Computer-based Identification of Types in the Empirical Sciences by Tangle Theory

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This patent introduces a computer-implemented method, system, and computer-readable medium based on the mathematical theory of tangles to automatically identify and distinguish meaningful clusters or types of qualities (features) in empirical data sets across various scientific domains. Unlike traditional clustering, which groups similar objects, this approach identifies groups of qualities that often occur together (tangles), enabling more robust and nuanced identification of types, phenomena, behaviors, illnesses, mindsets, or other categories from complex and potentially noisy data.

Use CasesContent extracted from patent full text and abstract with AI.

• Medical diagnostics: Discovering and defining diseases or syndromes based on clusters of symptoms, even when causes are unknown or data is imprecise.
• Text analysis: Identifying genres, topics, or authorial styles in collections of texts through characteristic linguistic features.
• Market segmentation: Profiling customer types or product types by analyzing purchasing behaviors and typical item groupings.
• Drug development: Grouping pathogens by shared features to streamline drug targeting and development.
• DNA and protein analysis: Identifying species, subspecies, or individual organisms from genetic sequence data despite incomplete or noisy data.
• Psychological and sociological research: Discovering mindsets, social groupings, or character types in large population surveys or behavioral data.
• Educational methods: Grouping teaching techniques into effective methods based on what works best for different student populations.
• Political science: Objectively identifying emergent political or social mindsets from survey data, potentially aiding in representative selection or understanding societal trends.
• Linguistics and philosophy: Quantifying family resemblances and word meanings or teaching computers robust, context-aware notions of meaning.

BenefitsContent extracted from patent full text and abstract with AI.

• Robustness to noisy, incomplete, or fuzzy data—tangles do not require precise delineation of object clusters.
• Unbiased discovery of new, previously unknown types, mindsets, or phenomena through data-driven, hypothesis-free analysis.
• Supports analysis of qualitative and binary data, making it broadly applicable across many empirical domains.
• Provides formal, computable criteria for grouping features (qualities) rather than just objects, revealing underlying types or causes more effectively.
• Enables dimensionality reduction by focusing on critical distinguishing features (Boolean expressions), making predictions and classifications more efficient.
• Applicable in unsupervised machine learning contexts and scalable to large data sets.
• Can supplement or replace traditional clustering methods, providing alternative insights into data structure.
• Facilitates diagnostic reasoning (e.g. in medicine or engineering expert systems) with structural redundancy rather than purely probabilistic models.
• Enables the design of interactive tools (such as adaptive thesauri or matchmaking algorithms) that directly exploit identified tangles.

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Patent Abstract

The present invention concerns a method of mechanically identifying and distinguishing clusters of qualities amongst potential qualities of given objects. The invention further concerns a computer-readable storage device having stored thereon instructions for carrying out such method. Moreover, the invention concerns a system for mechanical exploitation of a data set containing relations of objects each with at least one respective quality, each object being an element of a given set of objects, the qualities each being included in a given list of potential qualities.