Virtual Dialog Context Collection via Dynamic NLP Selection
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Solution Overview
Problem
Existing virtual dialog systems, such as chatbots, face challenges in efficiently and accurately collecting context data for effective problem diagnosis, leading to inefficient interactions and poor user experience.
Innovation Solution
A system comprising an AI platform with tools like a collection manager, director, selection manager, and execution manager, which dynamically collect context data using natural language processing (NLP) and leverage context models to identify appropriate collection mechanisms, optimizing the context collection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional context collection methods are used in virtual dialog systems, then the system can collect some context data, but the collection process is inefficient and inaccurate, leading to poor problem diagnosis
Solution Approach 1:
The system dynamically adapts context collection strategies based on dialog state and problem type. The context collector adjusts which context entities to gather and which collection mechanisms to use in real-time during the dialog, transforming static collection processes into dynamic, responsive operations that improve both accuracy and efficiency
Solution Approach 2:
The system changes parameters of context collection by identifying specific context entities (such as problem type, severity, components involved) and adjusting collection mechanisms based on these parameters. This allows the system to optimize what data is collected and how it is collected according to the specific diagnostic needs
2Reliability
If more context data is collected to improve problem diagnosis accuracy, then diagnostic quality improves, but the conversation becomes longer and user experience deteriorates
Solution Approach 1:
The system performs preliminary identification of required context entities before the actual collection process. By using NLP to pre-identify what context is needed based on the problem type and dialog state, the system avoids collecting unnecessary data during the conversation, thus maintaining diagnosis accuracy while reducing dialog length
Solution Approach 2:
The context collection process is segmented into different stages: initial problem identification, context entity identification using NLP, selective data collection based on identified entities, and resolution. This segmentation allows the system to collect only necessary context at each stage rather than gathering all possible data upfront
3Ease of operation
If the virtual dialog system uses simple question-answer mechanisms, then the system is easier to operate, but the system lacks the capability for effective context transformation and knowledge mapping
Solution Approach 1:
The system introduces an AI platform with NLP capabilities as an intermediary between the simple user questions and the complex knowledge base. This intermediary handles the transformation of user questions into structured context entities and maps them to appropriate answers, maintaining ease of operation while enabling sophisticated question transformation and knowledge mapping
Solution Approach 2:
The AI platform serves multiple functions: it processes user input, identifies context entities, transforms questions into knowledge representations, queries the knowledge base, and formulates answers. This multi-functionality allows a single system component to handle both simple interaction and complex processing
Data Source
AI summary
A system, computer program product, and a computer implemented method are provided for interfacing with a virtual dialog environment to dynamically and optimally collected context for problem diagnosis and resolution. A context model is leveraged to identify context entities, and one or more corresponding context collection mechanisms. The context model is implemented in real-time to facilitate dynamic selection of one or more of the identified context collection mechanisms, which are selectively subject to execution to resolve the problem diagnosis.