Registration Apparatus Reducing Network Update Calculation Period
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Solution Overview
Problem
The existing leaning data precision visualization system requires a lengthy calculation period when updating cohort data, which is inefficient due to the need to recalculate the entire network.
Innovation Solution
A registration apparatus that reduces the calculation period by registering a new node to the network based on similarity relationships among a subset of feature vectors, rather than regenerating the entire network, thereby minimizing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire network is regenerated when updating cohort data, then the network structure remains complete and accurate, but the calculation period becomes excessively long
Solution Approach 1:
The patent segments the network update process into two parts: (1) maintaining the existing network structure with first nodes and first edges, and (2) selectively adding second nodes and second edges based on new cohort data. This segmentation allows the system to update only the necessary portions of the network rather than regenerating the entire structure, thereby reducing calculation time while preserving network accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing feature vectors for cohort data in advance. When updating the network, the system utilizes these pre-computed feature vectors to quickly determine node positions and edge relationships, avoiding the need to recalculate all network parameters from scratch and thus significantly reducing the calculation period.
2Quantity of substance
If all first feature vectors are processed for network updates, then comprehensive data coverage is achieved, but computational load increases significantly
Solution Approach 1:
The patent extracts and utilizes only the essential feature vectors needed for network updates. Instead of processing all first feature vectors, the system selectively processes second feature vectors that represent new cohort data, extracting only the necessary information for maintaining network accuracy while minimizing computational load.
Solution Approach 2:
The patent applies partial action by processing a subset of feature vectors rather than the complete set. The system processes second feature vectors for new nodes and updates only the necessary second edges connecting these nodes to existing first nodes, achieving sufficient data coverage without the excessive computational burden of processing all first feature vectors.
Data Source
AI summary
A registration apparatus is configured to access a network formed of a set of first nodes and first edges, the first nodes each representing a first feature vector including a plurality of elements, the first edges each coupling two first nodes representing two first feature vectors to each other based on two first feature vectors, a processor in the registration apparatus is configured to execute: obtaining processing of obtaining a second feature vector; and registration processing of registering a second node representing the second feature vector to the network based on a similarity relationship among third feature vectors in a set of third feature vectors included in the set of first feature vectors, and coupling the second node and a third node representing the third feature vector to each other with a second edge, the number of third feature vectors being smaller than the number of first feature vectors.