Context Object Mediator for Natural Language User Interfaces
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Solution Overview
Problem
Natural language user interfaces face challenges in providing continuous contextual engagement, as they struggle to effectively understand and respond to user queries across various domains like expense management and e-commerce, often leading to user dissatisfaction due to limited contextual understanding and inadequate information extraction.
Innovation Solution
A system comprising a context manager, content knowledge database, information extraction engine, contextual topic engine, discourse manager, natural language generation module, inference engine, and query engine that work together to maintain a context object, extract information, infer user intent, and generate interactive dialogues, utilizing rule-based and statistical natural language processing to understand and respond to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language user interfaces are used to interact with applications, then user satisfaction increases through automated conversation engagement, but contextual understanding and information extraction capabilities are insufficient
Solution Approach 1:
The system segments the natural language processing task into multiple specialized modules: information extraction engine for extracting entities and attributes, contextual topic engine for identifying topics and entities, inference engine for deriving user intent, and discourse manager for maintaining conversation context. Each module handles a specific aspect of contextual understanding, collectively resolving the information loss problem while maintaining ease of operation.
Solution Approach 2:
The patent introduces a context object as an intermediary data structure that bridges the gap between user input and system response. The context object stores extracted information, inferred intent, and conversation history, serving as a mediator that preserves contextual understanding across multiple interaction turns, thereby preventing information loss while maintaining natural language interaction ease.
2Measurement precision
If multiple modules are integrated to improve contextual understanding, then information extraction accuracy improves, but system complexity increases
Solution Approach 1:
The context object serves as a universal data structure that is updated and utilized by all modules in the system. The discourse manager, inference engine, and natural language generation module all interact with the same context object, reducing the need for separate complex data structures in each module and thereby managing system complexity while maintaining high information extraction accuracy through coordinated multi-module operation.
Solution Approach 2:
The system implements feedback loops where the discourse manager continuously updates the context object based on conversation flow, and the inference engine uses this updated context to refine user intent understanding. This feedback mechanism allows modules to iteratively improve information extraction accuracy without requiring all modules to be fully complex from the start, as each module builds upon the contextual information provided by others.
Data Source
AI summary
In one exemplary aspect, a contextual user engagement system for automatically engaging in a conversation with a user is provided. A context manager manage, with at least one processor, the context of the conversation with a user. The context manager receives a user utterance, wherein the context manager calls the other modules of the contextual user engagement system to obtain information with respect to the user utterance. The context manager maintains a context object for a conversation with a user. The context manager automatically updates the context object based on an information detected by the other modules of contextual user engagement system. A content knowledge database stores a content knowledge of the contextual user engagement system. The content knowledge includes a history of interactions with the user. An information extraction engine extract information from a specified source based on a set of rules.