Semantic Node Representation for Explainable Natural Language Reasoning
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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 requiring broad and unstructured data, such as health management, nutrition tracking, and accounting, where traditional methods like structured data and statistical machine learning fall short in scalability and explainability.
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
1Adaptability or versatility
If structured data with comprehensive schema is used to represent broad heterogeneous data, then data coverage and completeness are improved, but system complexity and difficulty of maintenance increase exponentially
Solution Approach 1:
The patent segments the comprehensive schema into reusable data models that can be independently defined and combined. Instead of creating one enormous schema to cover all possible data, the system divides data representation into modular components (data models) that can be assembled as needed, reducing overall complexity while maintaining comprehensive coverage.
Solution Approach 2:
The patent creates universal data models that can serve multiple purposes and represent different types of heterogeneous data. These data models are designed to be reusable across multiple contexts and applications, allowing the same model to represent various data types without requiring separate schemas for each, thereby reducing complexity while maintaining versatility.
2Productivity
If statistical machine learning methods are used to process natural language, then processing speed is improved, but explainability and deep understanding of data meaning deteriorate
Solution Approach 1:
The patent introduces structured data models as an intermediary layer between natural language input and machine processing. Instead of directly applying statistical methods to raw text, the system first transforms natural language into structured representations using defined data models, which then guide the processing. This intermediary structure preserves semantic meaning while enabling efficient processing.
Solution Approach 2:
The patent performs preliminary structuring of natural language data into standardized formats before main processing occurs. By pre-defining data models and transforming input data into these structures in advance, the system prepares data for more efficient processing while maintaining semantic integrity, rather than relying solely on post-hoc statistical analysis.
3Ease of manufacture
If keyword searching and statistical NLP techniques are used for natural language processing, then implementation simplicity is improved, but accuracy in understanding data meaning deteriorates
Solution Approach 1:
The patent segments natural language processing into distinct stages: initial keyword matching for simplicity, followed by structured data model application for precision. This segmentation allows the system to use simple keyword searching to identify potential matches, then apply more sophisticated structured processing to verify and refine understanding, combining the benefits of both approaches.
Solution Approach 2:
The patent uses structured data models as an intermediary between simple keyword searching and accurate semantic understanding. The keyword search provides initial candidates, the data models structure and validate the information, and this intermediate structured representation enables accurate meaning interpretation without requiring the entire system to be complex.
Data Source
AI summary
A computer implemented method for the automated analysis or use of data, comprising: learning new information and representing the new information in a structured, machine-readable representation, in which representation of data comprises semantic nodes and passages; and in which each semantic node represents an entity and is represented by an identifier; and each passage is either (i) a semantic node or (ii) a combination of semantic nodes; and where machine-readable meaning comes from choice of semantic nodes and how they are combined and ordered as passages; in which the representation of data uses a shared syntax that applies to semantic nodes and passages that represent factual statements, query statements and reasoning statements, wherein the syntax is an unambiguous syntax comprising nesting of structured, representations of data to a depth; and storing the structured representation of data in a non-transitory storage medium and automatically processing it.