Semantic Embedding Matrix for Relationship Mapping Accuracy
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Solution Overview
Problem
Existing prediction models for relationship mapping in data structures, such as those used in task assignment and workforce management, often suffer from inaccuracies due to missing or undefined relative strengths, leading to inefficient use of computing and memory resources, especially when dealing with large datasets.
Innovation Solution
A prediction platform that receives two matrices representing relationships between parameters, estimates associations using a pseudoinversion network and multiple regression procedure, generates a semantic embeddings matrix, determines semantic similarity, calculates consistency errors, and uses a regression-based learning model to generate a third matrix, thereby improving the accuracy of relationship mapping and reducing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing prediction models are used for relationship mapping, then the system can process data structures, but the accuracy of relationship mapping deteriorates due to missing or undefined relative strengths
Solution Approach 1:
The patent introduces semantic embeddings as an intermediary layer between the input matrices and the relationship mapping output. By converting parameters into semantic embeddings and computing similarities in this intermediate semantic space, the system can infer relationships even when direct relationship data is missing or undefined, thereby improving mapping accuracy without sacrificing reliability
Solution Approach 2:
The patent transforms the relationship mapping problem from direct matrix multiplication to a multi-step process involving semantic embedding transformation. By changing the parameter representation from raw values to semantic embeddings, and computing relationships through similarity metrics in the embedded space, the system achieves more accurate and reliable relationship mapping
2Productivity
If traditional matrix operations are used to complete incomplete datasets, then the processing is simple, but computational and memory resources are excessively consumed when dealing with large datasets
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing semantic embeddings for parameters before the actual relationship mapping task. This preprocessing step converts raw parameter values into compact semantic representations that can be efficiently reused across multiple queries, significantly reducing computational resources needed during runtime while improving productivity
Solution Approach 2:
The patent creates compact copies of parameter information in the form of semantic embeddings. Instead of repeatedly processing large raw datasets, the system works with compressed semantic representations that capture essential relationships, reducing memory usage and computational energy while maintaining mapping accuracy
Data Source
AI summary
A device may determine an association between a second set of parameters and a third set of parameters using a pseudoinversion network and a multiple regression procedure. The device may determine semantic embeddings based on a set of semantic descriptions of the second set of parameters. The device may determine a semantic similarity between parameters of the second set of parameters based on the semantic embeddings. The device may determine a consistency error based on the semantic similarity. The device may generate, using a regression-based learning model technique, a matrix representing an association between the second set of parameters and the third set of parameters based on the association and the consistency error. The device may perform an action based on the matrix.