Machine-Learning Image Extraction for Unstructured Data Workflows
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Solution Overview
Problem
Conventional systems struggle with accurately identifying and processing unstructured data within images due to format variations, leading to user errors, inefficiencies, and increased processing latency.
Innovation Solution
A machine-learning model is trained to identify and classify unstructured data within images, allowing for automated data extraction and validation, independent of user input, using supervised and unsupervised learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image processing techniques are used to identify data, then the process is simpler to implement, but the accuracy and reliability deteriorate due to inability to handle format variations
Solution Approach 1:
The patent replaces conventional mechanical image processing techniques with machine learning algorithms that can adapt to various data formats. The system uses trained models to automatically identify and extract data from images with different structures, eliminating the need for complex manual processing rules while improving accuracy and reliability.
2Productivity
If manual data entry is performed, then the process is more controllable, but the time consumption and productivity loss increase
Solution Approach 1:
The system enables self-service data extraction where the machine learning model automatically identifies, extracts, and validates data from images without requiring manual user input. The model independently handles the entire data processing workflow, significantly reducing processing time and increasing productivity while maintaining accuracy through automated validation mechanisms.
3Ease of operation
If manual data entry is required, then the risk of user error is reduced through human review, but the ease of operation deteriorates due to additional user tasks
Solution Approach 1:
The patent implements automated feedback mechanisms where the machine learning model validates extracted data against expected formats and patterns, providing immediate feedback on data accuracy. The system automatically corrects or rejects erroneous entries without requiring user intervention, thereby maintaining high data accuracy while simplifying user interaction to merely uploading images.
4Extent of automation
If conventional systems are used, then the device complexity is lower, but the extent of automation deteriorates due to inability to execute workflows independently
Solution Approach 1:
The patent creates a universal machine learning system that can handle multiple data formats and execute various workflows independently. The trained model serves multiple functions: identifying data, validating accuracy, and triggering appropriate workflows based on extracted information. This multi-functional approach achieves high automation extent while managing system complexity through a single integrated platform rather than multiple specialized systems.
Data Source
AI summary
The disclosed techniques are directed to identifying textual data instances depicted within images having an unstructured/undefined format. A machine-learning model may be trained to identify textual data instances within the image and corresponding data types for the textual data instances. The values and/or data types of the textual data instances may be compared to previously-stored data that is associated with a data provider. If the values and/or data types match the previously-stored data, the values corresponding to the textual data instances may be used to execute one or more processes. Executing a process may comprise transmitting one or more data messages that include one or more values of the textual data instances. The disclosed techniques may be executed as part of a monitoring process that obtains images over a time period, detects and validates the textual data instances depicted within those images, and executes one or more additional processes using values extracted from the images.