AI Chatbot Request Processing with Dynamic Scoring
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Solution Overview
Problem
Conventional data processing approaches in enterprise settings are resource-intensive and error-prone, struggling to scale with increasing user requests.
Innovation Solution
The implementation of artificial intelligence techniques, including chatbots and knowledge bases, to automatically process user requests, generate answers, and calculate scores for determining when to transition from chatbot to human intervention, thereby overcoming resource and error issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data processing approaches are used to handle user requests, then the processing can be performed with traditional methods, but the processes become resource-intensive and error-prone as the number of requests increases
Solution Approach 1:
The patent replaces conventional mechanical data processing systems with an AI-based chatbot system that uses natural language processing, machine learning, and automated reasoning to handle user requests. This substitution enables the system to process requests with higher accuracy while reducing resource consumption through intelligent automation and optimized processing pathways.
Solution Approach 2:
The system dynamically adjusts processing parameters based on request complexity, user history, and system load. By changing parameters such as processing depth, model selection, and resource allocation in real-time, the system maintains high accuracy for complex requests while using resource-efficient processing for simple queries, thereby resolving the contradiction between reliability and resource consumption.
2Productivity
If conventional data processing approaches are used to handle user requests, then traditional processing methods can be maintained, but the systems struggle to scale as the number of requests increases
Solution Approach 1:
The chatbot system employs dynamic scaling capabilities where computational resources, model complexity, and processing capacity are automatically adjusted based on real-time demand. The system can scale from handling simple queries with lightweight models to processing complex multi-step requests with advanced AI techniques, enabling productive scaling without proportionally increasing system complexity.
Solution Approach 2:
The patent segments the request processing system into modular components including natural language understanding, intent recognition, knowledge base querying, and response generation. Each module can be independently optimized and scaled, allowing the system to increase productivity by activating only the necessary segments for each request type rather than processing all requests through the complete pipeline.
3Extent of automation
If AI techniques are used to automatically process user requests, then resource consumption is reduced and scalability is improved, but the system must determine when to transition from chatbot to human intervention
Solution Approach 1:
The system implements continuous feedback loops where the chatbot monitors its own performance metrics, user satisfaction signals, and request complexity indicators. Based on this feedback, the system automatically adjusts the level of automation and determines when human intervention is necessary. The feedback mechanism provides real-time information about processing quality and resource usage, enabling intelligent transition decisions without adding significant complexity to the automated decision-making process.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automatically processing user request data using artificial intelligence techniques are provided herein. An example computer-implemented method includes generating, using a chatbot during a communication session with a user, answers to user requests by processing the user requests using a first set of artificial intelligence techniques associated with the chatbot; generating knowledge base-related predictions associated with the user requests by processing the user requests using a second set of artificial intelligence techniques associated with a knowledge base; calculating at least one score associated with at least a portion of the answers and the user requests based on qualifying values computed in connection with at least one of generating the answers and generating the knowledge base-related predictions; and performing one or more automated actions based on the at least one calculated score.