Record Comparison Using Mean Information Without Covariance
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Solution Overview
Problem
Customer data processing systems lack the ability to efficiently identify significant records, such as customer journeys, for analysis purposes, as they often rely on covariance calculations that are computationally intensive and not independent.
Innovation Solution
A method and system that process a subset of customer records to generate mean information without performing covariance calculations, allowing for comparison of records independent of covariance, and identifying significant records by displaying results that indicate deviations from the subset, which can be used to adjust business rules and improve customer journeys.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If covariance calculation is used to compare customer records, then measurement precision is improved, but device complexity and processing time increase
Solution Approach 1:
The patent extracts and removes the covariance calculation step from the record comparison process. Instead of calculating covariance between customer records, the system uses mean information (averages of event attributes) to represent each record and compares these simplified representations directly. This extraction of the complex covariance operation while retaining the essential comparison functionality resolves the contradiction between measurement precision and device complexity.
2Measurement precision
If covariance calculation is performed on all records, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent segments the comparison process into two stages: first, pre-compute mean information for each record independently; second, use these pre-computed means for rapid comparison. This segmentation allows the system to avoid repeated covariance calculations while maintaining comparison accuracy, thereby improving productivity without sacrificing measurement precision.
Solution Approach 2:
The patent performs preliminary computation of mean information for each customer record before the actual comparison takes place. By pre-calculating these representative values and storing them, the system eliminates the need for repeated complex covariance calculations during comparison operations, significantly improving processing speed and productivity while maintaining accurate record comparison.
3Measurement precision
If covariance calculation is used for record comparison, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary computation of mean information for each customer record before the actual comparison takes place. By pre-calculating these representative values and storing them, the system eliminates the need for repeated complex covariance calculations during comparison operations, significantly improving processing speed and productivity while maintaining accurate record comparison.
Data Source
AI summary
A system, method, and computer program product are provided for identifying significant records (e.g. journeys, etc.). In use, a plurality of records including a plurality of events are stored. Further, an indication is received regarding at least one subset of the records. Thereafter, at least a portion of the events of the at least one subset of the records are processed to generate mean information such that the records of the at least one subset may be compared, utilizing the mean information. This may be accomplished while avoiding, at least in part, a covariance calculation, or in such a manner that the comparison is independent of such covariance calculation. A result of the comparison may be displayed.