On-Shelf Availability Detection Using Ensemble Signal Scoring
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Solution Overview
Problem
Retail enterprises face challenges in accurately determining the availability of items on shelves due to inventory management inaccuracies caused by various factors such as mis-scanning, inconsistent stocking, movement of items without system updates, and theft, leading to potential loss of sales and customer dissatisfaction.
Innovation Solution
A system and method using an ensemble model to aggregate signals from multiple product availability detection systems, classifying potential unavailability events as strong or weak signals, and calculating an overall unavailability score to generate restocking assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple product availability detection systems are used to monitor item availability, then the reliability of availability detection is improved, but the device complexity increases
Solution Approach 1:
The patent combines multiple product availability detection systems (automated detection systems, manual detection systems, and inventory management systems) into a unified ensemble model that processes signals from all sources simultaneously. This merging approach integrates diverse detection methods to improve overall reliability while managing complexity through a coordinated framework rather than separate independent systems.
Solution Approach 2:
The patent creates a composite detection system by combining different types of detection signals (automated detection signals, manual detection signals, and inventory management signals) into an ensemble model. This composite approach leverages the strengths of each detection type to achieve higher reliability than any single system could provide alone.
2Productivity
If automated detection systems are deployed throughout the retail location, then the productivity of inventory monitoring is improved, but the loss of information about actual shelf availability occurs
Solution Approach 1:
The patent implements feedback mechanisms where manual detection signals and automated detection signals continuously validate and correct each other. When automated systems detect potential unavailability, the system seeks confirmation through additional signals before generating restocking notifications, ensuring that false positives are filtered out while maintaining high monitoring productivity.
Solution Approach 2:
The patent segments the detection system into multiple independent signal sources (automated detection systems, manual detection systems, inventory management systems) that each operate independently but contribute to the overall assessment. This segmentation allows each component to maintain its strengths while the ensemble model integrates them to reduce information loss.
3Loss of time
If frequent restocking assessments are performed, then the loss of time for restocking is reduced, but the use of energy for monitoring increases
Solution Approach 1:
The patent employs periodic restocking assessments where the ensemble model evaluates accumulated detection signals at scheduled intervals rather than continuously. The system monitors signals continuously but performs comprehensive restocking assessments periodically when sufficient signal accumulation occurs, balancing timely restocking with reduced energy consumption compared to continuous full-system activation.
Data Source
AI summary
A process for determining an on-shelf availability status of an item within a retail location is provided. In example aspects, a plurality of potential unavailability events associated with an item for sale at the retail location are received from a collection of product availability detection systems. Potential unavailability events are aggregated in an ensemble model to calculate an overall unavailability score for the item. Based on the overall unavailability score, different actions may be taken, such as updating a tracked inventory or generating a restocking assessment notification.