Natural Language Processing for Surveillance Spatial Reasoning
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Solution Overview
Problem
Current controlled natural languages for processing surveillance data lack the ability to handle spatial reasoning and mathematically formalize vague concepts like 'near' or 'close to', making it difficult for machine learning systems to understand intent from surveillance observations in real-time, especially for non-technical domain experts.
Innovation Solution
A computer-implemented method that processes natural language inputs to extract parameters and define mathematical functions representing domains, enabling the detection of anomalous behavior by parsing spatial and temporal concepts into mathematical expressions, allowing for the interpretation of vague spatial and temporal concepts like 'near' or 'lunchtime' in a culturally appropriate manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language is used for inputting domain knowledge, then ease of operation is improved, but processing difficulty increases
Solution Approach 1:
The patent introduces controlled natural language as an intermediary between full natural language and formal mathematical representations. This intermediary language maintains grammatical structure and semantic meaning while being systematically translatable to mathematical functions, thereby reducing processing complexity while preserving ease of use for domain experts.
Solution Approach 2:
The system transforms natural language parameters into mathematical parameters through systematic mapping. By changing the representation parameters from unstructured text to structured mathematical expressions with defined semantics, the system maintains user-friendly input while reducing computational processing difficulty.
2Device complexity
If controlled natural language is used, then processing difficulty is reduced, but spatial reasoning capability is lost
Solution Approach 1:
The patent extends controlled natural language by adding spatial dimension parameters to the existing temporal and logical structure. This allows the language to express spatial relationships (near, far, left, right, above, below) while maintaining the systematic processing advantages of controlled language through defined grammatical rules and mathematical mappings.
3Productivity
If existing controlled natural languages are used, then processing efficiency is improved, but ability to handle vague concepts is reduced
Solution Approach 1:
The patent applies local quality by allowing different levels of precision for different spatial and temporal concepts within the same language framework. Vague concepts like 'near' or 'close to' can be expressed with appropriate mathematical functions (e.g., fuzzy logic, probability distributions) that capture the inherent uncertainty, while maintaining overall processing efficiency through systematic handling of these local variations.
Data Source
AI summary
Methods and systems are provided for processing natural language for machine learning analytical systems. The method includes receiving, at a processor, an input including text representing one or more observed parameters of an environment. The inputted text is in a natural language format. The processor parses the input and extracts the one or more parameters. A function is defined representing a domain of the one or more observed parameters based upon the one or more extracted parameters.