MACHINE LEARNING

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Resultados 149 resultados LastUpdate Última actualización 11/07/2020 [18:06:00] pdf PDF xls XLS

Solicitudes publicadas en los últimos 60 días / Applications published in the last 60 days



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SYSTEMS AND METHODS FOR PROVIDING AUTOMATED NATURAL LANGUAGE DIALOGUE WITH CUSTOMERS

NºPublicación: US2020202840A1 25/06/2020

Solicitante:

CAPITAL ONE SERVICES LLC [US]

US_2019287512_A1

Resumen de: US2020202840A1

A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of providing automated natural dialogue with a customer. The system may generate one or more events and commands temporarily stored in queues to be processed by one or more of a dialogue management device, an API server, and an NLP device. The dialogue management device may create adaptive responses to customer communications using a customer context, a rules-based platform, and a trained machine learning model.

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Secure Machine Learning Analytics Using Homomorphic Encryption

NºPublicación: US2020204341A1 25/06/2020

Solicitante:

ENVEIL INC [US]

Resumen de: US2020204341A1

Provided are methods and systems for performing a secure machine learning analysis over an instance of data. An example method includes acquiring, by a client, an homomorphic encryption scheme, and at least one machine learning model data structure. The method further includes generating, using the encryption scheme, at least one homomorphically encrypted data structure, and sending the encrypted data structure to at least one server. The method includes executing a machine learning model, by the at least one server based on the encrypted data structure to obtain an encrypted result. The method further includes sending, by the server, the encrypted result to the client where the encrypted result is decrypted. The machine learning model includes neural networks and decision trees.

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SYSTEMS AND METHODS FOR MODELING MACHINE LEARNING AND DATA ANALYTICS

NºPublicación: US2020202242A1 25/06/2020

Solicitante:

DATATRON TECH INC [US]

US_2019258947_A1

Resumen de: US2020202242A1

Systems and methods for implementing and using a data modeling and machine learning lifecycle management platform that facilitates collaboration among data engineering, development and operations teams and provides capabilities to experiment using different models in a production environment to accelerate the innovation cycle. Stored computer instructions and processors instantiate various modules of the platform. The modules include a user interface, a collector module for accessing various data sources, a workflow module for processing data received from the data sources, a training module for executing stored computer instructions to train one or more data analytics models using the processed data, a predictor module for producing predictive datasets based on the data analytics models, and a challenger module for executing multi-sample hypothesis testing of the data analytics models.

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HEALTHCARE SYSTEMS AND METHODS USING VOICE INPUTS

NºPublicación: US2020202863A1 25/06/2020

Solicitante:

CES ADVANCEMENTS LLC [US]

WO_2019173726_A1

Resumen de: US2020202863A1

A voice-enabled digital communications assistant powered in part using tailored machine learning models and other algorithms is used to engage with and control one or more healthcare devices or instruments such that a user is able to control the devices or instruments using natural language, conversational-like, voice commands. A command processor processes the audible instructions, while a context-aware processor monitors the present states and conditions of all devices and instruments, as well as the environment, for situational awareness purposes, including situations where executing commands may be incompatible with or conflict with the present states or conditions of devices and instruments as well as their expected future states. In addition to speech responses by the digital assistant, a separate notification engine provides audible or visual feedback to the user.

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SYSTEMS AND METHODS IMPLEMENTING AN INTELLIGENT OPTIMIZATION PLATFORM

NºPublicación: US2020202254A1 25/06/2020

Solicitante:

SIGOPT INC [US]

US_2019147362_A1

Resumen de: US2020202254A1

A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

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COMPUTATIONALLY-EFFICIENT QUATERNION-BASED MACHINE-LEARNING SYSTEM

NºPublicación: US2020202216A1 25/06/2020

Solicitante:

INTEL CORP [US]

US_2020193235_A1

Resumen de: US2020202216A1

A quaternion deep neural network (QTDNN) includes a plurality of modular hidden layers, each comprising a set of QT computation sublayers, including a quaternion (QT) general matrix multiplication sublayer, a QT non-linear activations sublayer, and a QT sampling sublayer arranged along a forward signal propagation path. Each QT computation sublayer of the set has a plurality of QT computation engines. In each modular hidden layer, a steering sublayer precedes each of the QT computation sublayers along the forward signal propagation path. The steering sublayer directs a forward-propagating quaternion-valued signal to a selected at least one QT computation engine of a next QT computation subsequent sublayer.

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MODEL-BASED MACHINE LEARNING SYSTEM

NºPublicación: US2020202235A1 25/06/2020

Solicitante:

IND TECH RES INST [TW]

Resumen de: US2020202235A1

A model-based machine learning system for calculating optimum molding conditions includes a data storage device providing a set of training data; an injection molding process emulator producing a set of emulated sensing data according to molding conditions as inputted; an injection molding process state observation unit, determining an injection molding process state from molding conditions, sensing data and a quality state, wherein the quality state at least includes an acceptance state; and an injection molding process optimization unit including an injection molding condition optimizer, wherein a molding condition optimization model constructed in the injection molding condition optimizer is trained according to the injection molding process state as determined, and the molding condition optimization model after training is introduced into an injection molding production line.

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MACHINE LEARNING IN AVIONICS

NºPublicación: US2020202723A1 25/06/2020

Solicitante:

THALES SA [FR]

EP_3671392_A1

Resumen de: US2020202723A1

Systems and methods for managing the flight of an aircraft, include the steps of receiving data from recordings of the flight of an aircraft; the data comprising data from sensors and/or data from the onboard avionics; determining the aircraft state at a point N on the basis of the received data; determining the state of the aircraft at the point N+1 on the basis of the state of the aircraft at point N by applying a model learnt by means of machine learning. Developments describe the use of the flight parameters SEP, FF and N1; offline and/or online unsupervised machine learning, according to a variety of algorithms and neural networks. Software aspects are described.

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METHODS, SYSTEMS, AND STORAGE MEDIA FOR PREDICTING PHYSICAL CHANGES TO A WELLHEAD IN AN AQUATIC VOLUME OF INTEREST

NºPublicación: US2020199996A1 25/06/2020

Solicitante:

CHEVRON USA INC [US]

Resumen de: US2020199996A1

Methods, systems, and storage media for predicting physical changes to a wellhead coupled to a riser in an aquatic volume of interest are disclosed. Exemplary implementations may: obtain training data; obtain a machine learning algorithm; generate a riser response model by applying a machine learning algorithm to the training data; store the riser response model, obtain target environmental data, target tension data, and target mud data, generate predicted riser response data, transform predicted riser response data, generate a representation, and display the representation.

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SYSTEMS AND METHODS FOR MACHINE LEARNING BASED RISKY CIRCUIT PATTERN IDENTIFICATION

NºPublicación: US2020202244A1 25/06/2020

Solicitante:

ANSYS INC [US]

Resumen de: US2020202244A1

Machine assisted systems and methods for detecting unreliable circuit patterns are described. These systems and methods can use a machine learning classifier, that has been trained to recognize such circuit patterns, to detect the unreliable circuit patterns without requiring computationally expensive simulations of a circuit netlist which can be over a million devices (e.g. over a million FETs). The classifier, once trained, can recognize unreliable circuit patterns quickly and can be updated over time as new unreliable circuit patterns are discovered from simulations or other sources.

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APPARATUS AND METHOD FOR OPERATING A PERSONAL GROOMING APPLIANCE OR HOUSEHOLD CLEANING APPLIANCE

NºPublicación: US2020201272A1 25/06/2020

Solicitante:

PROCTER & GAMBLE [US]

US_2020201266_A1

Resumen de: US2020201272A1

A system and method for operating a personal grooming/household appliance, including: providing a personal grooming/household appliance including at least one physical sensor taken from a group consisting of: an orientation sensor, an acceleration sensor, an inertial sensor, a global positioning sensor, a pressure sensor, and a load sensor, audio sensor, humidity sensor, and a temperature sensor; providing a camera associated with the personal grooming/household appliance; classifying data received from the physical sensor and from the camera using at least one trained machine learning classifier to generate an augmented classification; and providing user feedback information based upon the augmented classification or modifying operation of the grooming/household appliance based upon the augmented classification.

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INDUSTRIAL DATA SERVICE, DATA MODELING, AND DATA APPLICATION PLATFORM

NºPublicación: US2020201293A1 25/06/2020

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

CN_110799997_A

Resumen de: US2020201293A1

This disclosure relates to industrial data services, data modeling and applications for controlling an industrial operation. In one implementation, a platform is disclosed for allocating a data modeling request to a collaborative group of experts based on a two-dimensional data modeling flow data structure and a multilayer resource allocation graph to obtain a data model for controlling the industrial operation. The two-dimensional data modeling flow data structure and the multilayer resource allocation graph are established from an industrial graph knowledgebase using various data analytics and machine learning techniques.

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TRAINING A CLASSIFIER TO DETECT OPEN VEHICLE DOORS

NºPublicación: WO2020132676A2 25/06/2020

Solicitante:

WAYMO LLC [US]

US_2020202209_A1

Resumen de: WO2020132676A2

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a classifier to detect open vehicle doors. One of the methods includes obtaining a plurality of initial training examples, each initial training example comprising (i) a sensor sample from a collection of sensor samples and (ii) data classifying the sensor sample as characterizing a vehicle that has an open door; generating a plurality of additional training examples, comprising, for each initial training example: identifying, from the collection of sensor samples, one or more additional sensor samples that were captured less than a threshold amount of time before the sensor sample in the initial training example was captured; and training the machine learning classifier on first training data that includes the initial training examples and the additional training examples to generate updated weights for the machine learning classifier.

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SYSTEMS AND METHODS FOR INTELLIGENTLY CONFIGURING AND DEPLOYING A CONTROL STRUCTURE OF A MACHINE LEARNING-BASED DIALOGUE SYSTEM

NºPublicación: US2020193265A1 18/06/2020

Solicitante:

CLINC INC [US]

WO_2020123079_A1

Resumen de: US2020193265A1

A system and method of configuring a graphical control structure for controlling a machine learning-based automated dialogue system includes configuring a root dialogue classification node that performs a dialogue intent classification task for utterance data input; configuring a plurality of distinct dialogue state classification nodes that are arranged downstream of the root dialogue classification node; configuring a graphical edge connection between the root dialogue classification node and the plurality of distinct state dialogue classification nodes that graphically connects each of the plurality of distinct state dialogue classification nodes to the root dialogue classification node, wherein (i) the root dialogue classification node, (ii) the plurality of distinct classification nodes, (iii) and the transition edge connections define a graphical dialogue system control structure that governs an active dialogue between a user and the machine learning-based automated dialogue system.

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INTERPRETABILITY-BASED MACHINE LEARNING ADJUSTMENT DURING PRODUCTION

NºPublicación: US2020193313A1 18/06/2020

Solicitante:

PARALLEL MACHINES INC [US]

WO_2020123985_A1

Resumen de: US2020193313A1

Apparatuses, systems, program products, and methods are disclosed for interpretability-based machine learning adjustment during production. An apparatus includes a first results module that is configured to receive a first set of inference results of a first machine learning algorithm during inference of a production data set. An apparatus includes a second results module that is configured to receive a second set of inference results of a second machine learning algorithm during inference of a production data set. An apparatus includes an action module that is configured to trigger one or more actions that are related to a first machine learning algorithm in response to a comparison of first and second sets of inference results not satisfying explainability criteria.

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DISTRIBUTED MACHINE LEARNING DECENTRALIZED APPLICATION PLATFORM

NºPublicación: US2020193451A1 18/06/2020

Solicitante:

SAP SE [DE]

Resumen de: US2020193451A1

A request for an inference from a customer is received at a machine learning (ML) decentralized application (DAPP) platform, where the request includes a data record associated with a user that is associated with the customer. The data record is distributed by the ML DAPP platform among a number of service providers. An inference is received at the ML DAPP platform from each service provider. The received inferences are returned to the customer by the ML DAPP platform.

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MACHINE LEARNING MODELS FOR DETECTING THE CAUSES OF CONDITIONS OF A SATELLITE COMMUNICATION SYSTEM

NºPublicación: US2020194876A1 18/06/2020

Solicitante:

HUGHES NETWORK SYSTEMS LLC [US]

US_2020073742_PA

Resumen de: US2020194876A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training and using machine learning models to detect problems in a satellite communication system. In some implementations, one or more feature vectors that respectively correspond to different times are obtained. The feature vector(s) are provided as input to one or more machine learning models trained to receive at least one feature vector that includes feature values representing properties of the satellite communication system and output an indication of potential causes of a condition of the satellite communication system based on the properties of the satellite communication system. A particular cause that is indicated as being a most likely cause of the condition of the satellite communication system is determined based on one or more machine learning model outputs received from each of the one or more machine learning models.

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Detection Of Malware Using Feature Hashing

NºPublicación: US2020193024A1 18/06/2020

Solicitante:

CYLANCE INC [US]

US_2018211041_PA

Resumen de: US2020193024A1

Data to is analyzed using feature hashing to detect malware. A plurality of features in a feature set is hashed. The feature set is generated from a sample. The sample includes at least a portion of a file. Based on the hashing, one or more hashed features are indexed to generate an index vector. Each hashed feature corresponds to an index in the index vector. Using the index vector, a training dataset is generated. Using the training dataset, a machine learning model for identifying at least one file having a malicious code is trained.

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METHOD OF AND SYSTEM FOR SELECTIVELY PRESENTING A RECOMMENDATION BLOCK IN BROWSER APPLICATION

NºPublicación: US2020192966A1 18/06/2020

Solicitante:

YANDEX EUROPE AG [CH]

Resumen de: US2020192966A1

A method of selectively presenting a recommendation block to a user accessing a web resource in a browser application is disclosed. The method is executable at a server and includes receiving an indication of a browser application state and an indication of a user action with the browser application, determining features associated with at least a part of the web resource, receiving an indication of supplemental content at least in part based on the plurality of features, and executing a Machine Learning Algorithm (MLA) to determine a recommendation score based on at least the browser application state. Based on the recommendation score, the server selectively sends to the browser application at least one data packet configured to cause the browser application to display the recommendation block containing the supplemental content on the display of the electronic device.

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METHOD OF PRODUCING MACHINE LEARNING MODEL, AND COPYING APPARATUS

NºPublicación: US2020195807A1 18/06/2020

Solicitante:

SEIKO EPSON CORP [JP]

JP_2020096290_A

Resumen de: US2020195807A1

A method of producing a machine learning model includes collecting, as training data, data including an image of a document and a size of the document or a size of an actually copied output sheet from a copying apparatus that copies the document on the output sheet, and producing a model for determining whether or not a mark included in the image of the document is identifiable by a user on the document or the copied output sheet based on the training data.

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SMART COPY OPTIMIZATION

NºPublicación: US2020193322A1 18/06/2020

Solicitante:

ZETA GLOBAL CORP [US]

WO_2020123949_A1

Resumen de: US2020193322A1

In some examples, special-purpose machines are provided that facilitate smart copy optimization in a network service or publication system, including software-configured computerized variants of such special-purpose machines and improvements to such variants, and to the technologies by which such special-purpose machines become improved compared to other special-purpose machines that facilitate adding the new features. Such technologies can include special artificial-intelligence (AI), machine-learning (ML), and natural-language-processing (NLP) techniques.

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MACHINE LEARNING MODELS FOR EVALUATING DIFFERENCES BETWEEN GROUPS AND METHODS THEREOF

NºPublicación: US2020193321A1 18/06/2020

Solicitante:

CAPITAL ONE SERVICES LLC [US]

Resumen de: US2020193321A1

Systems, methods, and computer readable media are disclosed for generating, modifying, and using machine learning models to predict and evaluate differences between groups. Methods disclosed herein may include identifying variables that characterize members of a first group, generating shift indicators using the identified variables, generating a machine learning model using the shift indicators and the first group, using the machine learning model and the group to predict shifts between the first group and a predicted second group, determining an aggregate population shift and an aggregate performance shift between the first group and an actual second group, and identifying an impact of one or more of the shift indicators on the aggregate population shift or performance shift. Systems and methods disclosed herein may be configured to receive requests to predict and evaluate differences between group, and to return such predictions and evaluations to one or more users.

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Data Insight Automation

NºPublicación: US2020193230A1 18/06/2020

Solicitante:

SAP SE [DE]

Resumen de: US2020193230A1

Static and dynamic process data of a system are accessed. Thereafter, using this accessed process data, a subset of such data forming relevant data for a particular context is derived. The data is then explored using a computer-implemented process or processes to automatically get insight into information about structures, distributions and correlations of the relevant data. Rules can be generated based on the exploring of relevant data that describe data dependencies within the relevant data. These generated rules can later be used to generate synthetic data. Such synthetic data, in turn, can be used to for a variety of purposes including the training of a machine learning model while, at the same time, complying with applicable privacy and data protection laws and regulations.

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EXPLAINABILITY-BASED ADJUSTMENT OF MACHINE LEARNING MODELS

NºPublicación: WO2020123985A1 18/06/2020

Solicitante:

DATAROBOT INC [US]

US_2020193313_A1

Resumen de: WO2020123985A1

An apparatus for explainability-based adjustment of machine learning models includes a first prediction module configured to receive a first set of predictions from a primary machine learning model based on a data set, a second prediction module configured to receive a second set of predictions from a secondary machine learning model based on the data set, and an action module configured to trigger one or more actions related to the primary machine learning model in response to a comparison of the first and second sets of predictions not satisfying one or more explainability criteria.

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SMART COPY OPTIMIZATION

Nº publicación: WO2020123949A1 18/06/2020

Solicitante:

ZETA GLOBAL CORP [US]

US_2020193322_A1

Resumen de: WO2020123949A1

In some examples, special-purpose machines are provided that facilitate smart copy optimization in a network service or publication system, including software-configured computerized variants of such special-purpose machines and improvements to such variants, and to the technologies by which such special-purpose machines become improved compared to other special-purpose machines that facilitate adding the new features. Such technologies can include special artificial-intelligence (AI), machine-learning (ML), and natural-language-processing (NLP) techniques

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