LLM Customer Service Assistant via Unified Interface
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Solution Overview
Problem
Current customer service automation systems are complex and require significant training due to technical complexity, leading to data errors and a high barrier for new users, especially when navigating multiple applications for tasks like service order management and key performance indicator monitoring.
Innovation Solution
A conversational user interface employing a large language model that recognizes intents in natural language input, allowing users to perform customer service actions seamlessly across multiple applications, with the model trained to provide API payloads for database management and support multiple languages, thereby simplifying interactions and concealing implementation complexities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple applications are used to implement customer service functions, then functionality is available, but system complexity increases and requires significant training
Solution Approach 1:
The patent combines multiple separate customer service applications into a single unified interface that leverages large language model technology. This unified interface consolidates service order management, KPI monitoring, and other customer service functions that previously required navigating multiple applications, thereby reducing system complexity while maintaining full functionality.
Solution Approach 2:
The large language model-based interface serves as a universal entry point that can handle diverse customer service tasks including service order creation, modification, cancellation, KPI queries, and general inquiries. This single interface replaces multiple specialized applications, allowing users to perform various functions without needing to learn different application interfaces.
2Adaptability or versatility
If multiple applications are used for customer service tasks, then comprehensive service is provided, but ease of operation deteriorates due to navigation complexity
Solution Approach 1:
The large language model acts as an intermediary layer between the user and the complex backend systems. Users interact with the system through natural language conversations rather than navigating multiple application interfaces. The LLM translates user intents into appropriate system actions, serving as a mediator that simplifies the user experience while maintaining access to comprehensive service functions.
3Extent of automation
If traditional automated systems are used, then automation is provided, but reliability decreases due to data errors from transitioning between applications
Solution Approach 1:
By merging multiple application interactions into a single unified LLM interface, the system eliminates the data errors that occur when transitioning between applications. The unified interface maintains consistent context and data throughout the interaction, preventing the data loss and errors that previously occurred during application switching while preserving full automation capabilities.
4Adaptability or versatility
If multiple applications are used, then various customer service functions are available, but loss of time increases due to navigation between applications
Solution Approach 1:
The patent extracts the time-consuming navigation step from the customer service process by implementing a unified LLM-based interface. Users can directly invoke any customer service function through natural language without needing to navigate through multiple applications. This extraction of the navigation element eliminates the time loss associated with moving between applications while maintaining access to all service functions.
Data Source
AI summary
A large language model can be used to implement a service assistant. Natural language commands can be sent to the large language model, which identifies intents and responds with actions and API payloads. The command can then be implemented by an appropriate API call. The assistant can support actions that span a plurality of applications. A wide variety of human languages can be supported, and the large language model can maintain context between commands. Useful functionality such as prompting for missing parameters and the like can be supported.