OCR and Machine Learning for Collaboration Board Reuse
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Solution Overview
Problem
In business environments, multiple groups often work on similar software features or components across different projects without knowing about existing work, leading to redundant development efforts due to the lack of a quick and easy way to identify reuse opportunities.
Innovation Solution
A system and method leveraging OCR and machine learning to process digital images from collaboration boards, extract key phrases and concepts, and search metadata repositories to identify and rank potential reuse opportunities based on confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching and comparison of features across different groups is performed, then reuse opportunities can be identified, but it requires significant time and manual effort
Solution Approach 1:
The patent replaces manual mechanical searching and comparison processes with automated optical character recognition (OCR) technology and machine learning algorithms. The system captures images of collaboration boards, uses OCR to extract text and graphics, applies machine learning to identify key phrases and concepts, and automatically searches metadata repositories to detect reuse opportunities, thereby eliminating time-consuming manual efforts while maintaining high identification accuracy
Solution Approach 2:
The patent introduces an intermediary automated system that acts as a bridge between collaboration boards and metadata repositories. This intermediary system processes visual information from collaboration boards through OCR and machine learning, then queries metadata repositories to identify potential reuse opportunities, enabling efficient information retrieval without direct manual intervention
2Measurement precision
If comprehensive metadata repositories are searched to identify reuse opportunities, then more accurate matches can be found, but the search complexity and processing requirements increase
Solution Approach 1:
The patent segments the search process into distinct automated stages: image capture from collaboration boards, OCR text extraction, machine learning-based concept identification, metadata repository querying, and reuse opportunity ranking. Each stage processes specific types of information independently, reducing overall system complexity while enabling comprehensive and accurate searches across large metadata repositories
Solution Approach 2:
The system performs self-service by automatically executing the entire reuse detection workflow without manual intervention. The machine learning models autonomously extract key phrases and concepts from collaboration board images, automatically query metadata repositories using these extracted terms, and generate ranked lists of reuse opportunities based on confidence scores, eliminating the need for complex manual search operations
3Productivity
If multiple groups work independently on similar features without coordination, then each group can work autonomously and quickly, but redundant development work occurs
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors collaboration boards across different groups and automatically detects potential reuse opportunities. When similar features are identified, the system generates feedback by querying metadata repositories and producing ranked reuse recommendations, enabling groups to discover existing work before investing significant development effort, thus preventing redundant work while preserving autonomous development speed
Solution Approach 2:
The system performs preliminary action by proactively analyzing collaboration boards and searching metadata repositories before development work begins. By automatically extracting key phrases and concepts from collaboration board images and querying existing metadata, the system identifies potential reuse opportunities in advance, allowing groups to avoid redundant development while maintaining their autonomous workflow and productivity
Data Source
AI summary
An embodiment of the present invention is directed to capturing collaboration boards in images, applying OCR and/or other recognition technology to read the collaboration boards and leveraging machine learning to query metadata repositories to identify reuse opportunities.