Patents by Inventor Felipe Petroski SUCH
Felipe Petroski SUCH 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: 11922318Abstract: Embodiments of the present disclosure include a method that obtains a digital image. The method includes extracting a word block from the digital image. The method includes processing the word block by evaluating a value of the word block against a dictionary. The method includes outputting a prediction equal to a common word in the dictionary when a confidence factor is greater than a predetermined threshold. The method includes processing the word block and assigning a descriptor to the word block corresponding to a property of the word block. The method includes processing the word block using the descriptor to prioritize evaluation of the word block. The method includes concatenating a first output and a second output. The method includes predicting a value of the word block.Type: GrantFiled: October 20, 2020Date of Patent: March 5, 2024Assignee: KODAK ALARIS, INC.Inventors: Felipe Petroski Such, Raymond Ptucha, Frank Brockler, Paul Hutkowski
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Patent number: 11907675Abstract: A generative cooperative network (GCN) comprises a dataset generator model and a learner model. The dataset generator model generates training datasets used to train the learner model. The trained learner model is evaluated according to a reference training dataset. The dataset generator model is modified according to the evaluation. The training datasets, the dataset generator model, and the leaner model are stored by the GCN. The trained learner model is configured to receive input and to generate output based on the input.Type: GrantFiled: January 17, 2020Date of Patent: February 20, 2024Assignee: Uber Technologies, Inc.Inventors: Felipe Petroski Such, Aditya Rawal, Joel Anthony Lehman, Kenneth Owen Stanley, Jeffrey Michael Clune
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Patent number: 11715014Abstract: Embodiments of the present disclosure include a method that obtains a digital image. The method includes extracting a word block from the digital image. The method includes processing the word block by evaluating a value of the word block against a dictionary. The method includes outputting a prediction equal to a common word in the dictionary when a confidence factor is greater than a predetermined threshold. The method includes processing the word block and assigning a descriptor to the word block corresponding to a property of the word block. The method includes processing the word block using the descriptor to prioritize evaluation of the word block. The method includes concatenating a first output and a second output. The method includes predicting a value of the word block.Type: GrantFiled: October 20, 2020Date of Patent: August 1, 2023Assignee: KODAK ALARIS INC.Inventors: Felipe Petroski Such, Raymond Ptucha, Frank Brockler, Paul Hutkowski
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Patent number: 11068787Abstract: Systems and methods are disclosed herein for selecting a parameter vector from a set of parameter vectors for a neural network and generating a plurality of copies of the parameter vector. The systems and methods generate a plurality of modified parameter vectors by perturbing each copy of the parameter vector with a different perturbation seed, and determine, for each respective modified parameter vector, a respective measure of novelty. The systems and methods determine an optimal new parameter vector based on each respective measure of novelty for each respective one of the plurality of modified parameter vectors, and determine behavior characteristics of the new parameter vector. The systems and methods store the behavior characteristics of the new parameter vector in an archive.Type: GrantFiled: December 14, 2018Date of Patent: July 20, 2021Assignee: Uber Technologies, Inc.Inventors: Edoardo Conti, Vashisht Madhavan, Jeffrey Michael Clune, Felipe Petroski Such, Joel Anthony Lehman, Kenneth Owen Stanley
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Patent number: 10936862Abstract: Embodiments of the present disclosure include a method for extracting symbols from a digitized object. The method includes processing the word block against a dictionary. The method includes comparing the word block against a word in the dictionary, the comparison providing a confidence factor. The method includes outputting a prediction equal to the word when the confidence factor is greater than a predetermined threshold. The method includes evaluating properties of the word block when the confidence factor is less than the predetermined threshold. The method includes predicting a value of the word block based on the properties of the word block. The method further includes determining an error rate for the predicted value of the word block. The method includes outputting a value for the word block, the output equal to a calculated value corresponding to a value of the word block having the lowest error rate.Type: GrantFiled: September 19, 2017Date of Patent: March 2, 2021Assignee: Kodak Alaris Inc.Inventors: Felipe Petroski Such, Raymond Ptucha, Frank Brockler, Paul Hutkowski, Vatsala Singh
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Publication number: 20210034849Abstract: Embodiments of the present disclosure include a method that obtains a digital image. The method includes extracting a word block from the digital image. The method includes processing the word block by evaluating a value of the word block against a dictionary. The method includes outputting a prediction equal to a common word in the dictionary when a confidence factor is greater than a predetermined threshold. The method includes processing the word block and assigning a descriptor to the word block corresponding to a property of the word block. The method includes processing the word block using the descriptor to prioritize evaluation of the word block. The method includes concatenating a first output and a second output. The method includes predicting a value of the word block.Type: ApplicationFiled: October 20, 2020Publication date: February 4, 2021Applicant: Kodak Alaris Inc.Inventors: Felipe Petroski SUCH, Raymond PTUCHA, Frank BROCKLER, Paul HUTKOWSKI
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Publication number: 20210034850Abstract: Embodiments of the present disclosure include a method that obtains a digital image. The method includes extracting a word block from the digital image. The method includes processing the word block by evaluating a value of the word block against a dictionary. The method includes outputting a prediction equal to a common word in the dictionary when a confidence factor is greater than a predetermined threshold. The method includes processing the word block and assigning a descriptor to the word block corresponding to a property of the word block. The method includes processing the word block using the descriptor to prioritize evaluation of the word block. The method includes concatenating a first output and a second output. The method includes predicting a value of the word block.Type: ApplicationFiled: October 20, 2020Publication date: February 4, 2021Applicant: Kodak Alaris Inc.Inventors: Felipe Petroski SUCH, Raymond PTUCHA, Frank BROCKLER, Paul HUTKOWSKI
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Publication number: 20200372410Abstract: A machine learning model for reinforcement learning uses parameterized families of Markov decision processes (MDP) with latent variables. The system uses latent variables to improve ability of models to transfer knowledge and generalize to new tasks. Accordingly, trained machine learning based models are able to work in unseen environments or combinations of conditions/factors that the machine learning model was never trained on. For example, robots or self-driving vehicles based on the machine learning based models are robust to changing goals and are able to adapt to novel reward functions or tasks flexibly while being able to transfer knowledge about environments and agents to new tasks.Type: ApplicationFiled: May 22, 2020Publication date: November 26, 2020Inventors: Theofanis Karaletsos, Felipe Petroski Such, Christian Francisco Perez
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Patent number: 10846523Abstract: Embodiments of the present disclosure include a method that obtains a digital image. The method includes extracting a word block from the digital image. The method includes processing the word block by evaluating a value of the word block against a dictionary. The method includes outputting a prediction equal to a common word in the dictionary when a confidence factor is greater than a predetermined threshold. The method includes processing the word block and assigning a descriptor to the word block corresponding to a property of the word block. The method includes processing the word block using the descriptor to prioritize evaluation of the word block. The method includes concatenating a first output and a second output. The method includes predicting a value of the word block.Type: GrantFiled: November 14, 2017Date of Patent: November 24, 2020Assignee: KODAK ALARIS INC.Inventors: Felipe Petroski Such, Raymond Ptucha, Frank Brockler, Paul Hutkowski
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Publication number: 20200234144Abstract: A generative cooperative network (GCN) comprises a dataset generator model and a learner model. The dataset generator model generates training datasets used to train the learner model. The trained learner model is evaluated according to a reference training dataset. The dataset generator model is modified according to the evaluation. The training datasets, the dataset generator model, and the leaner model are stored by the GCN. The trained learner model is configured to receive input and to generate output based on the input.Type: ApplicationFiled: January 17, 2020Publication date: July 23, 2020Inventors: Felipe Petroski Such, Aditya Rawal, Joel Anthony Lehman, Kenneth Owen Stanley, Jeffrey Michael Clune
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Patent number: 10599975Abstract: A source system initializes, using an initialization seed, a first parameter vector representing weights of a neural network. The source system determines a second parameter vector by performing a sequence of mutations on the first parameter vector, the mutations each being based on a perturbation seed. The source system generates, and stores to memory, an encoded representation of the second parameter vector that comprises the initialization seed and a sequence of perturbation seeds corresponding to the sequence of mutations. The source system transmits the data structure to a target system, which processes a neural network based on the data structure.Type: GrantFiled: December 14, 2018Date of Patent: March 24, 2020Assignee: Uber Technologies, Inc.Inventors: Felipe Petroski Such, Jeffrey Michael Clune, Kenneth Owen Stanley, Edoardo Conti, Vashisht Madhavan, Joel Anthony Lehman
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Publication number: 20190188571Abstract: Systems and methods are disclosed herein for selecting a parameter vector from a set of parameter vectors for a neural network and generating a plurality of copies of the parameter vector. The systems and methods generate a plurality of modified parameter vectors by perturbing each copy of the parameter vector with a different perturbation seed, and determine, for each respective modified parameter vector, a respective measure of novelty. The systems and methods determine an optimal new parameter vector based on each respective measure of novelty for each respective one of the plurality of modified parameter vectors, and determine behavior characteristics of the new parameter vector. The systems and methods store the behavior characteristics of the new parameter vector in an archive.Type: ApplicationFiled: December 14, 2018Publication date: June 20, 2019Inventors: Edoardo Conti, Vashisht Madhavan, Jeffrey Michael Clune, Felipe Petroski Such, Joel Anthony Lehman, Kenneth Owen Stanley
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Publication number: 20190188553Abstract: A source system initializes, using an initialization seed, a first parameter vector representing weights of a neural network. The source system determines a second parameter vector by performing a sequence of mutations on the first parameter vector, the mutations each being based on a perturbation seed. The source system generates, and stores to memory, an encoded representation of the second parameter vector that comprises the initialization seed and a sequence of perturbation seeds corresponding to the sequence of mutations. The source system transmits the data structure to a target system, which processes a neural network based on the data structure.Type: ApplicationFiled: December 14, 2018Publication date: June 20, 2019Inventors: Felipe Petroski Such, Jeffrey Michael Clune, Kenneth Owen Stanley, Edoardo Conti, Vashisht Madhavan, Joel Anthony Lehman
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Publication number: 20180137350Abstract: Embodiments of the present disclosure include a method that obtains a digital image. The method includes extracting a word block from the digital image. The method includes processing the word block by evaluating a value of the word block against a dictionary. The method includes outputting a prediction equal to a common word in the dictionary when a confidence factor is greater than a predetermined threshold. The method includes processing the word block and assigning a descriptor to the word block corresponding to a property of the word block. The method includes processing the word block using the descriptor to prioritize evaluation of the word block. The method includes concatenating a first output and a second output. The method includes predicting a value of the word block.Type: ApplicationFiled: November 14, 2017Publication date: May 17, 2018Applicant: KODAK ALARIS INC.Inventors: Felipe Petroski SUCH, Raymond PTUCHA, Frank BROCKLER, Paul HUTKOWSKI
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Publication number: 20180137349Abstract: Embodiments of the present disclosure include a method for extracting symbols from a digitized object. The method includes processing the word block against a dictionary. The method includes comparing the word block against a word in the dictionary, the comparison providing a confidence factor. The method includes outputting a prediction equal to the word when the confidence factor is greater than a predetermined threshold. The method includes evaluating properties of the word block when the confidence factor is less than the predetermined threshold. The method includes predicting a value of the word block based on the properties of the word block. The method further includes determining an error rate for the predicted value of the word block. The method includes outputting a value for the word block, the output equal to a calculated value corresponding to a value of the word block having the lowest error rate.Type: ApplicationFiled: September 19, 2017Publication date: May 17, 2018Applicant: Kodak Alaris Inc.Inventors: Felipe Petroski SUCH, Raymond Ptucha, Frank Brockler, Paul Hutkowski, Vatsala Singh