Patents by Inventor Abraham Samuel Heifets
Abraham Samuel Heifets 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: 11080570Abstract: Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.Type: GrantFiled: November 6, 2019Date of Patent: August 3, 2021Assignee: ATOMWISE INC.Inventors: Abraham Samuel Heifets, Izhar Wallach, Michael Dzamba
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Publication number: 20200334528Abstract: Systems and methods for classifying a test object are provided. For each respective target object in a plurality of target objects, a first procedure is performed comprising (a) posing the test object against the respective target thereby obtaining an interaction between the test and target, and (b) scoring the interaction with a first classifier. Each such score across the plurality of targets forms a test vector that is inputted into a second classifier thereby obtaining an indication of a target object. The second classifier is trained on training vectors, each being the output from instances of the first classifier after inputting a corresponding training object in a plurality of training objects in accordance with the first procedure. Each object in one subset of the training objects is uniquely associated with one of the targets. Another subset of the training objects is not associated with the targets.Type: ApplicationFiled: January 17, 2020Publication date: October 22, 2020Inventors: Abraham Samuel Heifets, Izhar Wallach, Kong Thong Nguyen
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Publication number: 20200320355Abstract: Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.Type: ApplicationFiled: November 6, 2019Publication date: October 8, 2020Inventors: Abraham Samuel Heifets, Izhar Wallach, Michael Dzamba
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Patent number: 10546237Abstract: Systems and methods for classifying a test object are provided. For each respective target object in a plurality of target objects, a first procedure is performed comprising (a) posing the test object against the respective target thereby obtaining an interaction between the test and target, and (b) scoring the interaction with a first classifier. Each such score across the plurality of targets forms a test vector that is inputted into a second classifier thereby obtaining an indication of a target object. The second classifier is trained on training vectors, each being the output from instances of the first classifier after inputting a corresponding training object in a plurality of training objects in accordance with the first procedure. Each object in one subset of the training objects is uniquely associated with one of the targets. Another subset of the training objects is not associated with the targets.Type: GrantFiled: March 30, 2017Date of Patent: January 28, 2020Assignee: Atomwise Inc.Inventors: Abraham Samuel Heifets, Izhar Wallach, Kong Thong Nguyen
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Patent number: 10482355Abstract: Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.Type: GrantFiled: June 18, 2018Date of Patent: November 19, 2019Assignee: Atomwise Inc.Inventors: Abraham Samuel Heifets, Izhar Wallach, Michael Dzamba
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Publication number: 20190164021Abstract: Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.Type: ApplicationFiled: June 18, 2018Publication date: May 30, 2019Inventors: Abraham Samuel Heifets, Izhar Wallach, Michael Dzamba
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Publication number: 20180285731Abstract: Systems and methods for classifying a test object are provided. For each respective target object in a plurality of target objects, a first procedure is performed comprising (a) posing the test object against the respective target thereby obtaining an interaction between the test and target, and (b) scoring the interaction with a first classifier. Each such score across the plurality of targets forms a test vector that is inputted into a second classifier thereby obtaining an indication of a target object. The second classifier is trained on training vectors, each being the output from instances of the first classifier after inputting a corresponding training object in a plurality of training objects in accordance with the first procedure. Each object in one subset of the training objects is uniquely associated with one of the targets. Another subset of the training objects is not associated with the targets.Type: ApplicationFiled: March 30, 2017Publication date: October 4, 2018Inventors: Abraham Samuel Heifets, Izhar Wallach, Kong Thong Nguyen
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Patent number: 10002312Abstract: Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.Type: GrantFiled: June 20, 2016Date of Patent: June 19, 2018Assignee: Atomwise Inc.Inventors: Abraham Samuel Heifets, Izhar Wallach, Michael Dzamba
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Publication number: 20160300127Abstract: Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.Type: ApplicationFiled: June 20, 2016Publication date: October 13, 2016Inventors: Abraham Samuel Heifets, Izhar Wallach, Michael Dzamba
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Publication number: 20160196480Abstract: Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.Type: ApplicationFiled: February 23, 2016Publication date: July 7, 2016Inventors: Abraham Samuel Heifets, Izhar Wallach, Michael Dzamba
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Patent number: 9373059Abstract: Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.Type: GrantFiled: February 23, 2016Date of Patent: June 21, 2016Assignee: ATOMWISE INC.Inventors: Abraham Samuel Heifets, Izhar Wallach, Michael Dzamba