Service Center Engine for Intent Identification and Security
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Solution Overview
Problem
Conventional contact center systems often fail to accurately identify and address user intents during service calls, leading to negative experiences such as unauthorized account changes, bill shock, and reduced customer loyalty.
Innovation Solution
A computerized framework that includes a service center engine with modules for identification, analysis, determination, and output, which analyzes user interactions with service agents using AI/ML techniques to determine user intents and agent responsiveness, and scores these interactions to improve service call management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional contact center systems are used to handle service calls, then basic service operations can be performed, but user intents are not accurately identified and security concerns are not detected
Solution Approach 1:
An AI/ML-based service center engine is introduced as an intermediary layer between users and service agents. This engine analyzes service call data, identifies user intents, detects security concerns, and provides recommendations to agents, thereby improving intent identification accuracy without requiring complete system redesign.
Solution Approach 2:
The patent replaces manual intent identification and security monitoring performed by human supervisors with automated AI/ML algorithms. The service center engine processes service call data, transcripts, and user behavior patterns to automatically identify intents and detect anomalies, reducing the need for complex human oversight mechanisms.
2Reliability
If service agents have full authority to handle service requests, then service operations can proceed efficiently, but unauthorized account changes and fraudulent activities occur
Solution Approach 1:
The service center engine implements real-time feedback mechanisms by monitoring service calls and providing instant alerts to agents and supervisors when security concerns are detected. The system analyzes user behavior patterns, transaction histories, and conversation content to identify potential fraud and provides immediate feedback to prevent unauthorized actions.
Solution Approach 2:
The system performs preliminary security assessments by analyzing user intent and transaction requests before they are executed. The service center engine evaluates risk factors, verifies user authorization, and checks for anomalies in advance, allowing legitimate transactions to proceed efficiently while blocking potentially fraudulent activities.
3Reliability
If AI/ML analysis is implemented to identify user intents and detect security concerns, then service quality and security improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The service center engine is divided into modular functional components including data collection modules, AI/ML analysis modules, security detection modules, and reporting modules. Each module performs a specific function and can be independently configured, trained, and maintained, reducing overall system implementation complexity while maintaining comprehensive service quality.
Data Source
AI summary
Disclosed are systems and methods for a computerized framework enacted by service contact centers that provides a proactive and adaptive response system that accurately identifies security and/or legal concerns of service requests, and enables artificial intelligence/machine learning (AI/ML)-based mechanisms for dynamically addressing the underlying technical and/or service related concerns of such service requests. The disclosed framework can computationally determine how effective service agents have been, and are currently being in curating solutions/responses to each customer service call, which can enable modified functionality for the customer as well as curated services based on how sufficiently handled the service call was responded to by the agent. The disclosed systems and methods provide a generative service call experience that can improve agent performance while reducing the strain on user experience, both during and/or after service calls.