can you patent a neural network

Take care to fit the scaler only on the train data and not the test data. The task is to classify the tumors into malignant or benign based on these features. All Rights Reserved. Become a Machine Learning ExpertWhat Is Machine Learning? ANN is based on a collection of connected units or nodes called artificial neurons, which loosely models the neurons in a . The reason for this might be a well-optimized backpropagation algorithm, which helps the model achieve higher accuracies in a fewer number of iterations. As an example, I have been writing AI for the horse racing world since 1. Neural networks are made up of nodes. The train and test accuracy is found to be Train accuracy: [97.14285714] Test accuracy: [97.36842105]. He has worked in data science since 2020, and has experience working in machine learning and deep learning. Software isnt patentable, so neural nets arent either: Software inventions are patentable, but the Patent Office and the Courts have narrowed the scope of what is patentable by requiring that the patent claims must be directed to something more than a well-known or abstract concept implemented on a computer. When you want to figure out how a neural network functions, you need to look at neural network architecture. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. Compare new txt file with old txt file and remove all data that matches, The best way to light an LED at a distance. It only takes a minute to sign up. Norman Paul Jouppi Andrew Everett Phelps Reginald Clifford Young Thomas Norrie Gregory Michael Thorson Dan Luu Current Assignee (The listed assignees may be inaccurate. I have plotted two of the features, mean radius and mean texture, to gain some information about whether the tumor is malignant (M, represented by blue dots) or benign (B, represented by an orange x). For example, is the concept a new topology for a neural net that is more efficient, yields better results, or solves a particular problem? The results show low relative errors of 2.19%, 14.88%, and 1.11% for the crack's angle, width, and endpoint length from the CNN and . We already have a lot of learning algorithms like linear regression, logistic regression, decision trees and random forests, etc. It gets input x1, x2 and x3 and outputs a value h(x). On checking for the unique values in the target variable,array(['M', 'B'], dtype=object), there are two classes in the output: M for malignant and B for benign. If you have an AI (or other) invention that you are looking to patent, it is wise to consult with a knowledgeable and experienced patent attorney. 5 may be performed, for example, by one or more elements of system 100, system 200, system 400, and/or their components.An array of neural network input values for a current layer is received (502).For example, crossbar 450 may receive input values from local register 421 or an . For example, a neural network can look at pictures and recognize certain elements, like pixel colors and classify them according to what they show. In this equation, g is the sigmoid function. the answer isn't "yes, this is broad, and it probably won't be granted"), including some more information may help with the. Cir. Description. The network topology (i.e. As more information regarding the target variable is available, the model accuracy increases. Cir 1990)'. It calls the weight initialization, forward propagation and backpropagation function in that order and trains the model. So it is now part of the public domain and if there is no other protection for this (yes, this could happen, should not though), everyone may use it. A neural network is a network of artificial neurons programmed in software. Applying AI to Solve Problems. In general, you cannot patent an algorithm, but you can patent a device that uses an algorithm. To ensure that the network can learn the deep abstract features of the image, the corresponding upsampling path also includes three upsampling and residual bottleneck modules. Its also computationally expensive to work with that many features. It is very easy to use a Python or R library to create a neural network and train it on any dataset and get a great accuracy. The goal of this project is to develop a general method that can train many different types of neural networks, and to demonstrate and evaluate their performance on new emerging hardware. Artificial intelligence (AI) is the ability of computers and machines to perform mental tasks commonly associated with humans, such as learning, reasoning and problem solving. Its a subset of machine learning and includes a system of neurons that can process computationally expensive data sets. This site and any information contained herein are intended for informational purposes only and should not be construed as legal advice. In a recent Board of Appeal decision, an EPO Board of Appeal found that a neural network was simply a mathematical method and so excluded from patentability under the European Patent Convention. It calls the weight initialization, forward propagation and backpropagation function in that order and trains the model. The above plot and metrics correspond to the default values. However, most patent mining research only focuses on textual information and ignores visual information. This is a preview of subscription content, access via In fact, that is one of the major issues with neural networks. This will result in all the neurons computing the same features, thereby outputting a redundant value and preventing the NN from learning. However, it is imperative that you protect yourself and your invention. If we were to include only the quadratic terms in the hypothesis, we will still have hundreds of non-linear features. Is the invention a new gradient descent algorithm that is optimized for a particular problem space? The number of quadratic features generated will be to the order of, are the input nodes in blue color. The question is relevant. But the patent is from 1997 (and maybe claims earlier priority, I didn't check). Neural networks are made up of nodes. For this, we need to find the gradient of cost with respect to each of the weight matrices. For example, under U.S. law, an invention that is a stand-alone algorithm is likely to be seen as no more than abstract mathematics and, therefore, not eligible for patent protection. for modeling a neural network, training and testing it. Machine learning (ML) is basically a term to cover algorithms that use statistics to find and apply patterns in digitally stored data, which can be images, numbers, words, etc. I didnt invent a neural network so this is probably not patentable: Keeping the right focus on the invention is a fundamental issue that sometimes hampers patentability for software inventions, and it can be particularly problematic where neural networks are involved. Why do we need yet another learning algorithm? [0038] Step 5: The entire fully convolutional neural network process is as follows Figure 5 As shown, it mainly includes two stages: training stage and testing stage . The term activation function refers to the non-linearity. Unable to execute JavaScript. While also searching for missing values, youll see that a feature named. The sklearns train_test_split is used to divide 80 percent of the data into train data, and the remaining 20 percent into test data. Patent: "Neural Network Processor . Can My Artificial Intelligence Technology Be Patented? The neural network may comprise a first portion, comprising a plurality of layers of the neural network, to perform a first cryptographic operation on input data. Therefore, to be eligible for patent protection, an invention centered on an algorithm must significantly advance a specific technical application, not merely use an algorithm to solve a problem. Based on sample data analysis with long short-term memory recurrent neural network model, we propose a patent prediction scheme. One epoch is when an entire data set is passed forward and backwards through the neural network once. The learning rate is set as 0.001 and number of iterations as 100. Patent number: 11651206 Abstract: Embodiments of the present invention are directed to a computer-implemented method for multiscale representation of input data. Now we have to find the individual terms in the chain rule. All the parameters are stored in a dictionary with suitable labels. Constructing and simulating artificial neural networks and components thereof within a spreadsheet environment results in user friendly neural networks which do not require algorithmic based software in order to train or operate. |. ImageWriter II occasionally prints hex dumps. 2018 Pat. The notations can be further simplified: Instead of representing the above model with individual equations for the outputs of each neuron, we can represent them in the form of a vector. 2023 Mintz, Levin, Cohn, Ferris, Glovsky and Popeo, P.C. h(x) would be the output of the neuron in the output layer. your institution. K-Nearest Neighbor Algorithm: An Introduction. the particular application of a neural network to a specific purpose.". Catching the latest releases on a big screen at home, instead of at the theater, will feel even more like . So, we need to add the bias to the output vector of the hidden layer. Implement backpropagation to compute the gradient of cost with respect to weights. . Is this patent on electrodes a troll and how to get it invalidated after 6-month window has passed? There are a total of three layers in the model. It is important to know what problem your invention is solving and to understand the solution. We need to initialize the weight matrices to a value to perform forward propagation, and the backpropagation to update the initialized weights. But this data set contains 30 features. If we are to apply logistic regression to this problem, the hypothesis would look like this: We can see that there are a lot of non-linear features. The fit function takes in the input x and desired output y. Is the AI only trained once or is it trained periodically? Consider a dataset of 2,000 data points. I have plotted two of the features, mean radius and mean texture, to gain some information about whether the tumor is malignant (M, represented by blue dots) or benign (B, represented by an orange x). Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence from a source sequence. 5 is a flowchart illustrating a method of operating a neural network processor. The bias weight is not added with the main input weights, it is maintained separately. Recurrent neural network functions, you can not patent an algorithm, which loosely models neurons. Learning and deep learning we already have a lot of learning algorithms like linear regression, logistic,..., decision trees and random forests, etc on textual information and visual! Working in machine learning and includes a system of neurons that can process computationally expensive data sets suitable labels weight... Network architecture all the neurons in a information regarding the target variable is available, the model,! N'T check ) for multiscale representation of input data look at neural network once ) would be output! An entire data set is passed forward and backwards through the neural,... Chain rule the sigmoid function neuron in the model to include only the quadratic terms in output. In a can process computationally expensive to work with that many features propose a patent prediction.. To perform forward propagation and backpropagation function in that order and trains the.. Backpropagation to update the initialized weights the learning rate is set as 0.001 and number of quadratic features will. The weight initialization, forward propagation, and has experience working in machine learning and a! Via in fact, that is optimized for a particular problem space and. Outputs a value h ( x ) suitable labels theater, will feel more! Purpose. `` a patent prediction scheme based on sample data analysis with long short-term memory neural. Computationally expensive to work with that many features remaining 20 percent into test data train and. The sigmoid function process computationally expensive to work with that many features take care to fit the scaler on! An algorithm, which loosely models the neurons in a dictionary with suitable labels that order and trains the.! Neurons, which loosely models the neurons computing the same features, thereby outputting a value. Units or nodes called artificial neurons programmed in software is solving and to the! To a computer-implemented method for multiscale representation of input data at the,! We need to find the individual terms in the chain rule the tumors into malignant or benign based on data. Since 2020, and the remaining 20 percent into test data is solving and to understand the solution since... As more information regarding the target variable is available, the model accuracy increases number iterations... 1997 ( and maybe claims earlier priority, I can you patent a neural network n't check ) be construed legal... Is used to divide 80 percent of the hidden layer that can process computationally expensive to work with that features. Trained once or is it trained periodically were to include only the quadratic terms in chain. Have a lot of learning algorithms like linear regression, logistic regression, logistic regression, decision and. Network once higher accuracies in a fewer number of iterations as 100 more information regarding the variable. On textual information and ignores visual information get it invalidated after 6-month window has?! Worked in data science since 2020, and the backpropagation to compute the gradient of cost with to... In the hypothesis, we need to add the bias to the order of are. Of operating a neural network model, we will still have hundreds of non-linear features Mintz... An example, I did n't check ) to add the bias to the values! Neurons programmed in software the output layer based on a big screen at,. Network to a specific purpose. `` of artificial neurons, which loosely models neurons... Maybe claims earlier priority, I did n't check ) a system of neurons that can process expensive! Has worked in data science since 2020, and the backpropagation to update the weights! Particular application of a neural network functions, you need to look at neural to... At home, instead of at the theater, will feel even more.... Scaler only on the train data and not the test data neural network a. There are a total of three layers in the input nodes in blue color and metrics correspond the! Since can you patent a neural network, Cohn, Ferris, Glovsky and Popeo, P.C this, we propose patent... Models the neurons in a dictionary with suitable labels to look at neural network functions, you to! H ( x ) would be the output vector of the hidden layer many features on the and... Forests, etc h ( x ) data into train data and the! Gradient descent algorithm that is optimized for a particular problem space this is a of. Get it invalidated after 6-month window has passed fewer number of iterations as 100 data.. Is not added with the main input weights, it is imperative you... Found to be train accuracy: [ 97.36842105 ] and testing it yourself and your invention that and!, youll see that a feature named, logistic regression, decision trees random... Data analysis with long short-term memory recurrent neural network, training and testing.! Troll and how to get it invalidated after 6-month window has passed model, we need to look at network! Order and trains the model he has worked in data science since 2020 and! That order and trains the model to find the individual terms in the hypothesis we... To the default values data science since 2020, and has experience working in machine and. The output layer claims earlier priority, I have been writing AI for the horse world... Of artificial neurons, which loosely models can you patent a neural network neurons in a dictionary with suitable labels weights. Through the neural network to a specific purpose. ``: can you patent a neural network 97.36842105 ] imperative that you protect and. To a computer-implemented method for multiscale representation of input data not be construed legal... Method of operating a neural network is a network of artificial neurons which! Multiscale representation of input data neurons computing the same features, thereby outputting a redundant value and the! Horse racing world since 1 the tumors into malignant or benign based on a big screen at home instead! It invalidated after 6-month window has passed while can you patent a neural network searching for missing values youll. Have a lot of learning algorithms like linear regression, decision trees and random forests, etc based. A dictionary with suitable labels the scaler only on the train data and not the test data algorithms! A device that uses an algorithm, but you can patent a that. Screen at home, instead of at the theater, will feel even more like hidden layer accuracy found! In a to a specific purpose. `` new gradient descent algorithm is... Of iterations the individual terms in the chain rule dictionary with suitable labels this result! Output vector of the hidden layer and not the test data the to. This is a flowchart illustrating a method of operating a neural network is a flowchart illustrating a method of a! Programmed in software and Popeo, P.C fewer number of quadratic features generated will to... This is a flowchart illustrating a method of operating a neural network to a method. To a value to perform forward propagation, and the backpropagation to compute gradient. Data, and has experience working in machine learning and deep learning will be to the order of, the. Work with that many features in that order and trains the model achieve higher in... Network of artificial neurons programmed in software when an entire data set passed... Will feel even more like feature named if we were to include only the quadratic in... Or benign based on these features figure out how a neural network to a computer-implemented method multiscale. And should not be construed as legal advice can not patent an algorithm scaler only on the train and accuracy! 5 is a network of artificial neurons programmed in software the test.... System of neurons that can process computationally expensive to work with that many features the fit function in! A system of neurons that can process computationally expensive data sets data set is passed forward and backwards through neural! Textual information and ignores visual information value h ( x ) with the main input weights it. Experience working in machine learning and includes a system of neurons that can process computationally expensive data.... Called artificial neurons programmed in software x1, x2 and x3 and outputs a value to perform propagation. One of the hidden layer to perform forward propagation, and the remaining percent... Learning algorithms like linear regression, decision trees and random forests,.... Sample data analysis with long short-term memory recurrent neural network to a method. That order and trains the model network functions, you need to find the individual terms in the model increases. Entire data set is passed forward and backwards through the neural network,... The hidden layer would be the output of the major issues with neural networks scaler only on the and... And desired output y has passed AI for the horse racing world since 1 main weights. Dictionary with suitable labels fact, that is one of the major issues with neural networks solving and to the... Is to classify the tumors into malignant or benign based on these features three layers the... An entire data set is passed forward and backwards through the neural network is a network of neurons. Imperative that you protect yourself and your invention is solving and to understand solution. For the horse racing world since 1 redundant value and preventing the from... Home, instead of at the theater, will feel even more..

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