Patents by Inventor Felix Schuermann
Felix Schuermann has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11972343Abstract: A method that is implemented by one or more data processing devices can include receiving a training set that includes a plurality of representations of topological structures in patterns of activity in a source neural network and training a neural network using the representations either as an input to the neural network or as a target answer vector. The activity is responsive to an input into the source neural network.Type: GrantFiled: June 11, 2018Date of Patent: April 30, 2024Assignee: INAIT SAInventors: Henry Markram, Ran Levi, Kathryn Pamela Hess Bellwald, Felix Schuermann
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Publication number: 20240111994Abstract: Application of the output from a recurrent artificial neural network to a variety of different applications. A method can include identifying topological patterns of activity in a recurrent artificial neural network, outputting a collection of digits, and inputting a first digit of the collection to a first application that is designed to fulfil a first purpose and to a second application that is designed to fulfil a second purpose, wherein the first purpose differs from the second purpose. The topological patterns are responsive to an input of data into the recurrent artificial neural network and each topological pattern abstracts a characteristic of the input data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.Type: ApplicationFiled: October 16, 2023Publication date: April 4, 2024Inventors: Henry Markram, Felix Schürmann, Fabien Jonathan Delalondre, Daniel Milan Lütgehetmann, John Rahmon, Constantin Cosmin Atanasoaei, Michele De Gruttola
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Publication number: 20240046077Abstract: Abstracting data that originates from different sensors and transducers using artificial neural networks. A method can include identifying topological patterns of activity in a recurrent artificial neural network and outputting a collection of digits. The topological patterns are responsive to an input, into the recurrent artificial neural network, of first data originating from a first sensor and second data originating from a second sensor. Each topological pattern abstracts a characteristic shared by the first data and the second data. The first and second sensors sense different data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.Type: ApplicationFiled: October 20, 2023Publication date: February 8, 2024Inventors: Henry Markram, Felix Schürmann, Fabien Jonathan Delalondre, Daniel Milan Lütgehetmann, John Rahmon
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Patent number: 11893471Abstract: In one implementation, a method is implemented by a neural network device and includes inputting a representation of topological structures in patterns of activity in a source neural network, wherein the activity is responsive to an input into the source neural network, processing the representation, and outputting a result of the processing of the representation. The processing is consistent with a training of the neural network to process different such representations of topological structures in patterns of activity in the source neural network.Type: GrantFiled: June 11, 2018Date of Patent: February 6, 2024Assignee: INAIT SAInventors: Henry Markram, Ran Levi, Kathryn Pamela Hess Bellwald, Felix Schuermann
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Publication number: 20230370244Abstract: Methods, systems, and devices for homomorphic encryption. In one implementation, the methods include inputting first data into a recurrent artificial neural network, identifying patterns of activity in the recurrent artificial neural network that are responsive to the input of the secure data, storing second data representing whether the identified patterns of activity comports with topological patterns, and statistically analyzing the second data to draw conclusions about the first data.Type: ApplicationFiled: April 5, 2023Publication date: November 16, 2023Inventors: Henry Markram, Felix Schuermann, Kathryn Hess Bellwald, Fabien Delalondre
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Patent number: 11816553Abstract: Application of the output from a recurrent artificial neural network to a variety of different applications. A method can include identifying topological patterns of activity in a recurrent artificial neural network, outputting a collection of digits, and inputting a first digit of the collection to a first application that is designed to fulfil a first purpose and to a second application that is designed to fulfil a second purpose, wherein the first purpose differs from the second purpose. The topological patterns are responsive to an input of data into the recurrent artificial neural network and each topological pattern abstracts a characteristic of the input data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.Type: GrantFiled: December 11, 2019Date of Patent: November 14, 2023Assignee: INAIT SAInventors: Henry Markram, Felix Schürmann, Fabien Jonathan Delalondre, Daniel Milan Lütgehetmann, John Rahmon, Constantin Cosmin Atanasoaei, Michele De Gruttola
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Patent number: 11817220Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reconstructing and simulating neocortical microcircuitry. In one aspect, a method includes providing a model of neural tissue, the model including different types of neural cells and dynamic synaptic interconnections between the neural cells, changing a parameter in the model; and identifying a change in a computational state of the model of the neural tissue responsive to the change in the parameter. The change in the parameter can, e.g., change behavior of neural cells of at least one type, change interconnectivity between neural cells, or target a location within a volume in the model that interacts with multiple types of neural cells.Type: GrantFiled: October 10, 2017Date of Patent: November 14, 2023Assignee: ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE (EPFL)Inventors: Henry Markram, Eilif Benjamin Muller, Sean Lewis Hill, Felix Schuermann
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Patent number: 11797827Abstract: Abstracting data that originates from different sensors and transducers using artificial neural networks. A method can include identifying topological patterns of activity in a recurrent artificial neural network and outputting a collection of digits. The topological patterns are responsive to an input, into the recurrent artificial neural network, of first data originating from a first sensor and second data originating from a second sensor. Each topological pattern abstracts a characteristic shared by the first data and the second data. The first and second sensors sense different data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.Type: GrantFiled: December 11, 2019Date of Patent: October 24, 2023Assignee: INAIT SAInventors: Henry Markram, Felix Schürmann, Fabien Jonathan Delalondre, Daniel Milan Lütgehetmann, John Rahmon
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Publication number: 20230316077Abstract: A method includes defining a plurality of different windows of time in a recurrent artificial neural network, wherein each of the different windows has different durations, has different start times, or has both different durations and different start times, identifying occurrences of topological patterns of activity in the recurrent artificial neural network in the different windows of time, comparing the occurrences of the topological patterns of activity in the different windows, and classifying, based on a result of the comparison, a first decision that is represented by a first topological pattern of activity that occurs in a first of the windows as less robust than a second decision that is represented by a second topological pattern of activity that occurs in a second of the windows.Type: ApplicationFiled: April 5, 2023Publication date: October 5, 2023Inventors: Henry Markram, Felix Schuermann, John Rahmon, Daniel Milan Lütgehetmann, Constantin Cosmin Atanasoaei
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Publication number: 20230206493Abstract: A method of image processing can include receiving an image of an instance of an object and a three-dimensional model of the object, detecting a first plurality of landmarks of the instance of the object in the two-dimensional image, estimating a pose of the instance of the object in the received image relative to an imaging device that acquired the image, wherein the relative pose in the received image is estimated from the first plurality of the detected landmarks, using the estimated relative pose, projecting landmarks from the three-dimensional model of the object into a dimensional space of the received image of the instance of the object, comparing, in the dimensional space, characteristics of corresponding of the projected landmarks and the first plurality of the detected landmarks, and determining whether a threshold level of positional correspondence exists between positions of corresponding of the projected landmarks and the first plurality of the detected landmarks.Type: ApplicationFiled: March 14, 2022Publication date: June 29, 2023Inventors: Daniel Milan Lütgehetmann, Dimitri Zaganidis, Nicolas Alain Berger, Felix Schürmann
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Patent number: 11651210Abstract: A method includes defining a plurality of different windows of time in a recurrent artificial neural network, wherein each of the different windows has different durations, has different start times, or has both different durations and different start times, identifying occurrences of topological patterns of activity in the recurrent artificial neural network in the different windows of time, comparing the occurrences of the topological patterns of activity in the different windows, and classifying, based on a result of the comparison, a first decision that is represented by a first topological pattern of activity that occurs in a first of the windows as less robust than a second decision that is represented by a second topological pattern of activity that occurs in a second of the windows.Type: GrantFiled: December 11, 2019Date of Patent: May 16, 2023Assignee: INAIT SAInventors: Henry Markram, Felix Schürmann, John Rahmon, Daniel Milan Lütgehetmann, Constantin Cosmin Atanasoaei
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Patent number: 11652603Abstract: Methods, systems, and devices for homomorphic encryption. In one implementation, the methods include inputting first data into a recurrent artificial neural network, identifying patterns of activity in the recurrent artificial neural network that are responsive to the input of the secure data, storing second data representing whether the identified patterns of activity comports with topological patterns, and statistically analyzing the second data to draw conclusions about the first data.Type: GrantFiled: March 18, 2019Date of Patent: May 16, 2023Assignee: INAIT SAInventors: Henry Markram, Felix Schuermann, Kathryn Hess, Fabien Delalondre
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Publication number: 20230085384Abstract: A method for characterizing correctness of image processing includes receiving images of an instance of an object, wherein the images were acquired at different relative poses, identifying positions of corresponding landmarks on the object in each of the received images, receiving information characterizing a difference in position or a difference in orientation of at least one of the instance of the object and one or more imaging devices when the images were acquired, transferring the positions of landmarks identified in a first of the images based on the difference in position or the difference in orientation, and comparing the positions of the transferred landmarks with the positions of the corresponding of the landmarks identified in a second of the received images, and characterizing a correctness of the identification of the positions of the landmarks in at least one of the received images.Type: ApplicationFiled: November 10, 2021Publication date: March 16, 2023Inventors: Daniel Milan Lütgehetmann, Dimitri Zaganidis, Nicolas Alain Berger, Felix Schürmann
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Patent number: 11580401Abstract: Distance metrics and clustering in recurrent neural networks. For example, a method includes determining whether topological patterns of activity in a collection of topological patterns occur in a recurrent artificial neural network in response to input of first data into the recurrent artificial neural network, and determining a distance between the first data and either second data or a reference based on the topological patterns of activity that are determined to occur in response to the input of the first data.Type: GrantFiled: December 11, 2019Date of Patent: February 14, 2023Inventors: Henry Markram, Felix Schürmann, Fabien Jonathan Delalondre, Ran Levi, Kathryn Pamela Hess Bellwald, John Rahmon
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Publication number: 20210182654Abstract: Abstracting data that originates from different sensors and transducers using artificial neural networks. A method can include identifying topological patterns of activity in a recurrent artificial neural network and outputting a collection of digits. The topological patterns are responsive to an input, into the recurrent artificial neural network, of first data originating from a first sensor and second data originating from a second sensor. Each topological pattern abstracts a characteristic shared by the first data and the second data. The first and second sensors sense different data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.Type: ApplicationFiled: December 11, 2019Publication date: June 17, 2021Inventors: Henry Markram, Felix Schürmann, Fabien Jonathan Delalondre, Daniel Milan Lütgehetmann, John Rahmon
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Publication number: 20210182657Abstract: A method includes defining a plurality of different windows of time in a recurrent artificial neural network, wherein each of the different windows has different durations, has different start times, or has both different durations and different start times, identifying occurrences of topological patterns of activity in the recurrent artificial neural network in the different windows of time, comparing the occurrences of the topological patterns of activity in the different windows, and classifying, based on a result of the comparison, a first decision that is represented by a first topological pattern of activity that occurs in a first of the windows as less robust than a second decision that is represented by a second topological pattern of activity that occurs in a second of the windows.Type: ApplicationFiled: December 11, 2019Publication date: June 17, 2021Inventors: Henry Markram, Felix Schürmann, John Rahmon, Daniel Milan Lütgehetmann, Constantin Cosmin Atanasoaei
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Publication number: 20210182653Abstract: Application of the output from a recurrent artificial neural network to a variety of different applications. A method can include identifying topological patterns of activity in a recurrent artificial neural network, outputting a collection of digits, and inputting a first digit of the collection to a first application that is designed to fulfil a first purpose and to a second application that is designed to fulfil a second purpose, wherein the first purpose differs from the second purpose. The topological patterns are responsive to an input of data into the recurrent artificial neural network and each topological pattern abstracts a characteristic of the input data. Each digit represents whether one of the topological patterns of activity has been identified in the artificial neural network.Type: ApplicationFiled: December 11, 2019Publication date: June 17, 2021Inventors: Henry Markram, Felix Schürmann, Fabien Jonathan Delalondre, Daniel Milan Lütgehetmann, John Rahmon
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Publication number: 20210182681Abstract: Distance metrics and clustering in recurrent neural networks. For example, a method includes determining whether topological patterns of activity in a collection of topological patterns occur in a recurrent artificial neural network in response to input of first data into the recurrent artificial neural network, and determining a distance between the first data and either second data or a reference based on the topological patterns of activity that are determined to occur in response to the input of the first data.Type: ApplicationFiled: December 11, 2019Publication date: June 17, 2021Inventors: Henry Markram, Felix Schürmann, Fabien Jonathan Delalondre, Ran Levi, Kathryn Pamela Hess Bellwald, John Rahmon
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Publication number: 20210182655Abstract: Robust recurrent artificial neural networks and techniques for improving the robustness of recurrent artificial neural networks. For example, a system can include a plurality of nodes and links arranged in a recurrent neural network, wherein either transmissions of information along the links or decisions at the nodes are non-deterministic, and an output configured to output indications of occurrences of topological patterns of activity in the recurrent artificial neural network.Type: ApplicationFiled: December 11, 2019Publication date: June 17, 2021Inventors: Henry Markram, Felix Schürmann, Daniel Milan Lütgehetmann, John Rahmon
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Publication number: 20200304284Abstract: Methods, systems, and devices for homomorphic encryption. In one implementation, the methods include inputting first data into a recurrent artificial neural network, identifying patterns of activity in the recurrent artificial neural network that are responsive to the input of the secure data, storing second data representing whether the identified patterns of activity comports with topological patterns, and statistically analyzing the second data to draw conclusions about the first data.Type: ApplicationFiled: March 18, 2019Publication date: September 24, 2020Inventors: Henry Markram, Felix Schuermann, Kathryn Hess, Fabien Delalondre