Data Source Selection Using Quality Scores

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Solution Overview

Problem

Modern systems face challenges in selecting the most accurate and cost-effective data source from a plurality of data sources in a distributed system, due to data redundancy and inconsistencies across sources.

Innovation Solution

Assigning a data quality score to each data source based on computational and security metrics, as well as data recency, to determine the preferred data source for obtaining user data, thereby optimizing accuracy and cost.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If data is obtained from multiple data sources to ensure accuracy, then data reliability is improved, but computational cost and complexity increase

Engineering Contradiction:

Improvedata accuracyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the data source selection process by evaluating multiple data sources independently and assigning weights to different criteria (accuracy, recency, completeness, cost). This allows the system to handle complexity through structured segmentation rather than monolithic evaluation.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent changes parameters by introducing a scoring mechanism that transforms qualitative data source attributes into quantitative scores. This enables automated comparison and selection of data sources based on weighted criteria, resolving the contradiction between reliability and complexity.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If data is obtained from multiple data sources to ensure accuracy, then data reliability is improved, but computational cost increases

Engineering Contradiction:

Improvedata accuracyVSAvoidcomputational cost

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent introduces computational cost as a quantifiable parameter in the data source evaluation framework. By assigning weights to different criteria including cost, the system can optimize the balance between data reliability and computational expenditure through the scoring mechanism.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent applies partial action by selectively querying only the necessary data sources based on the scoring evaluation, rather than exhaustively accessing all available sources. This reduces computational cost while maintaining data accuracy through intelligent selection.

Inventive Principle:

Principle #16Partial or excessive action

3Reliability

If user data is manually entered to ensure accuracy, then data reliability is improved, but user burden and time consumption increase

Engineering Contradiction:

Improvedata accuracyVSAvoiduser time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by pre-evaluating and scoring data sources before data collection. This advance preparation enables the system to automatically select the most reliable data sources, eliminating the need for manual user input while maintaining data accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements self-service by enabling the system to automatically evaluate, select, and collect data from appropriate sources without user intervention. The scoring mechanism and automated selection process replace manual user entry, reducing time consumption while preserving data reliability.

Inventive Principle:

Principle #25Self-service

4Measurement precision

If the most accurate data source is selected, then data quality is improved, but security risk may increase

Engineering Contradiction:

Improvedata qualityVSAvoidsecurity risk

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent introduces security risk as a quantifiable parameter in the data source evaluation framework. By assigning weights to multiple criteria including security, the system can balance data quality and security risk through the comprehensive scoring mechanism, selecting sources that optimize both dimensions.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12326842B1System and methods for determining preferred data sources using data quality scores

Publication Date: 2025.06.10 WELLS FARGO BANK NA

AI summary

Systems, apparatuses, methods, and computer program products are disclosed for obtaining data. The data may be obtained from a variety of data sources. In order to determine the preferred data source for obtaining the data, data quality scores may be computed and assigned to each data source of interest for a given implementation. Each data quality score may take into account several criteria including the computing resources required to obtain the data, the financial cost of obtaining the data, the security risk of obtaining the data, etc. Data may be obtained from the preferred data source, presented to a user associated with the data for verification, and processed in order to provide a computer-implemented services to the user.