State Graph Machine Interaction for Natural Language Commands
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Solution Overview
Problem
Existing systems for interacting with machines or devices often require unnatural command syntax and grammar, making it difficult for users to communicate their goals and objectives effectively using natural language.
Innovation Solution
A human interaction system that processes natural language inputs, identifies objects and their properties within an environment using sensor data, and generates instructions for mechanical systems to achieve user-defined goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language input is used for machine interaction, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent introduces a state graph as an intermediary structure between natural language input and machine execution. The state graph contains nodes representing environmental states and edges representing transitions, serving as a mediator that translates human language into machine-understandable instructions without requiring users to learn complex syntax or grammar rules.
Solution Approach 2:
The patent replaces traditional command-line interfaces and structured query systems with a natural language processing system. Instead of requiring users to input formal commands with specific syntax, the system processes spoken or written natural language through NLP algorithms, converting them into state graph operations that drive machine behavior.
2Adaptability or versatility
If natural language processing is implemented, then adaptability is improved, but device complexity increases
Solution Approach 1:
The state graph is designed as a dynamic structure that can be modified and expanded based on user interactions and environmental changes. Nodes and edges can be added or modified to accommodate new concepts, objects, or relationships, allowing the system to adapt to diverse domains and applications without requiring complete system redesign.
Solution Approach 2:
The state graph framework provides a universal structure that can represent various types of information and relationships across different domains. The same basic graph structure can model physical environments, abstract concepts, or hybrid scenarios, making the system versatile across multiple applications while maintaining a consistent interaction model.
3Measurement precision
If state graph processing is used, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The state graph is constructed and prepared in advance, with nodes and edges representing possible environmental states and transitions. This preliminary structuring allows the system to quickly match user intentions against predefined state transitions, reducing processing time during actual interactions while maintaining precise interpretation of user goals.
Data Source
AI summary
In one embodiment, a method is provided. The method includes obtaining sensor data indicative of a set of objects detected within an environment. The method also includes determining a set of positions of the set of objects and a set of properties of the set of objects based on the sensor data. The method further includes generating a state graph based on the sensor data. The state graph represents the set of objects and the set of positions of the set of objects. The state graph includes a set of object nodes to represent the set of objects and a set of property nodes to represent the set of properties of the set of objects. The state graph is provided to a graph enhancement module that updates the state graph with additional data to generate an enhanced state graph.