Vector-Based Item Categorization Reducing Processing Time
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Solution Overview
Problem
Current item categorization methods in natural language processing are inefficient and inaccurate, particularly in large datasets, as they require extensive processing time and resources, and struggle to identify similarities between items and categorized items effectively.
Innovation Solution
A device and method that map items to vectors based on their terms, compare these vectors with categorized items, and determine similarity values using techniques like hamming distance to categorize items efficiently and accurately, reducing processing time and conserving resources by identifying matches and unmatched items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional item categorization methods are used in large datasets, then comprehensive categorization coverage is achieved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent creates vector representations (copies) of items that capture their semantic meaning in a compressed numerical format. Instead of processing full text documents, the system works with these vector copies, dramatically reducing processing time while maintaining categorization accuracy. The vector embedding technique transforms complex item data into simplified numerical vectors that preserve essential characteristics for comparison.
Solution Approach 2:
The patent transforms item categorization from text-based processing to vector-based numerical processing. By changing the representation parameter from raw text to embedded vectors, the system achieves faster computation and lower resource consumption while maintaining or improving categorization accuracy through mathematical operations on the vectors.
2Measurement precision
If traditional text-based comparison methods are used, then detailed item analysis is possible, but accuracy in identifying similarities decreases
Solution Approach 1:
The patent replaces traditional text-based mechanical comparison methods with vector-based mathematical operations. Instead of manually analyzing and comparing text semantics, the system uses vector embedding and mathematical distance calculations (such as cosine similarity or Euclidean distance) to automatically and accurately measure item similarities, significantly improving both precision and efficiency.
3Reliability
If extensive processing is performed on large datasets, then complete categorization is achieved, but resource consumption increases
Solution Approach 1:
The patent creates compact vector representations that serve as efficient copies of the original item data. These vectors consume minimal computational resources to store and process while preserving the essential semantic information needed for accurate categorization, thereby reducing energy and resource consumption without sacrificing reliability.
Solution Approach 2:
By transforming the data representation from complex text structures to compact numerical vectors, the patent reduces the computational parameters required for processing. This parameter change enables the system to handle large datasets with lower memory requirements and fewer computational operations, decreasing resource consumption while maintaining categorization accuracy.
Data Source
AI summary
A device may receive information that identifies an item to be categorized. The device may map the item to a first vector based on the information that identifies the item. The device may compare the first vector to a second vector based on mapping the item to the first vector. The device may determine a similarity value between the first vector and the second vector based on comparing the first vector and the second vector. The device may determine that the similarity value satisfies a threshold. The device may determine a category associated with the item based on the similarity value satisfying the threshold. The second vector may be associated with the category. The device may provide information that identifies the category associated with the item to cause an action to be performed.