Dynamic Digital Data Verification via Machine Learning
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Solution Overview
Problem
Current data retrieval technologies lack dynamic verification of data accuracy, leading to the presentation of outdated information and inadequate accuracy ratings, as they fail to perform real-time verification without human interference, resulting in increased inaccuracy.
Innovation Solution
An automated program that dynamically selects and verifies digital data from external sources by converting data to a uniform syntax, analyzing indicative markers, generating machine learning models, and automatically validating accuracy using a predetermined threshold, while continuously learning from user interactions to improve data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current data retrieval technologies are used, then data can be retrieved from external sources, but the data accuracy and up-to-date status deteriorate due to lack of dynamic verification
Solution Approach 1:
The system performs self-verification by automatically querying multiple external data sources, comparing retrieved data against established criteria and indicative markers, and dynamically updating accuracy values without human intervention. The program autonomously identifies outdated information and corrects it by selecting data from sources with higher accuracy ratings.
Solution Approach 2:
The system implements continuous feedback loops where retrieved data is verified against multiple external sources, accuracy values are dynamically updated based on comparative analysis, and the system learns from user interactions to improve future data selection. This feedback mechanism ensures data remains accurate and up-to-date through iterative verification.
2Measurement precision
If manual verification of digital data is performed, then data accuracy can be assessed, but time consumption and operational complexity increase
Solution Approach 1:
The system replaces manual verification processes with automated computer-programmed operations that query external data sources, analyze indicative markers, and assess accuracy values through algorithmic comparisons. This substitution eliminates human intervention while maintaining high measurement precision through systematic data validation.
Solution Approach 2:
The system performs preliminary verification by pre-querying multiple external data sources and pre-assessing accuracy values before presenting data to users. Indicative markers are analyzed in advance, and the system proactively identifies and corrects outdated information before it reaches the user, eliminating the need for subsequent manual verification.
3Reliability
If multiple external data sources are queried for verification, then data reliability improves, but system complexity and processing time increase
Solution Approach 1:
The system employs a universal verification framework that can query multiple types of external data sources (databases, APIs, web sources) through a single integrated program. The same verification logic and accuracy assessment mechanisms are applied across diverse data sources, reducing system complexity through standardized multi-functional processing.
Solution Approach 2:
The verification process is segmented into distinct modular components: data retrieval from external sources, conversion to uniform syntax, analysis of indicative markers, accuracy value assessment, and dynamic updating. Each segment is independently processed and can be executed in parallel, managing complexity through systematic division of the verification workflow.
Data Source
AI summary
Embodiments of the present invention provide a computer system a computer program product, and a method that comprises converting the retrieved data to a uniform syntax for data assessment; performing a query on a plurality of external data sources for additional information associated with the converted data; analyzing a plurality of indicative markers associated with the retrieved data and the additional information; generating a plurality of machine learning models associated with the converted data based on the analysis of each indicative markers within the plurality of indicative markers; dynamically selecting at least one generated machine learning model within the plurality of generated machine learning models associated with the retrieved data based on an analysis of the plurality of indicative markers associated with the retrieved data and the additional information; and automatically verifying an accuracy value associated with the at least one selected generated machine learning model.