Custom Score Generation System for Multi-Source Data Integration
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Solution Overview
Problem
Current data scoring systems are limited to single-dimensional assessments and lack comprehensiveness, as they rely on data from a single source, failing to provide holistic insights, and face challenges in data sharing and integration due to privacy issues and complex data collection processes, which hinders the growth of data services.
Innovation Solution
A custom score generation system that allows third-party data sources to merge data from multiple sources using a shared database, enabling the creation of custom scores through graphical interfaces for selecting and weighting data elements, defining scoring rules, and conditional statements, thereby simplifying the scoring process and expanding data services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple sources is integrated to create comprehensive scores, then the comprehensiveness and informational value of the score is improved, but the complexity of data collection, sharing, and integration increases
Solution Approach 1:
The patent introduces a score generation system as an intermediary platform that mediates between multiple data sources and end users. This system receives data from various sources, processes it through standardized algorithms, and generates comprehensive scores without requiring direct complex integration between data sources themselves. The system acts as a buffer that simplifies the integration complexity while maintaining comprehensive information aggregation.
Solution Approach 2:
The patent segments the score generation process into distinct modular components: data collection modules, data processing modules, scoring algorithm modules, and output generation modules. Each module handles specific tasks independently, allowing the system to manage complex multi-source data integration through standardized interfaces and protocols rather than requiring monolithic complex integration logic.
2Adaptability or versatility
If new data sources are added to expand service offerings, then the versatility and growth potential is improved, but the difficulty of data collection and verification increases
Solution Approach 1:
The patent implements a universal data interface framework that enables the system to accept and process data from diverse sources through standardized protocols. This multi-functional interface design allows new data sources to be integrated without creating custom verification procedures for each source, as the system employs general-purpose data validation and verification mechanisms that work across all data types and sources.
Solution Approach 2:
The system employs configurable parameters and metadata schemas that can be adjusted to accommodate different data sources and verification requirements. When new data sources are added, the system modifies its data collection and verification parameters rather than requiring fundamental changes to its core architecture, enabling flexible adaptation while maintaining consistent verification standards.
3Measurement precision
If complex algorithms are used to generate accurate scores from multiple data elements, then the measurement precision is improved, but the ease of operation and implementation is worsened
Solution Approach 1:
The patent implements self-service mechanisms where the score generation system automatically performs data validation, weighting optimization, and algorithm selection without requiring manual intervention. The system autonomously adjusts scoring parameters based on data quality metrics and automatically generates comprehensive scores, eliminating the need for operators to manually configure complex algorithms while maintaining high measurement precision.
Solution Approach 2:
The system performs preliminary actions by pre-processing data from multiple sources, pre-calculating weighted values, and pre-validating data quality before the actual score generation occurs. This preliminary preparation work is done automatically in the background, so when scores need to be generated, the complex computational work has already been partially completed, making the process appear simpler and faster to users.
Data Source
AI summary
Some embodiments provide a custom score generation system by offering third parties access to data from a shared database that they can merge with their own proprietary data for the purpose of defining and producing new data services. The system provides interfaces for selecting data elements from the shared database, attributing weights to the selected data elements, and defining scoring rules or parameters to automatically evaluate the significance of the data element values. Additionally, conditions may be specified to include different sets of the selected data elements, to attribute different weights to the different sets of the selected data elements, and to define different scoring rules according to which conditions are satisfied. The system then automatically formulates the algorithm to produce the custom score in conformance with the provided inputs and based on values for the selected data elements that are specific to different entities.