Convolutional State Modeling for Natural Language Conversation Planning
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Solution Overview
Problem
Current computerized systems lack the ability to effectively understand user needs and preferences in specific domains, leading to inefficiencies in content discovery and conversation planning, especially with the overload of information available online.
Innovation Solution
A method and system utilizing ensemble Natural Language Understanding and Processing techniques, including preprocessing, Name Entity Recognition, sentiment analysis, and classification, to convert user queries into machine queries that interact with a knowledge model to determine optimal conversation paths and provide relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If information overload occurs in online content, then the quantity of available information increases, but the learning experience quality deteriorates
Solution Approach 1:
The system segments the overwhelming amount of information into structured knowledge graphs with hierarchical relationships. Information is organized into entities, attributes, and relationships that can be systematically navigated, transforming the unmanageable information overload into organized, accessible knowledge structures that improve learning experience quality.
Solution Approach 2:
The patent introduces an AI-powered conversation planning system as an intermediary between users and the vast information repository. This intermediary uses natural language processing and knowledge graph querying to filter, retrieve, and present only the most relevant information, eliminating the need for users to navigate information overload directly while maintaining high learning experience quality.
2Productivity
If traditional search methods are used in information overload, then information retrieval is performed, but friction in content discovery increases
Solution Approach 1:
The system implements self-service through autonomous conversation agents that automatically plan and execute information retrieval tasks. These agents use the knowledge graph to autonomously navigate, query, and synthesize information without requiring users to manually search through overwhelming content, thereby reducing friction while maintaining high retrieval efficiency.
Solution Approach 2:
The patent transforms the search interface from traditional keyword-based parameters to natural language conversation parameters. Users can query the knowledge graph using everyday language rather than specialized search syntax, changing the interaction parameters to reduce friction while the system maintains efficient retrieval through its structured knowledge representation.
3Extent of automation
If autonomous coaches are deployed for content discovery, then automation of coaching processes increases, but system complexity increases
Solution Approach 1:
The patent extracts the complex conversation planning and natural language understanding functions into a separate, specialized module that interfaces with the knowledge graph. This extraction allows the autonomous coaching system to handle complex NLP tasks in isolation while maintaining a simpler overall system architecture, enabling high automation without proportionally increasing overall system complexity.
Data Source
AI summary
In one aspect, a computerized method useful for, with an ensemble of Natural Language Understanding and Processing methods converting a set of user actions into machine queries, includes the step of providing a knowledge model. The method includes the step of receiving a natural language user query; preprocesses the natural language user query for further processing as a preprocessed user query. The preprocessing includes the step of chunking a set of sentences of the natural language query into a set of smaller sentences and retaining the reference between chunks of the set of sentences. The method includes the step of, with the preprocessed user query. For each chunk of the chunked preprocessed user query the method implements the following steps.