18511337. ANOMALY DETECTION METHOD, ANOMALY DETECTION DEVICE, AND RECORDING MEDIUM simplified abstract (Panasonic Intellectual Property Corporation of America)
Contents
- 1 ANOMALY DETECTION METHOD, ANOMALY DETECTION DEVICE, AND RECORDING MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ANOMALY DETECTION METHOD, ANOMALY DETECTION DEVICE, AND RECORDING MEDIUM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
ANOMALY DETECTION METHOD, ANOMALY DETECTION DEVICE, AND RECORDING MEDIUM
Organization Name
Panasonic Intellectual Property Corporation of America
Inventor(s)
Denis Gudovskiy of San Ramon CA (US)
ANOMALY DETECTION METHOD, ANOMALY DETECTION DEVICE, AND RECORDING MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18511337 titled 'ANOMALY DETECTION METHOD, ANOMALY DETECTION DEVICE, AND RECORDING MEDIUM
Simplified Explanation
The abstract describes a method for anomaly detection using a convolutional neural network to obtain and analyze feature data from different layers.
- The method involves obtaining feature data from N convolutional layers of an encoder neural network when an image is inputted.
- It also includes obtaining feature data from M convolutional layers (where M is different from N) that are different in size from the first feature data.
- Anomaly detection is performed on the image using the features indicated by the first and second feature data.
Potential Applications
This technology can be applied in various fields such as:
- Cybersecurity for detecting anomalies in network traffic.
- Medical imaging for identifying abnormalities in scans.
- Quality control in manufacturing processes to detect defects.
Problems Solved
This technology addresses the following issues:
- Efficient anomaly detection in large datasets.
- Improved accuracy in identifying anomalies in complex data.
- Automation of anomaly detection processes.
Benefits
The benefits of this technology include:
- Enhanced security by quickly identifying abnormal patterns.
- Increased efficiency in anomaly detection tasks.
- Cost savings through automated anomaly detection processes.
Potential Commercial Applications
The potential commercial applications of this technology include:
- Integration into cybersecurity software for real-time anomaly detection.
- Development of medical diagnostic tools for early anomaly detection.
- Implementation in quality control systems for manufacturing industries.
Possible Prior Art
One possible prior art for this technology could be the use of traditional machine learning algorithms for anomaly detection, which may not be as effective in handling complex data structures.
Unanswered Questions
How does this method compare to traditional anomaly detection techniques?
This article does not provide a direct comparison to traditional anomaly detection methods, leaving the reader to wonder about the specific advantages or limitations of this new approach.
What are the computational requirements for implementing this anomaly detection method?
The article does not delve into the computational resources needed to execute this anomaly detection method, leaving a gap in understanding the practical implications of adopting this technology.
Original Abstract Submitted
An anomaly detection method by which a computer performs anomaly detection includes: obtaining first feature data outputted through N (N is an integer not less than 1) convolutional layers of a convolutional neural network configured as an encoder when an image is inputted to the convolutional neural network; obtaining second feature data outputted through M (M is an integer not less than 1, and M≠N) convolutional layers of the convolutional neural network and different in size from the first feature data; and performing anomaly detection on the image by using features indicated by the first feature data and the second feature data that are different in size.
