Document Authenticity Scoring Using Weighted Attribute Verification
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Solution Overview
Problem
Existing artificial intelligence solutions for identifying fraudulent documents face challenges due to insufficient training data and the need for specialized resources and time frames, making it difficult to distinguish between legitimate and illegitimate documents in real-time.
Innovation Solution
A system that uses weighted analysis of categorical attributes, leveraging both public and private data sources to determine authenticity by assigning higher importance to privately verified attributes, and performs image analysis for real-time document verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If artificial intelligence solutions are used to identify fraudulent documents, then detection accuracy may be improved, but the process requires large amounts of high-quality training data which is time-consuming and complex to obtain
Solution Approach 1:
The system performs preliminary actions by pre-establishing a database of legitimate document attributes and their associated weights before actual fraud detection occurs. This allows the AI model to operate without requiring extensive real-time training data, as the foundational knowledge is prepared in advance.
Solution Approach 2:
The patent introduces an intermediary verification process where a verification service compares document attributes against established legitimate patterns. This intermediary layer enables accurate detection without requiring the AI to process vast amounts of training data from scratch, as the verification service acts as a mediator with pre-computed reference data.
2Reliability
If artificial intelligence solutions are implemented for document verification, then detection capability is improved, but specialized knowledge and resources are required which limits availability and implementation speed
Solution Approach 1:
The system implements self-service by automatically extracting attributes from documents and comparing them against pre-established legitimate patterns without requiring specialized manual analysis. The verification service autonomously performs the comparison and generates assessments, eliminating the need for specialized human expertise in each verification case.
Solution Approach 2:
The patent uses copying by creating a digital representation of legitimate document attributes and their verification patterns. Instead of requiring specialized knowledge to analyze each document, the system copies the characteristics of legitimate documents into a database and uses these copies for automated comparison, simplifying the verification process.
3Measurement precision
If comprehensive attribute verification is performed to distinguish legitimate and illegitimate elements, then detection accuracy is improved, but the time required for verification increases
Solution Approach 1:
The system applies local quality by assigning different weights to different attributes based on their importance in distinguishing legitimate from illegitimate documents. Not all attributes are treated equally; critical attributes receive higher weights and are verified more thoroughly, while less critical attributes receive lower weights, enabling efficient verification that maintains high detection accuracy.
Solution Approach 2:
The patent changes the parameter of verification by introducing weighted scores that dynamically adjust the importance of different attributes. This allows the system to optimize verification speed by focusing computational resources on high-weight attributes while quickly accepting low-weight attributes, achieving both high accuracy and fast verification.
Data Source
AI summary
Systems and methods are described herein for novel uses and/or improvements to artificial intelligence applications. As one example, systems and methods are described for the use of an artificial intelligence-based solution in identifying fraudulent documents and/or other content. In particular, the systems and methods adapt the artificial intelligence-based solution to overcome the technical problem of insufficient training data and/or solutions that are commensurate with the time frame and resources available.