20240028887. Processing Using a Neural Network and a Similarity Metric simplified abstract (RapidSilicon US, Inc)
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Processing Using a Neural Network and a Similarity Metric
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Processing Using a Neural Network and a Similarity Metric - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240028887 titled 'Processing Using a Neural Network and a Similarity Metric
Simplified Explanation
The abstract describes a technology for classification using a convolutional-inspired neural network. Here are the bullet points explaining the patent/innovation:
- The technology involves a convolutional neural network that receives an input feature map.
- A convolutional layer is applied using an operator and a filter to generate an output feature map.
- The operator includes a similarity metric that calculates the similarity between a filter tensor and a feature tensor.
- The output feature map is then flattened.
- A fully connected output layer is used to define a class for the output feature map.
Potential Applications:
- Image recognition and classification
- Object detection and tracking
- Natural language processing and sentiment analysis
- Speech recognition and transcription
- Medical image analysis and diagnosis
Problems Solved:
- Efficient and accurate classification of complex data
- Handling large-scale datasets
- Extracting meaningful features from input data
- Automating the classification process
Benefits:
- Improved accuracy and efficiency in classification tasks
- Ability to handle large and complex datasets
- Automated and scalable classification process
- Potential for real-time classification in various domains
Original Abstract Submitted
a technology is described for classification using a convolutional-inspired neural network. the method can include the operation of receiving an input feature map to an input layer of the convolutional neural network. another operation may be applying a convolutional layer using an operator and a filter to form an output feature map. the operator may include a similarity metric that provides a similarity output between a filter tensor from the filter and a feature tensor from the input feature map. the output feature map may then be flattened. a further operation may be defining a class of the output feature map using a fully connected output layer.
