Similarity-Based Object Property Prediction Using Multi-Dimensional Data Retrieval
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Solution Overview
Problem
Current database systems and search technologies are inadequate for predicting object properties and events, particularly in identifying the source or characteristics of objects based on similarity analysis, as they often rely on exact matches rather than similarity-based retrieval and fail to effectively utilize multi-dimensional data sets for inference.
Innovation Solution
The development of a method and apparatus that uses similarity-based information retrieval and modeling to infer object properties by analyzing multi-dimensional databases, incorporating techniques such as principal component analysis, data clustering, and multivariate statistical analysis to predict properties from similar previously analyzed objects, including geographic information, material composition, and event characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact match search is used in database systems, then search precision is improved, but adaptability deteriorates because the system cannot handle similarity-based retrieval or predict properties of objects without exact matches
Solution Approach 1:
The patent transforms the search parameter from exact matching to similarity-based matching by introducing feature vectors and distance metrics. Instead of requiring identical key-value pairs, the system computes similarity scores between query objects and database objects based on multiple attributes, enabling flexible retrieval while maintaining meaningful precision through configurable similarity thresholds.
Solution Approach 2:
The patent extends the search space by introducing multi-dimensional feature vectors to represent objects. Each object is characterized by multiple attributes simultaneously, and the search operates in this expanded dimensional space using distance metrics. This allows the system to retrieve objects that are similar across multiple dimensions rather than requiring exact matches on single keys.
2Device complexity
If traditional database search technologies are used, then device complexity is reduced, but loss of information increases because multi-dimensional data sets cannot be effectively utilized for inference
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing feature vectors for all database objects. This preprocessing step transforms raw multi-dimensional data into a standardized format suitable for similarity search, enabling efficient retrieval without requiring complex real-time computations during query execution.
Solution Approach 2:
The patent replaces traditional mechanical database indexing mechanisms with mathematical similarity computation. Instead of relying on fixed index structures optimized for exact matches, the system uses vector-space models and distance metrics to enable flexible similarity search across multi-dimensional data, preserving information that would be lost in traditional indexing approaches.
3Adaptability or versatility
If similarity-based information retrieval is implemented, then adaptability is improved by enabling prediction of object properties, but device complexity increases due to advanced indexing and modeling requirements
Solution Approach 1:
The patent creates a universal framework that handles multiple functions through a single similarity search mechanism. The same feature vector representation and distance metric computation that enable property prediction also support classification, clustering, and retrieval operations, reducing the need for separate complex systems for each function.
Solution Approach 2:
The patent uses feature vectors as simplified copies or representations of complex objects. Instead of manipulating entire multi-dimensional data sets directly, the system works with compressed vector representations that capture essential characteristics, enabling efficient similarity computation and property prediction without proportionally increasing system complexity.
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 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, one of a machine component or process failure event, an intrusion event and a fire event and residual objects may be predicted and located and qualified such that, for example, properties of the residual objects may be qualified, for example, via black body radiation and micro-body databases including charcoal assemblages.