Entity Matching Using Weighted Bidirectional Suitability Scores
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Interpreting large volumes of customer and service provider data to provide meaningful recommendations is complicated and time-consuming, hindering effective matching of entities for tailored services or service selection.
Innovation Solution
A computer-implemented method determines matching scores for candidate and target entities by considering bidirectional suitability measures, using entity attributes and contact attributes, and applying weights to optimize matching suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large volumes of customer and service provider data are interpreted to provide meaningful recommendations, then the quality of matching suggestions is improved, but the time and complexity required to process the data increases
Solution Approach 1:
The patent segments the matching process into distinct components: candidate entity identification, target entity identification, suitability measure determination for both entities, and matching score calculation. This segmentation allows each component to be optimized independently and processed efficiently, reducing overall processing time while maintaining suggestion quality
Solution Approach 2:
The patent transforms raw entity data into standardized parameters including entity attributes and contact attributes with assigned weights. By converting unstructured data into weighted parameters, the system enables efficient computational processing while preserving the nuanced information needed for high-quality matching recommendations
2Measurement precision
If comprehensive entity attributes and contact attributes are considered for matching, then the accuracy of matching suggestions is improved, but the computational complexity increases
Solution Approach 1:
The patent applies local quality by assigning different weights to different attributes based on their relevance to the matching objective. Not all attributes are treated equally; instead, each attribute receives a weight reflecting its local importance, allowing the system to focus computational resources on the most significant matching criteria while maintaining high accuracy
Solution Approach 2:
The patent implements a balanced approach by considering comprehensive attributes but applying selective weighting where many attributes have zero or minimal weights. This partial action approach processes all available data structures but focuses computational effort only on the most relevant attributes, reducing effective complexity while maintaining accuracy
3Measurement precision
If bidirectional suitability measures are calculated for both target and candidate entities, then the quality of matching recommendations is improved, but the processing time increases
Solution Approach 1:
The patent implements continuous useful action by calculating suitability measures for both target entities and candidate entities in an integrated, continuous process rather than separate discrete steps. This allows the system to maintain comprehensive evaluation while optimizing the flow of computations to reduce processing time
Solution Approach 2:
The patent merges the suitability measure calculations for target entities and candidate entities into a unified matching score computation. By combining these bidirectional evaluations into a single integrated scoring mechanism, the system achieves comprehensive matching quality without the overhead of completely separate processing streams
Data Source
AI summary
A computer-implemented method performs by an accounting system identifying a candidate entity for matching with target entities by identifying one or more contacts associated with the target entity, determining one or more contact attributes of the contacts and determining one or more target entity attributes associated with the target entity based on the contact attributes. A target suitability measure is determined based on desired target entity attributes of the candidate entity and target entity attributes associated with the target entity. A matching score is determined for the candidate entity and target entity pair as a function of the target suitability measure. Suggested target entities are determined for matching with the candidate entity based on the matching score, and the one or more suggested target entities are displayed. A selection of a suggested target entity is received, and to the suggested target entity, a notification identifying the candidate entity is sent.