to quickly understand the message content, utilizing natural language processing and semantic analysis to determine the meaning of word groups and retrieve relevant images from a database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users read text-based electronic messages, then they can understand the complete message content, but it takes more time to process and review multiple messages
Solution Approach 1:
The patent creates visual copies (images) of the text content through optical character recognition (OCR) and image-to-text conversion. These visual representations serve as alternative copies of the original text that can be processed faster by both humans and machines, enabling quick comprehension without reading the full text while maintaining message understanding
Solution Approach 2:
The patent replaces the mechanical reading process (eyes scanning text) with an automated vision-based system. By converting text to images and using OCR to extract text from images, the system substitutes manual text processing with automated visual processing, significantly reducing the time required to comprehend messages
2Productivity
If users read all text in electronic messages, then they receive complete information, but processing multiple messages becomes inefficient
Solution Approach 1:
The patent segments the message processing task into multiple components: extracting text from images, converting images to text, and processing only the essential information. This segmentation allows the system to handle multiple messages simultaneously and efficiently, improving productivity by processing only what is necessary rather than reading all text in detail
Solution Approach 2:
The system performs self-service processing by automatically converting images to text and extracting meaningful information without requiring manual intervention. The automated OCR and image-to-text conversion processes handle the processing work themselves, enabling efficient batch processing of multiple messages and significantly reducing the time users need to spend reviewing each message individually
Data Source
AI summary
A plurality of word groups that satisfy at least one criterion, each word group comprising at least one word, can be identified in an electronic message. For each word group that satisfies the at least one criterion, at least a first image corresponding to a meaning of the word group can be automatically selected from a plurality of images. Each image selected for each respective word group that satisfies the at least one criterion can be presented with the electronic message.