Biological Sample Characteristic Identification via Multi-Dimensional Data Segmentation
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Solution Overview
Problem
Current methods for analyzing biological samples are limited in their ability to efficiently identify multi-dimensional and multi-layered characteristics, such as position, movement, size, conductivity, and shape, within biological samples, particularly in real-time and interactive applications.
Innovation Solution
An apparatus and method that obtain multi-dimensional and multi-layered datasets from biological samples by stimulating them with electrical, acoustic, or electromagnetic signals, filtering these datasets to select features of interest, comparing them to reference features, and making associations to identify characteristics, using dynamic filtering and parallel processing to enhance accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multi-dimensional and multi-layered datasets are obtained from biological samples, then comprehensive biological characteristics can be identified, but data complexity and processing difficulty increase
Solution Approach 1:
The patent divides multi-dimensional and multi-layered datasets into multiple sections, where each section contains at least one feature. This segmentation approach allows the complex data to be processed in manageable units while preserving all biological characteristics information.
Solution Approach 2:
The patent extracts features of interest from the segmented datasets by comparing them against reference features. This extraction process isolates relevant biological characteristics from the complex multi-dimensional data, reducing processing complexity while maintaining identification accuracy.
2Measurement precision
If filtering is applied to select features of interest, then feature selection accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary filtering of datasets into sections before detailed feature comparison. This preliminary action organizes data in advance, enabling faster and more accurate feature selection during the comparison stage without excessive processing time.
Solution Approach 2:
The patent replaces traditional sequential filtering mechanisms with a parallel processing approach where multiple sections are filtered and compared simultaneously. This substitution reduces processing time while maintaining feature selection accuracy through concurrent operations.
3Productivity
If parallel processing is used to process sections simultaneously, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent segments datasets into independent sections that can be processed in parallel. Each section contains sufficient information for independent feature extraction and comparison, enabling simultaneous processing that increases speed while keeping individual processing units relatively simple.
4Measurement precision
If dynamic filtering and autonomous learning are implemented, then identification accuracy improves, but computational requirements increase
Solution Approach 1:
The patent implements autonomous learning where the system automatically learns and stores reference features by iteratively operating filters on labelled reference features. This self-service mechanism improves identification accuracy over time without requiring continuous external intervention or excessive computational energy input.
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
Enables precise and timely identification of biological sample characteristics, facilitating applications in health monitoring, diagnostics, and human-machine interaction by reducing processing time and improving feature selection accuracy.
Implementation Method 1
obtaining a plurality of multi-dimensional and multi-layered datasets from an output signal provided by stimulating the biological sample with one or more of: an electrical signal, an acoustic signal, an electromagnetic signal
Implementation Method 2
The plurality of multi-dimensional and multi-layered datasets may comprise electrical impedance tomography signals
Data Source
AI summary
Examples of the disclosure relate to apparatus, methods and computer programs for identifying characteristics of biological samples. The apparatus can comprise means for obtaining a plurality of multi-dimensional and multi-layered datasets from a biological sample and dividing the plurality of multi-dimensional and multi-layered datasets into a plurality of sections wherein each section comprises at least one feature. The means are also for filtering, for at least a subset of the sections, the plurality of multi-dimensional and multi-layered datasets to select features of interest, comparing the selected features of interest to a plurality of reference features and making one or more associations between two or more selected features of interest to identify one or more characteristics of the biological sample.