Automated Pattern Recognition for Avian Influenza Detection
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Solution Overview
Problem
Current automated pattern recognition systems are limited by specific modalities and require extensive human analysis, leading to inefficiencies and errors in data processing, particularly in detecting avian influenza, which poses a risk during potential pandemics due to the lack of rapid screening methods.
Innovation Solution
An automated pattern recognition and object detection system that analyzes data from various modalities without adaptation, using a minimal number of algorithms to identify features and objects in native data form, enabling rapid development and improvement across multiple data types, including avian influenza virus detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual human analysis is used to process digital data, then expertise and training can be applied to interpret complex information, but the process becomes expensive, time-consuming, and prone to human errors
Solution Approach 1:
The patent replaces manual human analysis with an automated computer-based system that processes digital data through algorithmic operations. The system converts raw digital data into visual representations and automatically identifies patterns, eliminating the need for human experts to manually examine each dataset while maintaining consistent accuracy and reducing processing time.
Solution Approach 2:
The system enables self-service analysis by automatically performing data processing, pattern recognition, and result generation without requiring human intervention. The computer system independently executes the entire analysis workflow from raw data input to interpreted output, making the analysis process autonomous and eliminating dependency on human operators.
2Ease of operation
If data is processed and filtered for human presentation, then readability is improved, but significant information is lost from the original data
Solution Approach 1:
The patent introduces an intermediary computational process that acts as a bridge between raw digital data and human interpretation. Instead of directly filtering data for human consumption, the system uses algorithmic processing to extract and highlight relevant patterns while preserving the underlying data integrity, allowing both machine accuracy and human readability.
Solution Approach 2:
The system segments the data analysis process into distinct computational stages: raw data processing, pattern identification, and visual presentation. Each stage handles specific aspects of the data independently, allowing the system to maintain complete information in the computational layers while presenting only the most relevant interpreted results to human users.
3Reliability
If automated pattern recognition systems are designed for specific modalities, then they can achieve high performance on that specific data type, but they cannot effectively handle other data types without redesign
Solution Approach 1:
The patent creates a universal automated analysis system that can process multiple data modalities including seismic, sonar, ultrasound, and other digital data types. The system uses a common computational framework that automatically adapts to different input types, eliminating the need for separate specialized systems while maintaining high performance across all modalities through consistent algorithmic processing.
4Measurement precision
If complex algorithms are used to find shapes and patterns in data, then detection accuracy is improved, but the processing becomes extremely time-intensive and deployment is delayed
Solution Approach 1:
The patent applies preliminary filtering and preprocessing operations that prepare the data in advance for more efficient pattern recognition. By pre-organizing the digital data into structured formats and pre-identifying potential pattern locations, the system reduces the computational burden of subsequent complex algorithms, achieving both high accuracy and improved processing speed.
Data Source
AI summary
Systems and methods for automated pattern recognition and detection of avian influenza virus in a data set corresponding to an aspect of a biological sample. The method includes receiving a first data set corresponding to a first aspect of a first biological sample, analyzing the first data set using results of a first series of algorithms processed on a second data set corresponding to an aspect of a second biological sample known to contain avian influenza virus, generating an algorithm value cache for the first data set by running a second series of algorithms on the first data set, generating a match result by comparing the algorithm value cache with the results of the first series of algorithms, and performing a processing action based on the generated match result.