Entity Performance Analysis via Data Fusion System
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Solution Overview
Problem
Current methods for evaluating entity performance, such as retail merchant locations, do not consider sales made by competitors or demographic features of the areas, leading to inaccurate performance measurements.
Innovation Solution
A system that analyzes entity performance by integrating real-time data fusion of credit/debit card transactions, merchant data, and geographic information to provide comprehensive performance metrics, including competitor sales and demographic factors, using a data fusion system that transforms various data sources into an object model for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional entity performance evaluation methods are used, then the evaluation process is simple, but the measurement accuracy is poor because competitor sales and demographic factors are not considered
Solution Approach 1:
The patent segments the performance evaluation system into distinct functional modules: data acquisition module that collects transaction data from multiple sources, data processing module that cleans and standardizes data, analysis module that computes performance metrics, and reporting module that presents results. This segmentation allows the complex system to be managed through independent, specialized components that can be developed and maintained separately.
Solution Approach 2:
The patent introduces a data fusion system as an intermediary layer between raw data sources and performance evaluation. This intermediary integrates transaction data, competitor information, and demographic data, transforming them into a unified format that can be analyzed for performance metrics. The data fusion system acts as a mediator that reconciles multiple data sources with different formats and quality levels.
2Loss of information
If real-time data fusion of multiple sources is implemented, then comprehensive performance metrics including competitor sales and demographics are obtained, but the data processing complexity increases
Solution Approach 1:
The patent merges multiple data sources (transaction data, competitor sales data, demographic data) into a unified data structure that preserves information from all sources while enabling comprehensive analysis. The merging process integrates heterogeneous data types into a consistent format, allowing the system to maintain complete information across all data sources without creating separate processing pipelines for each source.
Solution Approach 2:
The patent creates a universal data processing framework that handles multiple data types and sources through a single integrated system. The data fusion system is designed to process transaction data, competitor information, and demographic data using the same processing logic and data structures, making the system multi-functional and reducing overall complexity compared to having separate specialized systems for each data type.
3Measurement precision
If comprehensive data analysis including competitor and demographic factors is performed, then performance evaluation accuracy improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing data from multiple sources before the actual performance evaluation. Transaction data, competitor information, and demographic data are cleaned, validated, and structured in advance, creating ready-to-analyze datasets. This preliminary preparation reduces the time required for the actual performance computation when evaluation is needed.
Solution Approach 2:
The patent implements continuous data processing where the system continuously ingests and processes transaction data, competitor sales data, and demographic information in real-time or near-real-time. Rather than batch processing all data at once, the system maintains continuous operation, constantly updating performance metrics as new data arrives, which reduces overall processing time and enables timely decision-making.
Data Source
AI summary
Approaches for analyzing entity performance are disclosed. A first set of data and a second set of data can be stored in a data structure. This data can be associated with a plurality of interactions, and can be modified to include additional interactions. These interactions can involve consuming entities and provisioning entities. The modified data structure can be queried to retrieve information associated with one or more entities. After information is retrieved, it can be provided to a user.