Point-of-Sale Image Auditing with Confidence-Ranked Candidate Lists
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Solution Overview
Problem
Existing automatic image recognition systems for point-of-sale images often fail to detect all products or incorrectly identify them, requiring significant manual intervention by auditors to review and correct the results, which is time-consuming and labor-intensive.
Innovation Solution
The system enables auditors to identify regions of interest (ROI) in images not recognized by the automatic image recognition engine, performs a confidence search to generate a list of candidate products with assigned confidence levels, and allows quick modification of product information, reducing the need for extensive manual review and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic image recognition is used to identify products in point-of-sale images, then productivity is improved, but measurement precision deteriorates due to detection failures and incorrect identifications
Solution Approach 1:
The patent introduces an intermediary verification system that bridges automatic recognition and final accuracy. The system generates candidate products with confidence scores, then uses interactive UI elements (hotspots, popups, sliders) to allow auditors to verify and correct results efficiently. This intermediary layer maintains high productivity while improving measurement precision through human-in-the-loop verification.
Solution Approach 2:
The system implements feedback mechanisms where auditor corrections are used to improve future automatic recognition. The confidence score thresholds and candidate ranking are adjusted based on verification results, creating a closed-loop system that continuously improves both speed and accuracy over time.
2Measurement precision
If manual review of automatic recognition results is performed, then measurement precision is improved, but loss of time increases due to extensive auditing requirements
Solution Approach 1:
Instead of requiring uniform review of all products, the system applies local quality by focusing auditor attention only on uncertain cases. Products are ranked by confidence score, and auditors interact primarily with low-confidence candidates through targeted UI elements like hotspots and popups. High-confidence products are automatically accepted, eliminating unnecessary review time.
Solution Approach 2:
The system performs partial action by reviewing only the necessary portion of results. Rather than 100% manual verification, the system processes candidates in order of confidence, stopping when a sufficient number of products are verified or when confidence thresholds are met. This partial review approach achieves acceptable precision without excessive time investment.
3Measurement precision
If comprehensive product detection is attempted, then measurement precision is improved, but device complexity increases due to multiple processing steps
Solution Approach 1:
The patent segments the detection process into distinct stages: initial automatic recognition, candidate generation with confidence scoring, ranked listing of alternatives, and interactive verification. Each stage handles a specific aspect of the problem, breaking down the complex task of comprehensive detection into manageable, modular steps that improve completeness without overwhelming system complexity.
Data Source
AI summary
Examples methods, apparatus/systems and articles of manufacture for auditing point-of-sale images are disclosed herein. Example methods disclosed herein include comparing a region of interest of an image displayed via a user interface with a plurality of reference product images stored in a database to identify a plurality of candidate product images from the plurality of reference product images as potential matches to a first product depicted in the image. For example, the candidate product images are associated with respective confidence levels indicating respective likelihoods of matching the first product. Disclosed example methods also include displaying, via the user interface, the candidate product images simultaneously with the image in a manner based on the respective confidence levels, and automatically selecting a first one of the candidate product images as matching the first product based on the respective confidence levels.