Blockchain Identity Verification for User-Generated Data Aggregation
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Solution Overview
Problem
Existing electronic transactional systems face challenges in ensuring the reliability, accuracy, and completeness of user-generated electronic data (UGED) that is not directly entered by the user, particularly when sourced from diverse and heterogeneous devices, leading to concerns about data authenticity and trustworthiness.
Innovation Solution
A blockchain-enabled system that includes a cloud gateway agent server, wearable devices, and a blockchain-based identity authorization device, which aggregates and verifies user-generated data through natural language processing, metadata analysis, and user verification, using a rules engine and machine learning algorithms to classify and validate data, ensuring data integrity and trustworthiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If user-generated electronic data is collected from diverse devices without direct user entry, then data quantity and sources are increased, but data reliability and accuracy deteriorate
Solution Approach 1:
The patent introduces an intermediary verification system consisting of a processing circuit and rules engine that acts as a mediator between diverse data sources and the electronic transactional system. This intermediary automatically verifies data from multiple devices by checking against predefined rules, metadata standards, and consistency criteria, thereby maintaining data reliability while accepting large quantities of data from heterogeneous sources without requiring direct user entry for each data point.
Solution Approach 2:
The system implements feedback mechanisms where the processing circuit continuously monitors incoming data from diverse devices, compares it against established rules and metadata schemas, and provides automatic verification feedback. This feedback loop enables the system to maintain high data reliability by rejecting or flagging inconsistent data while accepting valid data points, thus managing large data quantities from multiple sources effectively.
2Loss of information
If data is aggregated from multiple heterogeneous sources, then data completeness is improved, but system complexity increases
Solution Approach 1:
The patent applies homogeneity by standardizing data from heterogeneous sources through a common metadata schema and verification framework. The processing circuit normalizes data structures, formats, and validation criteria across all data sources, transforming diverse incoming data into a uniform format that can be processed consistently. This standardization approach enables the system to achieve data completeness from multiple sources while managing complexity through homogeneous processing rules.
Solution Approach 2:
The verification system is designed with universal functionality to handle multiple types of data from various heterogeneous sources through a single integrated processing circuit and rules engine. This multi-functional approach allows the same verification infrastructure to process different data formats, devices, and sources using common validation logic, thereby achieving data completeness without proportionally increasing system complexity.
3Productivity
If automatic data aggregation is implemented without user intervention, then productivity is improved, but data trustworthiness deteriorates
Solution Approach 1:
The system implements self-service through an automated verification process where the processing circuit independently validates incoming data against predefined rules, metadata standards, and consistency criteria without requiring user intervention. This self-verification mechanism maintains high productivity by automatically processing data from multiple sources while preserving trustworthiness through systematic rule-based validation, eliminating the need for manual verification while ensuring data quality.
Solution Approach 2:
The patent applies preliminary action by establishing verification rules, metadata schemas, and validation criteria before data aggregation begins. The processing circuit is pre-configured with trustworthiness criteria that automatically evaluate incoming data points. This preliminary setup enables high-speed automatic data aggregation while maintaining data trustworthiness, as the verification framework is already in place to assess each data point before it enters the electronic transactional system.
Data Source
AI summary
A system is provided for aggregating user generated electronic data associated with a user from a plurality of computing machines located separately without user intervention. Metadata associated with the user generated electronic data is stored in an electronic record repository database to perform natural language processing and metadata analysis of the user generated electronic data to identify user verified data and user unverified data. A data object including query statements and approval options is generated and presented on a remotely located display unit accessible by the user. An input against each of the plurality of query statements is received. The system updates the unverified data based on the received input. The user generated electronic data is then pushed into the electronic transactional system which may communicate electronic data messages among a plurality of computer stations.