Anatomy Data Cohorting for Automated Attribute Detection

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Solution Overview

Problem

Medical professionals face challenges in early detection of medically relevant attributes due to large quantities of unlabeled clinical data, obscuring the connection between structural and anatomic bases.

Innovation Solution

An apparatus and method using a processor to receive and analyze anatomy data, extract anatomic features, group data into cohorts, and detect attributes through statistical comparison, leveraging machine learning and large language models.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If medical professionals manually analyze large quantities of unlabeled clinical data to detect medically relevant attributes, then detection accuracy may be maintained, but the time consumption and workload increase significantly

Engineering Contradiction:

Improvedetection accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent introduces an intermediary system comprising a processor that automatically performs attribute detection on anatomy data. This intermediary system bridges the gap between raw clinical data and medical professionals, handling the time-consuming analysis task while maintaining detection accuracy through automated feature extraction and cohort-based statistical comparison.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the mechanical manual analysis process with an automated computational system. The processor executes algorithms for feature extraction, cohort grouping, and statistical comparison, substituting human manual labor with machine-based automation that operates faster and consistently without fatigue.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated attribute detection is implemented to reduce time consumption, then processing speed increases, but the complexity of the system increases

Engineering Contradiction:

Improveprocessing speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The automated detection system is segmented into distinct functional modules: a feature extraction module that identifies anatomic features from anatomy data, a cohort grouping module that organizes subjects into cohorts based on extracted features, and a statistical comparison module that detects attributes through comparison. This segmentation manages system complexity by creating modular, independently manageable components.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system manages complexity by changing parameters in a controlled manner - using cohort-based grouping to reduce the dimensionality of the problem space, and applying statistical thresholds to simplify attribute detection decisions. These parameter changes transform complex continuous data into discrete, manageable categories.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If comprehensive feature extraction is performed on all anatomy data to ensure thorough detection, then detection completeness improves, but the computational resources and processing time increase

Engineering Contradiction:

Improvedetection completenessVSAvoidcomputational resources

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system extracts only the most relevant anatomic features from the comprehensive anatomy data rather than processing all possible features. This selective extraction maintains detection completeness for medically significant attributes while reducing the overall computational burden by focusing on high-value features.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system performs partial feature extraction and analysis, focusing on the subset of features most likely to contain medically relevant attributes. Rather than exhaustively analyzing every possible feature, the system applies targeted analysis to the most promising candidates, achieving sufficient detection completeness with reduced computational resources.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250329470A1Apparatus and methods for attribute detection in anatomy data

Publication Date: 2025.10.23 NFERENCE INC

AI summary

Apparatus for attribute detection in anatomical data and methods used therein are described, wherein the apparatus includes a processor and a memory communicatively connected to the processor, wherein the memory includes instructions configuring the processor to receive reference anatomy data and reference metadata, extract anatomic features from the received reference anatomy data and reference metadata, group the received reference anatomy data and reference metadata into a plurality of cohorts with one or more similar groups of anatomic features as a function of the extracted anatomic features, receive query anatomy data and query metadata, label the received query anatomy data and query metadata as a function of the plurality of cohorts, and detect at least an attribute as a function of the label.