Machine Learning Action Ranking for Customer Incident Response
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Solution Overview
Problem
Existing systems require custom development and complex integration of external data sources to provide relevant data to service agents addressing customer incidents, complicating the resolution process.
Innovation Solution
A customer incident response system using a machine learning model to curate and rank candidate actions from various endpoints, ensuring uniform display and secure integration of external AI services, allowing service agents to easily access contextually relevant insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If custom development and complex integration of external data sources are implemented, then relevant data can be provided to service agents, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary system that sits between external data sources and service agents. This intermediary automatically retrieves, processes, and presents relevant data without requiring service agents to directly integrate with external systems. The intermediary handles authentication, data extraction, and formatting, thereby reducing the complexity burden from the overall system while maintaining data relevance.
Solution Approach 2:
The system segments the complex integration task into distinct modular components: external data source interfaces, data processing engines, and presentation layers. Each component handles specific functions independently, allowing the system to manage complexity through modular architecture while ensuring comprehensive data coverage for customer issues.
2Adaptability or versatility
If multiple external data sources are integrated, then comprehensive information is available, but integration complexity increases
Solution Approach 1:
The patent implements a universal interface layer that can connect to multiple different external data sources using standardized protocols. This universal interface handles various data formats and authentication methods through a single unified system, allowing comprehensive information gathering from diverse sources without proportionally increasing integration complexity. The interface adapts to different sources while maintaining consistent internal processing.
3Adaptability or versatility
If custom communication code and service authentication code are developed, then external systems can be accessed, but development time and resources increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring authentication mechanisms and communication protocols when external data sources are initially connected. Authentication credentials, API keys, and communication templates are established in advance and stored securely. This preliminary setup eliminates the need for repeated custom code development when accessing these external systems, significantly reducing development time for subsequent integrations.
Data Source
AI summary
Techniques for providing candidate actions to a service agent based on a customer incident and associated attributes are disclosed. In one or more embodiments, a customer incident response system allows a customer support team to leverage a data ecosystem available to provide service agents with contextually relevant insights into a current data context that describes the customer incident. The system allows an administrator to configure connections to endpoints for external and/or third-party services, including artificial intelligence (AI), machine learning, static content, temporally based content, and rules-based content. Once configured, the system displays a series of insight cards near an incident workspace, where each insight card includes an action that the service agent may execute to attempt to resolve the customer incident. The system allows for external AI engines to generate insights and potential next actions to address the customer incident while enjoying a simplified setup.