Mobile Device Interactive Product Auditing with Image Recognition
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Solution Overview
Problem
Shelf audits are labor-intensive and costly, with existing methods involving manual data collection and image recognition requiring human verification, leading to inaccurate and time-consuming results.
Innovation Solution
Interactive product auditing using a mobile device with image recognition, allowing users to capture and analyze point-of-sale images, perform image stitching, and modify results in real-time, reducing the need for manual intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image recognition is performed on point-of-sale images to identify products, then productivity increases, but measurement precision deteriorates due to manual verification requirements
Solution Approach 1:
The system provides real-time feedback to the auditor by displaying detected products, quantities, and locations on the mobile device screen. This allows the auditor to immediately verify and correct any misidentifications, ensuring high measurement precision while maintaining the speed benefits of automated image recognition.
Solution Approach 2:
The mobile device performs self-verification through on-device image processing and pattern matching algorithms. The system automatically compares detected patterns against product databases and provides confidence scores, enabling the device to self-correct minor identification errors without requiring constant external intervention.
2Measurement precision
If manual data collection is used for shelf audits, then measurement precision is maintained, but productivity decreases due to labor intensity
Solution Approach 1:
The patent replaces manual mechanical data collection with automated image recognition technology. The mobile device captures images of shelves and automatically identifies products, quantities, and locations using computer vision algorithms, eliminating the need for manual counting and scanning while maintaining high data accuracy through pattern matching and verification mechanisms.
3Measurement precision
If image recognition results are processed centrally, then measurement precision improves through verification, but loss of time increases due to transmission and processing delays
Solution Approach 1:
The mobile device performs preliminary image processing and pattern recognition locally before any data transmission occurs. By pre-processing images and identifying potential products on-device, the system eliminates time-consuming transmission and initial processing steps, achieving both speed and accuracy through distributed computation.
Solution Approach 2:
The mobile device acts as an intermediary between the auditor and the central processing system. It performs initial image analysis and pattern matching locally, then transmits only verified results to the central server for final confirmation, reducing the overall processing time while maintaining precision through this two-stage verification approach.
Data Source
AI summary
Interactive product auditing with a mobile device is described. Example methods disclosed herein include performing, with an auditing device, image recognition based on a first set of candidate patterns accessed by the auditing device to identify a first product in a first region of interest of a segmented image. The disclosed example methods also include prompting, with the auditing device, a user to enter input associated with a first grid of the first region of interest displayed on a display, the first grid including the first product. The disclosed example methods further include determining, with the auditing device, a second set of candidate patterns to use to identify a second product in a second region of interest of the segmented image, the second set of candidate patterns determined based on the user input and a group of products identified in a neighborhood of the first region of interest.