Search System Using Classification-Based Database Segmentation
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Solution Overview
Problem
Existing search systems using learners to find similar information are limited in accuracy as they can only search for similarities based on known features learned by the system, failing to account for unknown features.
Innovation Solution
A search system that divides databases into classifications based on the analysis results of input information, allowing for the search of information similar to the input within the corresponding classification, thereby improving search accuracy by utilizing feature vectors and scores as indexes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the learner searches for similar information based on known features learned in the system, then the search can be performed using existing analysis results, but the accuracy of the search cannot be fully improved because unknown features are not considered
Solution Approach 1:
The patent segments the search process into two distinct phases: (1) classification-based filtering that uses known features to divide the database into relevant categories, and (2) similarity search within those categories that can detect both known and unknown features. This segmentation allows the system to leverage the strengths of both supervised classification and unsupervised similarity measurement.
Solution Approach 2:
The patent introduces an intermediary mechanism (the classification result based on known features) that mediates between the input information and the final similarity search. This intermediary filters and organizes the search space, enabling more effective detection of similarities including unknown features within each classification category.
2Measurement precision
If the system searches all information in the database without classification, then it can potentially find similar information based on any features, but the search process becomes inefficient and time-consuming
Solution Approach 1:
The patent applies segmentation by dividing the entire database into multiple classification categories based on known features before performing similarity searches. This reduces the search space from the entire database to only relevant subsets, dramatically improving search speed while maintaining comprehensive similarity detection within each category.
Solution Approach 2:
The patent performs preliminary classification of the database based on known features before conducting the actual similarity search. This preliminary action organizes and filters the data in advance, so that when the similarity search is executed, it operates on a pre-filtered subset rather than the entire database, improving efficiency.
3Productivity
If the learner uses only classification results for search, then the search is fast and simple, but it cannot detect similarities in features that were not learned during training
Solution Approach 1:
The patent segments the feature detection capability into two layers: the classification layer that handles known features efficiently, and the similarity search layer that handles both known and unknown features. This segmentation allows each layer to specialize in its strength while working together to achieve comprehensive search capability.
Solution Approach 2:
The patent adds another dimension to the search process by introducing similarity search as a complementary approach to classification-based search. While classification operates in the dimension of known labeled features, similarity search operates in the broader dimension of feature space including unlabeled and unknown features, providing comprehensive coverage.
Data Source
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AI summary
To improve accuracy of search, a learner (L) of a search system (1) calculates a feature quantity of information that is input and outputs a first analysis result of the information in a first viewpoint and a second analysis result of the information in a second viewpoint based on the feature quantity. Storing means (102) stores a feature quantity of information to be searched, which has been input in the learner (L), in a database. Input means (103) inputs input information in the learner (L). Search means (104) searches for information to be searched that is similar to the input information in the feature quantity based on the database.