Interactive Query Interfaces for Adaptive Responses to Complex Questions
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Solution Overview
Problem
Complex products and services are overwhelming for consumers, and existing computer-generated bots struggle to provide nuanced responses due to limited understanding of varying questions, leading to inefficiencies in query resolution.
Innovation Solution
An interactive query interface using a machine learning model and natural language processing system to generate personalized recommendations, enabling dynamic and configurable conversations with consumers, leveraging historical conversational data to improve query resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional computer-generated bots are used to answer consumer questions, then resource consumption is reduced, but the ability to understand varying questions and provide nuanced responses deteriorates
Solution Approach 1:
The system transforms the bot's processing capabilities by adjusting parameters of the machine learning model, allowing it to dynamically adapt its understanding and response generation based on the complexity and nuance of consumer questions about complex products and services
Solution Approach 2:
The machine learning model enables the bot to autonomously learn from historical conversational data and improve its responses without human intervention, allowing it to self-adjust its understanding capabilities while maintaining resource efficiency
2Reliability
If staff resources are increased to provide responsive customer service, then query resolution quality improves, but operational cost increases
Solution Approach 1:
The system creates a virtual copy of human staff expertise through the machine learning model, which can handle multiple consumer queries simultaneously with high quality resolution, eliminating the need to proportionally increase human staff resources
Solution Approach 2:
The machine learning-powered bot serves multiple functions including answering product questions, providing service information, and handling various consumer inquiries across different product lines, replacing the need for specialized staff for each function
3Device complexity
If predefined answer bots are used to reduce complexity, then device complexity is reduced, but the ability to provide relevant information according to query nuances deteriorates
Solution Approach 1:
The system transitions from static predefined answers to dynamic response generation where the machine learning model adapts its outputs based on the specific nuances of each consumer query, maintaining information relevance while managing complexity through automated processing
Data Source
AI summary
Implementations claimed and described herein provide systems and methods for responding to a query associated with a product or service. The systems and methods use a machine learning model to generate a recommendation and a user interface. The recommendation is transmitted to a user device for display via the user interface.