Feature Information Deletion for Product Recognition Storage
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Solution Overview
Problem
The existing methods for product recognition in retail environments require multiple pieces of feature information for each product, leading to increased storage needs and inefficiencies.
Innovation Solution
An information processing apparatus and method that determines and deletes unnecessary feature information based on predetermined conditions, reducing the number of feature information pieces required for product recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple pieces of feature information are prepared for each product, then product recognition precision is improved, but storage area usage increases
Solution Approach 1:
The patent extracts and removes redundant feature information from the storage system. The deletion unit identifies and deletes feature information that is unnecessary for product recognition, keeping only the essential features needed for accurate recognition. This extracts the core useful information while removing the harmful excess storage consumption.
Solution Approach 2:
The patent implements a mechanism to discard redundant feature information while recovering and preserving only the necessary features. The determination unit evaluates each piece of feature information to decide whether to discard it, ensuring that only redundant data is removed while useful features are retained for future recognition tasks.
2Measurement precision
If multiple pieces of feature information are stored for each product, then recognition accuracy is maintained, but system complexity increases
Solution Approach 1:
The system extracts only the necessary feature information needed for accurate product recognition, removing redundant features that contribute to system complexity. This simplifies the data structure and reduces the computational burden while preserving recognition accuracy.
Solution Approach 2:
Instead of storing all possible feature information and filtering during recognition, the patent inverts the approach by pre-filtering and storing only the necessary features. This reverses the traditional workflow, reducing complexity at the storage level rather than at the processing level.
3Ease of operation
If redundant feature information is kept in storage, then product recognition can be performed, but storage efficiency decreases
Solution Approach 1:
The patent implements a discard-and-recover mechanism where redundant feature information is identified and discarded, while only the essential features are recovered and retained in storage. This maintains product recognition capability by preserving necessary features while improving storage efficiency by removing duplicates and unnecessary data.
Solution Approach 2:
The system performs self-service by automatically evaluating and managing its own feature information storage. The determination unit autonomously identifies redundant features and the deletion unit automatically removes them, eliminating the need for manual storage management while maintaining recognition capabilities.
Data Source
AI summary
A information processing apparatus (2000) includes a determination unit (2160) and a deletion unit (2180). The determination unit (2160) determines whether feature information to be determined satisfies a predetermined condition. When feature information to be determined is determined to satisfy the predetermined condition, the deletion unit (2180) deletes the feature information to be determined from the storage apparatus (120).