Similarity-Based Object Property Prediction Using Multi-Dimensional Feature Vectors
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Solution Overview
Problem
Current database systems and search technologies are inadequate for predicting object properties from complex data sets, particularly in identifying geographic origins and characteristics of objects based on multi-dimensional data, as they often rely on exact matches rather than similarity-based retrieval methods.
Innovation Solution
The implementation of a method and apparatus that uses similarity-based information retrieval and modeling, leveraging database and modeling technologies to infer object properties by searching for nearest neighbors in multi-dimensional databases, incorporating electrical, electromagnetic, and acoustic spectral data, along with micro-body assemblage data, to predict properties such as geographic origin and composition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact match search methods are used in database systems, then search precision is improved, but the ability to predict object properties from similar data sets deteriorates
Solution Approach 1:
The patent transforms the search approach by changing the parameter from exact matching to similarity-based matching using multi-dimensional feature vectors. This allows the system to find objects with similar properties rather than requiring identical matches, enabling property prediction from analogous data sets while maintaining search effectiveness
Solution Approach 2:
The patent introduces feature vectors as an intermediary representation between raw object data and search queries. These vectors capture essential characteristics in a standardized format, allowing the system to compare and predict properties based on similarity without requiring exact matches between original data formats
2Adaptability or versatility
If similarity-based retrieval methods are implemented, then the ability to predict object properties is improved, but search precision deteriorates
Solution Approach 1:
The patent resolves this contradiction by operating in multi-dimensional feature space rather than single-dimensional exact matching. By projecting objects into vectors with multiple attributes, the system can simultaneously achieve similarity-based property prediction and maintain precision through weighted dimension evaluation and distance metrics in the extended space
3Measurement precision
If multi-dimensional databases with spectral data are used, then object characterization accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex multi-dimensional data into distinct feature vectors representing different object characteristics (electrical, electromagnetic, acoustic spectral data, micro-body assemblage data). This segmentation allows the system to manage complexity by processing individual feature sets independently while integrating results for comprehensive object characterization
Solution Approach 2:
The patent creates a universal feature vector framework that can handle multiple types of spectral data and object properties through a common retrieval mechanism. This multi-functional approach reduces overall system complexity by using a single standardized interface for diverse data types rather than separate processing systems for each data category
Data Source
AI summary
Method and apparatus for predicting properties of a target object comprise application of a search manager for analyzing parameters of a plurality of databases for a plurality of objects, the databases comprising an electrical, electromagnetic, acoustic and thermal spectral database (ESD), a micro-body assemblage database (MAD) and a database of image data whereby the databases store data objects containing identifying features, source information and information on site properties and context including time and frequency varying data. The method comprises application of multivariate statistical analysis and principal component analysis in combination with content-based image retrieval for providing two-dimensional attributes of three dimensional objects, for example, via preferential image segmentation using a tree of shapes and to predict further properties of objects by means of k-means clustering and related methods. By way of example, an evidence tree display showing a target object linked by a pathway to a predicted property comprises a similarity value, a speculation value and a model-based rank.