AI Clipboard Capture for OCR-Based Cross-Device Data Transfer
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Solution Overview
Problem
Current methods lack an efficient and convenient way to digitize physical documents and screen content from other computing systems, requiring manual entry into applications, which is time-consuming and inefficient.
Innovation Solution
A system using AI models for Computer Vision and OCR to extract content from digital photographs of physical documents or screens, determine pertinent applications, and automatically transfer the information without manual entry, utilizing RPA robots or clipboard applications for seamless integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual entry is used to transfer data from physical documents to applications, then data accuracy can be maintained through user verification, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent uses optical copying (photographing documents with mobile device) combined with OCR technology to create digital copies of physical documents. This eliminates manual typing while maintaining data accuracy through automated text recognition and validation algorithms that verify the extracted content.
Solution Approach 2:
The patent replaces the mechanical process of manual data entry with automated systems including OCR for text extraction, AI models for data interpretation, and RPA robots for application interaction. This substitution dramatically reduces time consumption while maintaining accuracy through automated validation.
2Productivity
If manual data entry is performed, then user control and verification are possible, but productivity and operational efficiency decrease
Solution Approach 1:
The system enables self-service automation where the AI and RPA components autonomously perform data extraction, interpretation, and entry without continuous human intervention. The system handles the entire workflow independently, dramatically improving productivity while requiring minimal user input.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system validates extracted data, verifies target application fields, and can request user confirmation for ambiguous cases. This maintains user control and verification capability while achieving high automation levels for routine operations.
3Loss of time
If automated AI and RPA systems are implemented, then time consumption and manual effort are reduced, but system complexity and technological requirements increase
Solution Approach 1:
The patent employs universal AI models and RPA frameworks that can handle multiple document types, applications, and data formats through a single integrated system. This multi-functionality reduces the need for separate specialized tools, managing system complexity while providing comprehensive automation capabilities.
Solution Approach 2:
The system uses intermediary components including AI interpretation layers and RPA orchestration platforms that bridge the gap between diverse document formats and target applications. These intermediaries manage complexity by providing standardized interfaces and translation layers between different systems.
4Measurement precision
If comprehensive AI models are used for content extraction and application determination, then automation accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent segments the automated workflow into distinct stages: document photographing, OCR text extraction, AI content interpretation, target application determination, and data entry. Each segment uses appropriately scaled computational resources, avoiding the need for excessive computing power at every stage while maintaining overall accuracy.
Data Source
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AI summary
Artificial intelligence (Al)-driven, automatic data transfer including semantic associations between a source from a mobile device and a target computing system is disclosed. A digital photograph of the contents of physical documents or a screen of another computing system is obtained with the mobile device, the contents from the digital photograph are extracted, and pertinent target application(s) are determined using AI. The extracted, semantically matched contents are then entered into target application(s) of the target computing system without requiring manual entry by a user.