Item Recognition Unique Key Generation
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Solution Overview
Problem
Current methods for recognizing and classifying items, such as images and alphanumeric characters, require significant computational resources and seek to reduce computational load while maintaining accuracy.
Innovation Solution
A method that generates a unique key for items by concatenating and comparing numeric parameters, using a primary string and difference string calculated iteratively, to efficiently recognize or classify items by similarity index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex algorithms are used for item recognition and classification, then recognition accuracy is improved, but computational load increases
Solution Approach 1:
The patent segments the recognition process into distinct phases: image acquisition, parameter extraction, unique key generation, and comparison. By dividing the complex recognition task into smaller, manageable segments, the computational load is distributed and optimized at each stage while maintaining overall accuracy.
Solution Approach 2:
The patent transforms physical parameters of items into numerical values and then into unique alphanumeric keys. This parameter transformation allows the system to work with simplified data representations during comparison operations, reducing computational complexity while preserving the essential information needed for accurate recognition and classification.
2Measurement precision
If complex algorithms are used for item recognition and classification, then recognition accuracy is improved, but computation time increases
Solution Approach 1:
The patent performs preliminary actions by pre-generating and storing unique keys for items in the reference database before actual recognition operations. During runtime, the system only needs to extract parameters and generate a unique key for comparison, avoiding the need to process entire images or perform complex pattern matching, thus significantly reducing computation time.
Solution Approach 2:
The patent creates simplified copies of item characteristics in the form of unique alphanumeric keys that represent the essential features of each item. These keys serve as lightweight substitutes for full image data, enabling rapid comparison operations while maintaining recognition accuracy.
3Measurement precision
If detailed parameter analysis is performed for each item, then classification accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential parameters needed for recognition and classification from the full set of possible item attributes. By selecting and extracting only the most relevant physical parameters, the system achieves accurate classification without the complexity of analyzing all possible item characteristics.
Solution Approach 2:
The patent creates a universal framework where the same parameter extraction and unique key generation process can be applied to different types of items across various applications. This multi-functional approach allows the system to handle diverse recognition tasks with a single unified methodology, reducing overall system complexity.
Data Source
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AI summary
The method provides for acquiring a plurality of parameters of an item to be recognized or classified and associating a numerical value with each parameter of the item to be recognized or classified. A unique key is then created, associated with the item to be recognized or classified, based on the numerical values associated with the parameters of the item to be recognized or classified. The unique key comprises a concatenated sequence of parameters. The item is classified or recognized by comparing the unique key associated with te item to be recognized or classified with a plurality of previously stored unique keys.