Visual Metadata Software Configuration via OCR
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Solution Overview
Problem
Conventional techniques for configuring business software are complex and error-prone, requiring extensive expertise and time, and often restrict developers to specific design systems, leading to inefficiencies and incompatibilities across different platforms.
Innovation Solution
A novel technique that captures digital images or audio recordings of data schemas and workflows, using machine learning to generate metadata for software configuration, allowing seamless integration across various design systems without requiring extensive programming or navigation through multiple screens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional techniques are used for configuring business software, then developers can access and configure software systems, but the configuration process becomes complex and error-prone requiring extensive expertise and time
Solution Approach 1:
The patent uses optical character recognition (OCR) technology to capture visual images of configuration screens and automatically convert them into machine-readable metadata. This copying approach eliminates the need for manual navigation through complex configuration interfaces, significantly reducing configuration time and errors while maintaining accuracy.
Solution Approach 2:
The patent replaces manual mechanical interaction with configuration screens (clicking, typing, navigating) with an automated image-based system. By substituting the mechanical configuration process with optical capture and AI-powered metadata generation, the system achieves faster, more accurate software configuration without requiring developer expertise.
2Reliability
If developers manually navigate through configuration screens, then software can be configured, but the process is prone to human errors and requires multiple attempts
Solution Approach 1:
By capturing configuration screens as images and using OCR to extract metadata, the system creates an accurate digital copy of the configuration state. This eliminates manual entry errors and ensures high reliability in software configuration while simplifying the process through automated image-based capture.
Solution Approach 2:
The patent implements a feedback mechanism where the AI system analyzes captured images, generates metadata, and allows for verification and correction. This feedback loop ensures configuration accuracy by enabling reviewers to validate the automated extraction before finalizing the software configuration, reducing the need for multiple attempts.
3Adaptability or versatility
If developers work with multiple design systems, then cross-platform compatibility can be achieved, but developers must spend endless hours updating cross-referencing design systems
Solution Approach 1:
The patent creates a universal image-based configuration system that works across multiple design systems and platforms. By using visual capture and AI-powered metadata extraction instead of system-specific manual procedures, the solution achieves cross-platform compatibility while eliminating the time-consuming task of maintaining cross-referencing design systems.
Solution Approach 2:
The system uses visual copying of configuration screens from any design system and converts them into standardized metadata through OCR and AI processing. This approach enables seamless integration across different design systems without requiring developers to manually update cross-references, significantly improving development efficiency while maintaining versatility.
4Reliability
If conventional database approaches are used, then data can be retrieved and stored, but secure and efficient retrieval of accurate information becomes difficult
Solution Approach 1:
The patent uses optical capture to create accurate visual copies of configuration screens and data displays, which are then converted into structured metadata through OCR. This copying mechanism ensures reliable and accurate data retrieval by eliminating manual transcription errors while simplifying the database interaction process through automated image-based data extraction.
Data Source
AI summary
In accordance with embodiments, there are provided mechanisms and methods for facilitating visual media metadata-based software configuration using design systems according to one embodiment. In one embodiment and by way of example, a method includes identifying a visual image of contents including a workflow of transactions to be performed by a software application associated with a software design system, where the visual image represents a digital photograph captured by a camera. The method may further include translating the contents into metadata compatible with the software design system, generating a process flow corresponding to the workflow and based on the metadata, and configuring the software application based on the process flow such that one or more objects of the software application are augmented based on the process flow.