Patents by Inventor Thomas da Silva Paula
Thomas da Silva Paula 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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Publication number: 20230409624Abstract: Systems, methods, and corresponding computer-readable media for multi-modal hierarchical semantic search of electronic documents. Systems in accordance with the disclosure comprise search engine controllers, any desired numbers of search engine interface controllers, and persistent data stores accessible by the search engine controllers. The controllers are configured to parse searchable document data sets to extract document object data sets. Each document object data set can represent textual and/or image feature of the document. For each document object data sets the controller generates at least one enriched document object data set, and forms at least one of a document graph or index. In response to search requests, the search controller compares document graphs or indexes to identify the most similar documents.Type: ApplicationFiled: October 30, 2020Publication date: December 21, 2023Applicant: Hewlett-Packard Development Company, L.P.Inventors: Thomas da Silva Paula, Juliano Cardoso Vacaro, Wagston Tassoni Staehler, David Murphy
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Publication number: 20230051713Abstract: Examples of neural networks trained for multiple tasks are described herein. In some examples, a method may include determining a feature vector using a first portion of a neural network. In some examples, the neural network is trained for multiple tasks. Some examples of the method may include transmitting the feature vector to a remote device. In some examples, the remote device is to perform one of the multiple tasks using a second portion of the neural network.Type: ApplicationFiled: February 12, 2020Publication date: February 16, 2023Applicant: Hewlett-Packard Development Company, L.P.Inventors: Thomas da Silva Paula, David Murphy, Wagston Tassoni Staehler, Juliano Cardoso Vacaro
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Publication number: 20230048206Abstract: Examples of methods for controlling machine learning model structures are described herein. In some examples, a method includes controlling a machine learning model structure. In some examples, the machine learning model structure may be controlled based on an environmental condition. In some examples, the machine learning model structure may be controlled to control apparatus power consumption associated with a processing load of the machine learning model structure.Type: ApplicationFiled: February 6, 2020Publication date: February 16, 2023Applicant: Hewlett-Packard Development Company, L.P.Inventors: Madhu Sudan Athreya, Manu Rastogi, M. Anthony Lewis, Thomas da Silva Paula
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Patent number: 11483370Abstract: A method includes receiving, with a computing device, a configuration file and a client request to apply a machine learning model to a set of data from a sensor. The method includes performing, with the computing device, preprocessing on the set of data from the sensor based on the configuration file to generate preprocessed data. The method includes sending, with the computing device, a call to a model server to apply the machine learning model to the preprocessed data.Type: GrantFiled: March 14, 2019Date of Patent: October 25, 2022Assignee: Hewlett-Packard Development Company, L.P.Inventors: David Murphy, Thomas da Silva Paula, Wagston Tassoni Staehler, Joao Eduardo Carrion, Alexandre Santos da Silva, Jr., Juliano Cardoso Vacaro, Gabriel Rodrigo De Lima Paz
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Publication number: 20220334651Abstract: A system may comprise a processor to execute an application, a keystroke analyzer module to determine a frequency with which each key on a keyboard is utilized during execution of the application, and a key illumination module to illuminate the keys on the keyboard with an illumination pattern that visually distinguishes a first subset of keys on the keyboard from a second subset of keys on the keyboard.Type: ApplicationFiled: October 15, 2019Publication date: October 20, 2022Applicant: Hewlett-Packard Development Company, L.P.Inventors: Thomas da Silva Paula, Alexandre Santos da Silva, Jr., Wagston Tassoni Staehler
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Publication number: 20220308894Abstract: An application-classification and peripheral device settings (APDS) system may comprise a categorization subsystem to capture descriptive information about an application, analyze the descriptive information, determine a classification of the application, and assign the application to an application category that includes applications that each have a similar classification. The system may also include a peripheral device configuration subsystem to identify a peripheral device setting from the application category and apply the peripheral device setting to an operating configuration of the application.Type: ApplicationFiled: October 15, 2019Publication date: September 29, 2022Applicant: Hewlett-Packard Development Company, L.P.Inventors: Thomas da Silva Paula, Alexandre Santos da Silva, Jr., Wagston Tassoni Staehler
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Publication number: 20220156639Abstract: Examples of methods for predicting processing workloads are described herein. In some examples, a method may include predicting a processing workload for a set of machine learning models. In some examples, the method may include loading a machine learning model of the set of machine learning models from non-volatile memory based on the predicted processing workload.Type: ApplicationFiled: August 7, 2019Publication date: May 19, 2022Applicant: Hewlett-Packard Development Company, L.P.Inventors: Vitor Henrique da Silva, Thomas da Silva Paula, Pedro Henrique Garcez Monteiro
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Publication number: 20220076084Abstract: A method includes receiving, with a computing device, a first client request from a first client that identifies a machine learning model and a sensor. The method includes sending, with the computing device, a call to a server to apply the identified machine learning model to a set of data from the identified sensor, in response to the first client request. The method includes receiving, with the computing device, a second client request from a second client that identifies a same machine learning model and sensor as the first client request. The method includes sending, with the computing device, response data from the identified machine learning model to both the first client and the second client without sending an additional call to the server in response to the second client request.Type: ApplicationFiled: March 14, 2019Publication date: March 10, 2022Applicant: Hewlett-Packard Development Company, L.P.Inventors: David Murphy, Thomas da Silva Paula, Wagston Tassoni Staehler, Joao Edurado Carrion, Alexandre Santos da Silva, Jr., Juliano Cardoso Vacaro, Gabriel Rodrigo De Lima Paz
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Publication number: 20220053071Abstract: A method includes receiving, with a computing device, a configuration file and a client request to apply a machine learning model to a set of data from a sensor. The method includes performing, with the computing device, preprocessing on the set of data from the sensor based on the configuration file to generate preprocessed data. The method includes sending, with the computing device, a call to a model server to apply the machine learning model to the preprocessed data.Type: ApplicationFiled: March 14, 2019Publication date: February 17, 2022Applicant: Hewlett-Packard Development Company, L.P.Inventors: David Murphy, Thomas da Silva Paula, Wagston Tassoni Staehler, Joao Eduardo Carrion, Alexandre Santos da Silva, Jr., Juliano Cardoso Vacaro, Gabriel Rodrigo De Lima Paz
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Publication number: 20220050623Abstract: A hierarchical sensor data storage system includes a data storage to store processed data that includes sensor data generated by a sensor and feature vectors associated with the sensor data that are generated by a processing subsystem processing the sensor data. Another data storage may store a reduced subset of the feature vectors and associated sensor data as salient data, as determined by a saliency subsystem.Type: ApplicationFiled: March 21, 2019Publication date: February 17, 2022Applicant: Hewlett-Packard Development Company, L.P.Inventors: David Murphy, Thomas da Silva Paula, Carlos Eduardo Leao
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Publication number: 20210337073Abstract: An example of an apparatus is provided. The apparatus includes an extraction engine to extract a plurality of patches from an image of a printed document. The apparatus further includes a classification engine to analyze each patch of the plurality of patches and to assign a defect probability to each patch of the plurality of patches. The apparatus also includes a rendering engine to generate a map based on the defect probability of each patch of the plurality of patches. The map is to identify defects in the printed document.Type: ApplicationFiled: December 20, 2018Publication date: October 28, 2021Applicant: Hewlett-Packard Development Company, L.P.Inventors: Qian Lin, Otavio Basso Gomes, Augusto Cavalcante Valente, Guilherme Augusto Silva Megeto, Marcos Henrique Cascone, Thomas da Silva Paula, Fabio Vinicius Moreira Perez
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Publication number: 20210312607Abstract: An example of an apparatus is provided. The apparatus includes a preprocessing engine to preprocess an image of a print into a reduced format wherein the reduced format includes a plurality of pixels. The apparatus further includes a segmentation analysis engine to generate a plurality of labels. Each label of the plurality of labels is associated with a pixel of the plurality of pixels. The plurality of labels identifies each pixel of the plurality of pixels as a defective pixel or a non-defective pixel. The apparatus also includes a rendering engine to display defects in the reduced format based on the plurality of labels.Type: ApplicationFiled: November 2, 2018Publication date: October 7, 2021Applicant: Hewlett-Packard Development Company, L.P.Inventors: Qian Lin, Augusto Cavalcante Valente, Otavio Basso Gomes, Deangeli Gomes Neves, Guilherme Augusto Sliva Megeto, Marcos Henrique Cascone, Thomas da Silva Paula
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Publication number: 20210089571Abstract: A machine learning encoder encodes images into image feature vectors representable in a multimodal space. The encoder also encodes a query into a textual feature vector representable in the multimodal space. The image feature vectors are compared to the textual feature in the multimodal space to identify an image matching the query based on the comparison.Type: ApplicationFiled: April 10, 2017Publication date: March 25, 2021Inventors: Christian Perone, Thomas da Silva Paula, Roberto Pereira Silveria