Aggregation Engine for Structural Representation of Physical Entities
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Solution Overview
Problem
Pattern recognition in intelligent systems is computationally intensive, particularly when comparing physical entities, as it often requires well-defined basic objects, which are not always available, leading to inefficiencies in recognizing internal structures and reducing computation time for usable structure representations.
Innovation Solution
A method and system utilizing an aggregation engine that updates the state and label of processing units based on links between them, forming a graphical representation of physical entities, allowing for efficient recognition of internal structures without relying on pre-defined basic objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pattern recognition methods are used to compare physical entities, then object comparison can be performed, but computational burden increases significantly
Solution Approach 1:
The patent segments physical entities into discrete processing units that represent individual elements or components. Each processing unit maintains state and label information, allowing the system to analyze entities hierarchically rather than as monolithic objects, thereby reducing computational complexity while preserving comparison accuracy
Solution Approach 2:
The patent creates graphical representations as simplified copies of physical entities, where processing units represent entity elements and links represent relationships. These graphical models enable efficient computation on structured data rather than raw entity comparisons, reducing computational burden while maintaining measurement precision
2Measurement precision
If well-defined basic objects are used as comparison bases, then object characterization can be achieved, but the method fails when basic objects are not available
Solution Approach 1:
The patent enables processing units to autonomously develop their state and label information through iterative aggregation with neighboring units. This self-organizing capability allows the system to characterize entities without requiring pre-defined basic objects, making the method adaptable to novel or previously unrecognized entity types while maintaining characterization accuracy
Solution Approach 2:
The patent implements dynamic state updates where processing unit labels evolve iteratively based on aggregation with connected units. This dynamic approach allows the system to adapt to different entity types and structures without requiring static pre-defined basic objects, enhancing versatility while preserving measurement precision through convergence to stable label assignments
3Measurement precision
If detailed segment comparison is performed for object recognition, then recognition accuracy improves, but computational complexity increases
Solution Approach 1:
The patent divides physical entities into discrete processing units with hierarchical organization. This segmentation enables detailed analysis of entity components while managing complexity through modular processing units that can be independently updated and aggregated, balancing recognition accuracy with system complexity
Solution Approach 2:
The patent combines information from multiple processing units through aggregation operations where state and label data are merged iteratively. This merging process achieves comprehensive recognition accuracy by integrating segment-level details while reducing overall system complexity through consolidated representation at higher hierarchical levels
Data Source
AI summary
The present disclosure relates to a method a system and an aggregation engine for providing a structural representation of a physical entity. Processing units provide representation of elements composing the physical entity. Processing units comprise a label, which represent the elements, and a state. Links are established between the processing units. By iteration in the aggregation engine, the states and labels of the processing units are updated based on states and labels of linked processing units. A graphical representation of the physical entity is obtained based on the labels, on the states, and on the links.