Online Information Verification System Using Author Identity Normalization
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Solution Overview
Problem
The challenge of acquiring and verifying credible online information is hindered by the prevalence of fake and unverifiable data, with existing search engines and commerce sites struggling to filter noise and establish credibility, leading to unreliable reviews and ratings that can mislead users.
Innovation Solution
A system and method that acquire and verify information by normalizing data from trusted sources, determining author identity, classifying data based on metadata, and storing it in a structured format, allowing users to access credible reviews and ratings from trusted networks, and utilizing a smart card system to capture and share transaction details and reviews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is acquired from multiple online sources, then the quantity of information increases, but the reliability of the information decreases due to fake and unverifiable data
Solution Approach 1:
The patent introduces an intermediary verification system that acts as a mediator between data sources and users. This system includes verification modules that authenticate data sources, verify author identities, and validate data authenticity before presenting information to users, thereby maintaining reliability while handling multiple data sources
Solution Approach 2:
The patent replaces traditional manual verification mechanisms with automated electronic verification systems. These systems use digital authentication methods, metadata analysis, and algorithmic verification processes to automatically assess data reliability, enabling the system to handle large quantities of information from multiple sources without compromising verification quality
2Reliability
If existing search engines and commerce sites attempt to filter noise, then the reliability of information improves, but the device complexity increases
Solution Approach 1:
The patent divides the verification system into distinct modular components including data acquisition modules, verification modules, authentication modules, and presentation modules. Each module performs a specific function in the data verification process, making the overall complex system manageable through functional segmentation and independent development of each component
Solution Approach 2:
The patent implements preliminary verification actions where data sources and author identities are authenticated before data is accepted into the system. Metadata is extracted and verified in advance, and authentication credentials are validated beforehand, preventing unreliable data from entering the system in the first place rather than filtering it later
3Ease of operation
If user-submitted reviews are accepted without verification, then the ease of operation improves, but the reliability of reviews decreases
Solution Approach 1:
The patent enables users to self-verify their identities and authenticate their review submissions through automated credential verification systems. Users provide authentication credentials that are automatically verified by the system, allowing them to submit verified reviews without manual intervention while maintaining both ease of operation and reliability
Solution Approach 2:
The patent implements feedback mechanisms where the verification system provides immediate confirmation to users about whether their review has been successfully verified and accepted. This real-time feedback loop informs users of the verification status, maintaining ease of operation while ensuring only verified reviews are published
Data Source
AI summary
A system and method of acquiring and verifying information is provided. The system comprises a processor, and a memory comprising a sequence of instructions which when executed by the processor configure the processor to perform the method. The method comprises acquiring data from a data source associated with an author of the data, normalizing the acquired data, determining, by the processor, an identity of the author of the data, classifying the normalized data based on the identity and acquired metadata, and storing in a memory the normalized data. Normalizing the acquired data comprises parsing the acquired data for meaningful information, extracting metadata from the acquired data, and mapping the parsed information to internal data structures.