State Machine Parser for Natural Language Context
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Solution Overview
Problem
Current natural language processing systems face challenges in understanding single sentences and sequences of sentences, as they struggle to interpret the relationships between sentences and maintain context across multiple sentences.
Innovation Solution
A method is introduced to convert parser-determined sentence parts into novel computer understanding state machine states, utilizing a modified Woods sentence parsing method to abstract word categories and activate abstract data states, enabling the computer understanding state machine to process and understand single sentences and sequences by creating data links and transitioning between states based on sentence inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Woods state machine parser is used to convert words to sentence parts, then the sentence can be parsed into subject, verb, and object, but the computer cannot understand the meaning or relationships in the sentence
Solution Approach 1:
The patent introduces an intermediary layer between the Woods parser and the computer system. This intermediary consists of abstract data states that represent semantic concepts (e.g., SUBJECT, VERB, OBJECT, LOCATION, TIME) and serve as mediators to translate parsed sentence parts into computer-understandable representations. The intermediary layer enables the computer to understand sentence meaning by mapping parsed components to semantic concepts through state transitions.
2Ease of operation
If traditional NLP systems parse sentences into sentence parts, then the grammatical structure is identified, but the relationships between multiple sentences cannot be understood
Solution Approach 1:
The patent implements continuity of useful action by maintaining active state representations across multiple sentences. When a sentence is parsed, abstract data states are activated and maintained in the system. Subsequent sentences can reference and build upon these active states, enabling the computer to understand relationships between sentences. The state machine continues to process and update states continuously, preserving contextual information across the sequence of sentences.
3Loss of information
If more detailed sentence analysis is performed to improve understanding, then the system can identify more sentence parts, but the complexity of the processing increases
Solution Approach 1:
The patent applies local quality by creating specific abstract data states for different semantic roles (SUBJECT, VERB, OBJECT, LOCATION, TIME, etc.) rather than using a single generic state. Each sentence part type has its own dedicated state representation, allowing the system to handle different types of information with appropriate specificity. This localized specialization improves understanding quality without requiring a monolithic complex system, as each state can be processed independently.
Data Source
AI summary
A method for processing natural language sentences and a computer understanding state machine with computer understanding state machine states are disclosed. The computer understanding state machine receives as inputs the parsed sentence including abstractions of the parsed sentence including weights of the abstractions. These inputs are used to find the best fitting state to process the input sentence. Subsequent sentences also are parsed and these are input to the state machine to allow processing of sequences of sentences.