State-Based Parser for Unstructured Text Disambiguation
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Solution Overview
Problem
Current systems for dealing with unstructured data are complex and require highly skilled professionals, making them expensive and difficult to deploy, as they struggle to efficiently impart structure and facilitate meaningful information processing.
Innovation Solution
A state-based, regular expression parser that tokenizes unstructured text, allowing for pattern recognition and reaction through stimulus/response paradigms, utilizing a probabilistic parser and knowledge bases to create structured data and enable efficient information retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If knowledge architects are used to disambiguate unstructured data, then the quality of knowledge extraction is improved, but the system complexity and deployment difficulty increase significantly
Solution Approach 1:
The system enables self-service through automated disambiguation of unstructured data using probabilistic parsing and tokenization algorithms. The knowledge base automatically processes and structures data without requiring highly skilled knowledge architects, allowing the system to serve itself in extracting meaningful information from unstructured sources.
Solution Approach 2:
The patent replaces the mechanical system of manual knowledge architecture with an automated computational system. Instead of relying on human experts to manually structure data, the system uses probabilistic parsers, regular expressions, and tokenization algorithms to automatically disambiguate and structure unstructured data, substituting human intellectual labor with automated processing mechanisms.
2Manufacturing precision
If traditional disambiguation systems are deployed, then data structure quality is improved, but deployment cost and time increase
Solution Approach 1:
The system performs preliminary action by pre-processing unstructured data through tokenization and probabilistic parsing before actual information retrieval operations. The data is disambiguated and structured in advance using automated algorithms, creating a ready-to-use knowledge base that can be quickly deployed without requiring time-consuming manual setup by specialized professionals.
Solution Approach 2:
The patent applies parameter changes by transforming unstructured data into structured formats through automated parameter extraction and assignment. The system changes the state of data from unstructured to structured by applying probabilistic models and tokenization parameters, achieving high data structure quality through automated parameter transformation rather than manual configuration.
3Measurement precision
If highly skilled knowledge architects are engaged, then data disambiguation accuracy is improved, but operational cost increases
Solution Approach 1:
The system uses copying by replicating the disambiguation process through automated algorithms that can be instantiated multiple times without requiring additional human expertise. Instead of engaging multiple expensive knowledge architects, the patent creates copyable automated parsing and tokenization systems that maintain consistent disambiguation accuracy across multiple deployments at minimal marginal cost.
Solution Approach 2:
The patent employs cheap computational objects (algorithms, parsers, tokenizers) that can be rapidly deployed and discarded or replaced as needed. These automated disambiguation components are inexpensive compared to human knowledge architects and can be quickly created, modified, or deleted without significant cost, enabling flexible and cost-effective data processing operations.
Data Source
AI summary
Various embodiments provide a state-based, regular expression parser in which data, such as generally unstructured text, is received into the system and undergoes a tokenization process which permits structure to be imparted to the data. Tokenization of the data effectively enables various patterns in the data to be identified. In some embodiments, one or more components can utilize stimulus/response paradigms to recognize and react to patterns in the data.