Cognitive Engine Agent for Automated Scam Baiting
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Solution Overview
Problem
Current technologies lack effective solutions to counteract scams targeting individuals, as antivirus programs and other security software are ineffective against human-targeted scams, and existing deterrents like scam baiting require significant human interaction and are time-consuming, limiting their scalability.
Innovation Solution
A computer-implemented method using a cognitive engine agent and messaging server to monitor and respond to scam messages with intelligent, interactive natural language, mimicking human conversation to deter scammers and waste their time, while gathering information for authorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If scam baiting is used to counteract scams, then the effectiveness in deterring scammers improves, but the time consumption and human interaction requirements worsen
Solution Approach 1:
The system enables automated scam baiting where the cognitive engine agent independently monitors incoming messages, identifies scam messages, and engages scammers in conversation without requiring human intervention. This self-service approach maintains the effectiveness of scam baiting while eliminating the time consumption and human resource requirements.
Solution Approach 2:
The patent replaces the mechanical human interaction process with an automated cognitive system. The cognitive engine agent uses natural language processing and understanding to simulate human-like conversations with scammers, substituting the manual scam baiting process with an automated intelligent system that operates continuously without time constraints.
2Reliability
If manual scam baiting is performed, then scam deterrence effectiveness improves, but scalability worsens due to limited human resources
Solution Approach 1:
The automated cognitive engine agent performs scam baiting operations independently without requiring human operators, enabling the system to scale to handle multiple scam messages simultaneously across different channels and platforms, thus improving productivity and scalability while maintaining deterrence effectiveness.
Solution Approach 2:
The cognitive engine agent is designed with universal capabilities to handle various types of scam messages across different communication platforms and languages. This multi-functional design allows a single automated system to replace multiple human scam baiters, significantly improving scalability and productivity.
3Productivity
If automated systems are used for scam detection, then productivity improves, but the ability to handle human-targeted scams worsens due to lack of human interaction
Solution Approach 1:
The patent employs a cognitive engine agent with advanced natural language understanding capabilities that replaces traditional automated detection systems. This cognitive system can analyze contextual nuances, emotional tones, and conversational patterns in scam messages, maintaining effectiveness against human-targeted scams while achieving automated productivity.
Solution Approach 2:
The cognitive engine agent acts as an intermediary between automated systems and human-targeted scams. It bridges the gap by using intelligent natural language processing to understand and respond to scammers in a human-like manner, thereby maintaining the reliability needed for human-targeted scam detection while achieving automated efficiency.
Data Source
AI summary
A computer-implemented method for deterring scams including: monitoring by a cognitive engine agent incoming messages for scam messages; receiving by a messaging server an incoming message having a sender of the incoming message; identifying by the cognitive engine agent the incoming message as a scam message; and replying by the cognitive engine agent in cooperation with the message server to the scam message by initiating a message conversation with the sender of the scam message, the message conversation including one or more reply messages to the sender of the scam message replying to the scam message and any subsequent scam messages from the sender of the scam message with each reply message being an intelligent, interactive message using natural unscripted language to appear as if the one or more reply messages was written by a human and is responsive to a content of a scam embodied in the scam message.