Photo Notice Data Extraction for Workflow Initiation
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Solution Overview
Problem
Existing systems face challenges in accurately and efficiently transforming information from physical resource transmission request notices into electronic data for online workflows due to format variability and lack of security features, leading to human errors and inefficiencies.
Innovation Solution
A system that uses image data processing to identify and extract field data from physical notices, employing heatmaps and a field data extraction model to determine field names and values, even in unknown formats, and generate electronic requests for resource transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data entry is used for resource transmission requests, then system flexibility is maintained, but human error increases and processing speed decreases
Solution Approach 1:
The patent replaces manual mechanical data entry with an automated image processing system that captures images of physical notices and automatically extracts data using OCR and machine learning algorithms, eliminating human error while maintaining processing flexibility
Solution Approach 2:
The system enables self-service by allowing the notice itself to provide the data through image capture and automated processing, without requiring human intervention to manually transcribe information from the physical notice to the digital system
2Productivity
If automated image processing is implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent implements a universal image processing system that can handle multiple types of physical notices (invoices, bills, forms) with varying formats through a single integrated platform using heatmaps and machine learning, rather than requiring separate systems for each notice type
Solution Approach 2:
The system uses heatmaps that define probabilistic regions for different data fields, allowing the same processing framework to adapt to various notice formats by changing parameter regions and probability thresholds rather than requiring different processing logic
3Measurement precision
If heatmaps and machine learning models are used, then data extraction accuracy improves, but computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models and creating heatmap templates during an offline phase, so that during actual processing, the system only needs to apply pre-computed heatmaps and trained models rather than performing complex computations in real-time
Data Source
AI summary
Systems and methods are provided for generating a resource transmission request to initiate a workflow associated with resource transmission. In particular, the disclosed technology is directed to processing image data corresponding to a physical notice of a request for resource transmission to generate an electronic resource transmission request. The system captures image data of the notice and extracts data from the image data. As an example, the system matches the extracted data against predetermined forms and determines whether the notice is in a known format. In instances where there is no match (such that the notice has an unknown format), the system uses one or more of heatmaps, rules of locating field data, and/or a field data extraction model to assign respective field names with data values in the extracted data. The heatmap includes regions in the image data with a likelihood of data values corresponding to particular field names.