Patents by Inventor Nils Yannick Hammerla
Nils Yannick Hammerla 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: 11321363Abstract: A graphical classification method for classifying graphical structures, said graphical structures comprising nodes defined by feature vectors and having relations between the nodes. The method includes representing the feature vectors and relations as a first graphical representation. The method also includes mapping said first graphical representation into a second graphical representation wherein the mapping comprises using an attention mechanism, said attention mechanism establishes the importance of specific feature vectors dependent on their neighbourhood and the relations between the feature vectors, said mapping transforming the feature vectors of the first graphical representation to transformed feature vectors in the second graphical representation. The method also includes combining the transformed feature vectors to obtain a third combined representation said third combined representation being an indication of the classification of the graphical structure.Type: GrantFiled: April 4, 2019Date of Patent: May 3, 2022Assignee: Babylon Partners LimitedInventors: Daniel William Busbridge, Pietro Cavallo, Dane Grant Sherburn, Nils Yannick Hammerla
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Publication number: 20210233658Abstract: A computer-implemented method for medical diagnosis, comprising: receiving a user input from a user, the user input comprising an input symptom; determining a measure of relevance of a plurality of items of medical data to the user input, wherein the plurality of items of medical data are items of medical data for which information associated with the user is stored; determining whether to include the stored information corresponding to an item of medical data in a first set of information, based on the measure of relevance for the item of medical data; providing the user input and the first set of information as an input to a model, the model being configured to output a probability of the user having a disease; and outputting a diagnosis based on the probability of the user having a disease.Type: ApplicationFiled: January 23, 2020Publication date: July 29, 2021Inventors: Camille Van Assel, Domenico Corapi, Maurizio Morriello, Miklos Kepes, Joshua Samuel Nathan Levy-Kramer, Nils Yannick Hammerla
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Patent number: 10824653Abstract: A computer implemented method for classifying molecular structures is provided. The method includes representing the elements and atoms in a molecular structure as nodes and the bonds as relations as a first graphical representation. The method also includes mapping said first graphical representation into a second graphical representation wherein the mapping comprises using an attention mechanism, said attention mechanism establishes the importance of specific feature vectors dependent on their neighbourhood and the relations between the feature vectors, said mapping transforming the feature vectors of the first graphical representation to transformed feature vectors in the second graphical representation. The method also includes combining the transformed feature vectors to obtain a third combined representation. The method also includes mapping said third combined representation to a feature vector indicating properties of the molecular structure.Type: GrantFiled: April 4, 2019Date of Patent: November 3, 2020Assignee: Babylon Partners LimitedInventors: Daniel William Busbridge, Pietro Cavallo, Dane Grant Sherburn, Nils Yannick Hammerla
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Patent number: 10824949Abstract: A method of training a model, said model being adapted to map a first graphical data structure representation to a second graphical data structure representation, the first graphical data structure representation comprising nodes, with at least one of a plurality of relations between said nodes, the second graphical data structure representation comprising nodes, the mapping comprises using an attention mechanism, wherein said attention mechanism establishes the importance of specific nodes dependent on their neighbourhood and the relations between the nodes, wherein the mapping comprising using a projection kernel to map the nodes of the first graphical structure to nodes of an intermediate representation and using an attention kernel to enact the attention mechanism. The method includes receiving a training data set comprising an output layer and a corresponding input layer. The method also includes training the parameters of the projection kernel and the attention kernel using the training data set.Type: GrantFiled: April 4, 2019Date of Patent: November 3, 2020Assignee: Babylon Partners LimitedInventors: Daniel William Busbridge, Pietro Cavallo, Dane Grant Sherburn, Nils Yannick Hammerla
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Publication number: 20200104409Abstract: A method of mapping a first graphical data structure representation to a second graphical data structure representation, the first graphical data structure representation comprising nodes, with at least one of a plurality of relations between said nodes, the second graphical data structure representation comprising nodes, the mapping comprises using an attention mechanism, wherein said attention mechanism establishes the importance of specific nodes dependent on their neighbourhood and the relations between the nodes.Type: ApplicationFiled: September 27, 2018Publication date: April 2, 2020Inventors: Daniel William BUSBRIDGE, Pietro CAVALLO, Dane Grant SHERBURN, Nils Yannick HAMMERLA
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Publication number: 20200104729Abstract: A method of training a model, said model being adapted to map a first graphical data structure representation to a second graphical data structure representation, the first graphical data structure representation comprising nodes, with at least one of a plurality of relations between said nodes, the second graphical data structure representation comprising nodes, the mapping comprises using an attention mechanism, wherein said attention mechanism establishes the importance of specific nodes dependent on their neighbourhood and the relations between the nodes, wherein the mapping comprising using a projection kernel to map the nodes of the first graphical structure to nodes of an intermediate representation and using an attention kernel to enact the attention mechanism. The method includes receiving a training data set comprising an output layer and a corresponding input layer. The method also includes training the parameters of the projection kernel and the attention kernel using the training data set.Type: ApplicationFiled: April 4, 2019Publication date: April 2, 2020Inventors: Daniel William BUSBRIDGE, Pietro CAVALLO, Dane Grant SHERBURN, Nils Yannick HAMMERLA
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Publication number: 20200104311Abstract: A computer implemented method for classifying molecular structures is provided. The method includes representing the elements and atoms in a molecular structure as nodes and the bonds as relations as a first graphical representation. The method also includes mapping said first graphical representation into a second graphical representation wherein the mapping comprises using an attention mechanism, said attention mechanism establishes the importance of specific feature vectors dependent on their neighbourhood and the relations between the feature vectors, said mapping transforming the feature vectors of the first graphical representation to transformed feature vectors in the second graphical representation. The method also includes combining the transformed feature vectors to obtain a third combined representation. The method also includes mapping said third combined representation to a feature vector indicating properties of the molecular structure.Type: ApplicationFiled: April 4, 2019Publication date: April 2, 2020Inventors: Daniel William BUSBRIDGE, Pietro CAVALLO, Dane Grant SHERBURN, Nils Yannick HAMMERLA
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Publication number: 20200104312Abstract: A graphical classification method for classifying graphical structures, said graphical structures comprising nodes defined by feature vectors and having relations between the nodes. The method includes representing the feature vectors and relations as a first graphical representation. The method also includes mapping said first graphical representation into a second graphical representation wherein the mapping comprises using an attention mechanism, said attention mechanism establishes the importance of specific feature vectors dependent on their neighbourhood and the relations between the feature vectors, said mapping transforming the feature vectors of the first graphical representation to transformed feature vectors in the second graphical representation. The method also includes combining the transformed feature vectors to obtain a third combined representation said third combined representation being an indication of the classification of the graphical structure.Type: ApplicationFiled: April 4, 2019Publication date: April 2, 2020Inventors: Daniel William BUSBRIDGE, Pietro CAVALLO, Dane Grant SHERBURN, Nils Yannick HAMMERLA
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Patent number: 10599686Abstract: A method of mapping a first graphical data structure representation to a second graphical data structure representation, the first graphical data structure representation comprising nodes, with at least one of a plurality of relations between said nodes, the second graphical data structure representation comprising nodes, the mapping comprises using an attention mechanism, wherein said attention mechanism establishes the importance of specific nodes dependent on their neighbourhood and the relations between the nodes.Type: GrantFiled: September 27, 2018Date of Patent: March 24, 2020Assignee: Babylon Partners LimitedInventors: Daniel William Busbridge, Pietro Cavallo, Dane Grant Sherburn, Nils Yannick Hammerla
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Patent number: 10482183Abstract: A computer-implemented method comprising: receiving the first set of words and the second set of words, wherein each of the first and second sets of words; calculating a first likelihood-based measure representing how well a first model can be fit to the first and second sets of words, the first model comprising a shared parametric distribution representing both the first and second sets of words; calculating a second likelihood-based measure representing how well a second model can be fit to the first and second sets of words, the second model comprising a first parametric distribution representing the first set of words and a second parametric distribution representing the second set of words; calculating a similarity score based on a ratio of the first likelihood measure to the second likelihood measure, the similarity score being representative of the similarity between the first and second sets of words; and outputting the similarity score.Type: GrantFiled: September 27, 2018Date of Patent: November 19, 2019Assignee: Babylon Partners LimitedInventors: Francisco Vargas, Kamen Brestnichki, Dane Grant Sherburn, Vitalii Zhelezniak, Nils Yannick Hammerla