Sub-Primal Cut Classification Using Multi-Scan Shape Matching
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Solution Overview
Problem
Existing systems struggle to efficiently identify and categorize sub-primal cuts based on their shape and attribute characteristics, leading to inefficiencies in portioning, sorting, and packaging processes.
Innovation Solution
A computer-implemented method using machine learning models to analyze scans of sub-primal cuts, aligning reference shapes and attribute characteristics to determine the type and category of each cut, enabling precise identification and optimization of processing steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple scan types and machine learning models are used to identify sub-primal cuts, then identification accuracy is improved, but system complexity increases
Solution Approach 1:
The identification system is segmented into multiple independent components: X-ray scanning subsystem, optical scanning subsystem, machine learning model library, and processing unit. Each component performs a specific function (density measurement, visual characteristic detection, pattern recognition, coordinate alignment), allowing the complex identification task to be divided into manageable segments that can be processed independently and combined for final classification.
Solution Approach 2:
The system employs a universal machine learning model architecture that can handle multiple scan types (X-ray and optical) and various sub-primal cut types through a single integrated processing framework. The coordinate alignment technique provides a universal method for matching scan data with reference models regardless of the specific cut type or scan modality, enabling one system to serve multiple identification functions.
2Manufacturing precision
If shape and attribute characteristics are precisely measured, then categorization accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary coordinate alignment between scan data and reference model coordinates before detailed comparison. This preliminary step establishes the correct spatial relationship and orientation, allowing subsequent shape and attribute measurements to be performed more efficiently with fewer iterations. The pre-alignment prevents time-consuming trial-and-error matching during the detailed categorization phase.
Solution Approach 2:
The system replaces manual or mechanical measurement methods with automated machine learning algorithms for comparing shapes and attributes. The computational models automatically calculate shape match values and attribute match values by comparing scanned coordinates with reference models, eliminating the need for physical measurement tools and manual assessment, thereby reducing processing time while maintaining high precision.
3Reliability
If multiple reference shapes and match characteristics are aligned, then identification reliability is improved, but computational requirements increase
Solution Approach 1:
The system applies different levels of analysis to different parts of the sub-primal cut based on their importance for identification. Critical regions (such as bone structures, fat distribution patterns, or distinctive shape features) receive more detailed analysis with multiple reference shape comparisons, while less critical areas use simplified matching. This local quality approach ensures high reliability for distinguishing features while reducing computational energy spent on less discriminative regions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the efficiency of sub-primal cut processing by accurately identifying and categorizing cuts, allowing for optimized portioning, sorting, and packaging based on their specific characteristics.
Implementation Method 1
a first scan of a first scan type showing a first match characteristic and a second scan of a second scan type showing a second match characteristic
Implementation Method 2
a first scan of a first scan type showing a first match characteristic and a second scan of a second scan type showing a second match characteristic
Data Source
AI summary
A method carried out by a computing device may include: receiving a plurality of models each associated with a type of sub-primal cut, including a reference shape(s) for detection of a corresponding match characteristic by a corresponding scan type, and including a plurality of attribute characteristic value ranges; receiving a registered scan of a sub-primal cut including scans showing corresponding match characteristics; identifying attribute characteristics for the sub-primal cut using the registered scan; concurrently aligning reference shape(s) of each model to corresponding match characteristics and calculating a shape match values based on the alignment; determining a best model match for the registered scan using a plurality of attribute match values calculate based on the identified attribute characteristics compared to the attribute characteristic value ranges and the plurality of shape match values; and assigning a type of sub-primal cut to the sub-primal cut based on the best model match.