Automated Item Attribute Correlation via OCR
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Solution Overview
Problem
Companies face inefficiencies in determining the best value for items purchased from various vendors, as current methods require painstaking searches through databases to evaluate prices, quantities, and types, especially for large sets of items, making it time-consuming and impractical.
Innovation Solution
A system that uses software, firmware, or a combination of both to perform text recognition on images, classify data cells, identify key cells, and determine correlations between items, allowing for the adjustment of attributes based on defined parameters, thereby automating the correlation and adaptation of item attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching through databases is used to evaluate item prices, quantities, and types, then evaluation accuracy can be maintained, but the process becomes time-consuming and impractical for large sets of items
Solution Approach 1:
The patent replaces manual mechanical searching and evaluation processes with an automated image recognition and data processing system. The system captures images of item displays, automatically extracts pricing and quantity information through optical character recognition (OCR), and processes the data to evaluate item values, thereby substituting human manual labor with automated technological processes that achieve both speed and accuracy.
Solution Approach 2:
The system enables self-service by automatically performing data extraction, correlation, and evaluation without requiring manual intervention. The automated process captures images, extracts text data, identifies key information, correlates items across different displays, and generates value assessments independently, freeing administrators from painstaking manual searches while maintaining evaluation quality.
2Productivity
If automated image recognition and data processing is implemented, then processing speed and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent divides the complex automated processing system into distinct functional modules: image capture module, optical character recognition module, data extraction module, item correlation module, and value evaluation module. Each module performs a specific function, making the overall complex system manageable through modular design where each segment can be independently developed, tested, and maintained.
Solution Approach 2:
The system introduces an intermediary data processing layer that bridges the gap between raw image data and final evaluation results. This intermediary layer includes structured data extraction and normalization processes that convert unstructured image data into organized information, facilitating smoother processing and reducing the direct complexity burden on the system architecture.
3Measurement precision
If detailed classification and correlation of item attributes is performed, then data accuracy and completeness are improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by focusing detailed classification and correlation efforts on key attributes that matter most for item evaluation, such as price, quantity, and item type, rather than uniformly processing all possible attributes with equal depth. This selective approach ensures high accuracy for critical data points while reducing unnecessary processing overhead on less important attributes.
Solution Approach 2:
The system performs partial action by extracting and correlating only the essential attributes needed for value evaluation from the complete set of possible item attributes. Rather than exhaustively processing every detail, the system identifies and focuses on the most relevant information (price, quantity, item identification) to achieve sufficient accuracy for business decisions without excessive processing time.
Data Source
AI summary
The technology includes an example method for parsing data to determine corollary objects and adapt attributes of the corollary objects using the data. In some implementations, the method may include receiving one or more images, performing text recognition to determine recognized text in the one or more images, and determining data cells containing information associated with a first item. The method may then classify one or more of the determined data cells, and may identify key cells in the determined data cells based on the classification of the one or more determined data cells. Correlations between information contained in the key cells and a second item, the second item including an interchangeable item to the first item may be determined, and the method may adjust attributes associated with the second item based on defined parameters and the recognized text.