Chatbot Authentication Using Open-Ended AI Identity Verification
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Solution Overview
Problem
Traditional user authentication methods, such as knowledge-based authentication (KBA), are vulnerable to fraud and lack accuracy in determining user identity, as users may forget answers or provide irrelevant responses, and multiple-choice quizzes can be easily guessed, exposing personal information.
Innovation Solution
A chatbot authentication system using a large language model (LLM) and natural language processing (NLP) to interpret open-ended answers, generating iterative questions, and evaluating user responses for accuracy and completeness, with additional security measures against fraudulent activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional knowledge-based authentication (KBA) with multiple-choice quizzes is used, then the authentication process is simple to implement, but the security is weak because answers can be easily guessed or researched from public records
Solution Approach 1:
The patent transforms the authentication approach from fixed multiple-choice questions with predetermined answers to open-ended questions where the system dynamically evaluates the semantic meaning and factual accuracy of user responses using AI language models. This parameter change in question type and evaluation method significantly increases security while maintaining ease of implementation.
Solution Approach 2:
The patent replaces the traditional mechanical multiple-choice quiz system with an AI-powered semantic analysis system that uses natural language processing and large language models to evaluate open-ended responses. This substitution enables more sophisticated security verification while keeping the user interface simple and easy to implement.
2Ease of operation
If traditional KBA requires exact text matching of security questions, then the authentication is straightforward, but users may fail authentication due to forgetting specific formatting, spelling, or punctuation
Solution Approach 1:
The patent changes the evaluation parameter from exact text matching (case-sensitive, punctuation-sensitive) to semantic meaning evaluation. The AI language model understands the intent and factual accuracy of responses without being constrained by specific formatting, spelling, or punctuation, making authentication more forgiving for users while maintaining accuracy.
Solution Approach 2:
The system provides iterative feedback through follow-up questions when initial responses are incomplete or ambiguous, allowing users to clarify their answers rather than immediately failing authentication. This feedback mechanism improves both ease of operation and authentication accuracy.
3Reliability
If open-ended questions with AI evaluation are used, then security and authentication accuracy are improved, but the system complexity increases
Solution Approach 1:
The patent introduces an AI language model as an intermediary between the user's open-ended response and the authentication decision. This intermediary handles the complex semantic analysis and factual verification, allowing the overall system to achieve high authentication accuracy without requiring complex rule-based logic in the authentication system itself.
Solution Approach 2:
The AI language model serves multiple functions: understanding semantic meaning, verifying factual accuracy, generating follow-up questions, and making authentication decisions. This multi-functionality reduces the need for separate specialized components, thereby managing system complexity while maintaining high authentication accuracy.
4Loss of time
If multiple-choice KBA questions are used, then the authentication process is quick, but fraudsters can compromise security by displaying correct answers or researching public records
Solution Approach 1:
The patent changes the question format from multiple-choice to open-ended, which fundamentally alters the fraud landscape. While open-ended questions take slightly longer to answer, they cannot be compromised by displaying answers or simple research, as the system evaluates semantic meaning and factual accuracy rather than matching predetermined options.
Solution Approach 2:
The patent replaces the vulnerable multiple-choice mechanism with an AI-powered semantic evaluation system that processes open-ended responses. This substitution maintains reasonable authentication speed while eliminating the specific fraud vulnerabilities associated with displaying or researching multiple-choice answers.
Data Source
AI summary
Systems and methods for a chatbot authentication system and process that may utilize a large language model (LLM) and state of the art generative natural language processing (NLP) to generate and/or interpret user answers questions generated based on comprehensive known data about the user. The questions can include skills-based and/or knowledge-based queries which may be open ended questions. The user answers provided may be evaluated for accuracy and completeness. Iterative and/or follow-up questions may be generated and posed to allow the user to provide clarifications or additional details. By virtue of the LLM and NLP, the systems and methods disclosed herein may understand the semantical meaning of the answers provided by the user, which can enable a more accurate, user friendly and secure authentication process.