Structured Pattern Generation from Unstructured Data
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Solution Overview
Problem
Current natural language processing (NLP) systems fail to capture higher-level concepts in unstructured data, such as expert notes, and are limited by privacy concerns, making it difficult to leverage knowledge and wisdom from experts in a machine-interpretable and sharable format.
Innovation Solution
A computer-implemented method and system for real-time capture and translation of human thoughts and ideas into structured patterns, involving data capture, key term extraction, attribute assignment, and generation of structured patterns, which enables the conversion of unstructured data into a format that is easily sharable and interpretable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If NLP systems are used to process unstructured data, then text can be analyzed and tokens extracted, but higher-level concepts and semantic patterns cannot be captured
Solution Approach 1:
The patent introduces an intermediary layer between traditional NLP token extraction and concept understanding. This intermediary involves using large language models to generate contextual embeddings and semantic representations that bridge the gap between raw tokens and higher-level concepts, enabling both token extraction and conceptual understanding to coexist
Solution Approach 2:
The patent transforms the one-dimensional token sequence into multi-dimensional semantic space by projecting tokens into high-dimensional embedding spaces. This dimensional transformation allows the system to capture semantic relationships, contextual meanings, and higher-level concepts that are invisible in the original token sequence
2Productivity
If narrative text is shared to leverage expert knowledge, then wisdom can be leveraged at scale, but privacy concerns prevent sharing
Solution Approach 1:
The patent extracts only the essential semantic patterns and conceptual structures from narrative text while leaving behind personally identifiable information and sensitive details. This extraction process produces distilled knowledge representations that retain expert wisdom but remove privacy risks, enabling safe knowledge sharing
Solution Approach 2:
The patent creates abstracted copies of the original narrative text in the form of structured semantic patterns and knowledge graphs. These copies preserve the essential knowledge and insights while being devoid of private information, allowing the system to share knowledge without exposing sensitive data
3Loss of time
If unstructured data is processed in real-time, then timely insights can be obtained, but computational complexity increases
Solution Approach 1:
The patent performs preliminary processing by pre-computing contextual embeddings and semantic representations as data arrives. This preliminary action prepares the data in advance for subsequent analysis, reducing the computational burden during real-time processing and enabling timely insights without excessive complexity
Solution Approach 2:
The patent segments the complex processing task into distinct modular stages: token extraction, embedding generation, pattern recognition, and knowledge representation. This segmentation allows each stage to be optimized independently and processed in parallel, reducing overall computational complexity while maintaining real-time performance
Data Source
AI summary
Examples of techniques for the real-time capture and translation of human thoughts and ideas into structured patterns are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method may include capturing, by a processing device, unstructured data. The method may also include extracting key terms from the unstructured data. Additionally, the method may include assigning an attribute to at least one of the key terms. The method may further include generating, by the processing device, a structured pattern based on the key terms and the attributes.