Semantic Node Language for Accurate Natural Language Analysis
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Solution Overview
Problem
Existing computer systems struggle to deeply understand and process the meaning of natural language due to the complexity and heterogeneity of data, leading to inefficiencies in applications that require broad and unstructured data, such as health management, nutrition tracking, and accounting, where traditional structured data approaches fall short.
Innovation Solution
A computer-implemented method using a structured, machine-readable language (UL) that represents data with semantic nodes and links, allowing for the analysis and processing of user speech or text inputs, enabling faster and more accurate responses by simplifying processing through the use of semantic nodes that are also semantic links, and allowing for nested expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional structured data approaches are used to process natural language, then data organization is simplified, but understanding depth and processing accuracy deteriorate
Solution Approach 1:
The patent segments natural language understanding into distinct components: semantic role labeling (SRL) identifies predicates and arguments, while separate processing handles context and reasoning. This segmentation allows structured data organization to coexist with deep semantic analysis, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent introduces semantic role labeling as an intermediary layer between raw natural language input and structured data processing. This intermediary extracts and structures semantic information (predicates, arguments, roles) in a way that maintains both organizational simplicity and understanding depth, bridging the gap between the two opposing requirements.
2Adaptability or versatility
If broad heterogeneous data collections are used to enhance application capabilities, then application versatility improves, but schema complexity and processing difficulty increase
Solution Approach 1:
The patent implements a universal schema based on semantic role labeling that can handle diverse data types (health records, nutrition information, accounting data, job applications) through a common framework of predicates, arguments, and semantic roles. This universal approach enables broad versatility without requiring separate complex schemas for each application domain.
Solution Approach 2:
The patent changes the parameter of data representation from domain-specific structured fields to semantic roles and predicates. By parameterizing the schema around universal semantic concepts (agent, patient, instrument, location, time) rather than domain-specific attributes, the system achieves versatility across heterogeneous data while maintaining manageable schema complexity.
3Productivity
If statistical machine learning and deep learning are used to process natural language, then processing capability improves, but explainability and human understanding of results deteriorate
Solution Approach 1:
The patent enables the system to automatically generate human-readable explanations of its reasoning process through semantic role labeling. The structured extraction of predicates, arguments, and semantic roles provides inherent interpretability without requiring external explanation mechanisms, allowing the system to serve its own explainability needs while maintaining high processing capability.
Data Source
AI summary
A computer implemented method comprising: storing or accessing in a non-transitory storage medium a structured machine-readable representation of data that conforms to a machine-readable processable language, receiving a sequence of words in a natural language, and automatically translating that sequence into the machine-readable processable language by identifying or generating structured, machine-readable representations that semantically represent a meaning of the sequence of words. The representation of data comprises semantic nodes and passages. A semantic node represents an entity and represented by an identifier. A passage is either a semantic node or a combination of semantic nodes. Meaning comes from choice of semantic nodes and the way their combinations. The method further includes translating the structured-machine readable representation to the natural language. The machine-readable processable language is processed to vary generated translations between alternatives that are substantially semantically equivalent to create varied and fresh responses for the benefit of human users.