MACHINE LEARNING

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Resultados 149 resultados LastUpdate Última actualización 12/07/2020 [17:14:00] pdf PDF xls XLS

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



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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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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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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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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 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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LEARNING DATA CONFIRMATION SUPPORT DEVICE, MACHINE LEARNING DEVICE, AND FAILURE PREDICTING DEVICE

NºPublicación: US2020198128A1 25/06/2020

Solicitante:

FANUC CORP [JP]

CN_111352388_A

Resumen de: US2020198128A1

To facilitate confirmation as to whether or not it is data measured by the same operation upon acquiring measurement data in an industrial machine. A learning data confirmation support device 3 that facilitates confirmation of contamination of inappropriate data when learning data including only normal data are acquired in advance, in order to detect an anomaly of an industrial machine using machine learning, includes a data acquisition unit 31 that acquires measurement data including time-series data representing at least one of a predetermined state quantity or control quantity relating to control when the industrial machine is made to perform a certain operation; and a display control unit 32 that aligns a plurality of pieces of time-series data acquired by the data acquisition unit in a direction of a time axis and, in this state, superimposes a same type of pieces of data of the time-series data to display in a graph.

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DEVELOPMENT OF VIRTUAL CHARACTER IN A LEARNING GAME

NºPublicación: US2020197818A1 25/06/2020

Solicitante:

INTERLAKE RES LLC [US]

Resumen de: US2020197818A1

Technologies for executing a virtual character development application using machine learning are described herein. In typical simulation applications, a user may be enabled to create a virtual character and navigate a virtual world. A typical simulation application may accept inputs from the user, determine actions initiated by the user based on the inputs, determine subsequent outcomes of the user-initiated actions, and mold the simulation according to the outcomes. However, most outcomes may be predetermined and predictable by design. In contrast, some embodiments may include a server configured to execute a virtual character development application in conjunction with one or more client devices. A user may utilize a client device to create and develop a virtual character within the application. The user may be enabled to provide inputs to the virtual character development application, and the artificial component may process the input and extract information associated with the virtual character.

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TRAINING A CLASSIFIER TO DETECT OPEN VEHICLE DOORS

NºPublicación: US2020202209A1 25/06/2020

Solicitante:

WAYMO LLC [US]

WO_2020132676_A2

Resumen de: US2020202209A1

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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Methods and apparatus for automatically defining computer-aided design files using machine learning, image analytics, and/or computer vision

NºPublicación: AU2018360836A1 25/06/2020

Solicitante:

AIRWORKS SOLUTIONS INC [US]

US_2019155973_A1

Resumen de: AU2018360836A1

A non-transitory processor-readable medium includes code to cause a processor to receive aerial data having a plurality of points arranged in a pattern. An indication associated with each point is provided as an input to a machine learning model to classify each point into a category from a plurality of categories. For each point, a set of points (1) adjacent to that point and (2) having a common category is identified to define a shape from a plurality of shapes. A polyline boundary of each shape is defined by analyzing with respect to a criterion, a position of each point associated with a border of that shape relative to at least one other point. A layer for each category including each shape associated with that category is defined and a computer-aided design file is generated using the polyline boundary of each shape and the layer for each category.

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SYSTEMS AND METHODS UTILIZING MACHINE LEARNING TECHNIQUES TO MANAGE CONTENT IN STANDALONE MULTI-TENANCY ENVIRONMENTS

NºPublicación: US2020202239A1 25/06/2020

Solicitante:

YUSSOUFF ASHRAF [US]

Resumen de: US2020202239A1

A system described herein may allow for the intelligent, dynamic selection of an active tenant for a standalone, multi-tenant environment, such as a multi-operator bus or other vehicle. Intelligent selection may be performed using machine learning and/or other suitable techniques, which may be based on similarity to previous usage by a registered tenant, and may further include analyzing structured and/or unstructured data regarding the environment. In addition, the system may allow different profiles, content, or content templates to be associated with different tenants, thus granting a high level of dynamic flexibility in tailoring the content and/or services provided to the users of the environment based on the selected tenant.

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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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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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METHOD AND SYSTEM FOR COMPOSITE SCORING, CLASSIFICATION, AND DECISION MAKING BASED ON MACHINE LEARNING

NºPublicación: US2020193312A1 18/06/2020

Solicitante:

OPENMATTERS INC [US]

CA_3059699_A1

Resumen de: US2020193312A1

To clear a blindspot in the way business leaders, analysts and investors make decisions about capital investments in various businesses, the present inventors devised, among other things, business model classification, search, and analysis systems and methods. One exemplary system automatically classifies businesses based on quantitative and qualitative business data according to a 4-class framework that spans traditional industry boundaries. This classification is based on a combination of spending patterns, financial metrics, and language to identify each firm's business model. The resulting business model is then utilized in conjunction with additional financial and non-financial metrics, securities analysis, leading and lagging indicators, and/or industry comparison to produce a score which can be used to compare business performance within and across classifications to generate superior performance and mitigate risks for business leaders and investment managers.

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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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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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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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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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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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METHODS AND SYSTEMS FOR PREDICTING OR DIAGNOSING CANCER

NºPublicación: US2020194119A1 18/06/2020

Solicitante:

HANGZHOU NEW HORIZON HEALTH TECH CO LTD [CN]

WO_2020081445_PA

Resumen de: US2020194119A1

The present disclosure provides methods, systems, compositions, and kits for evaluating cancer risk. The methods and systems comprise producing an Operational Taxonomic Unit (OTU) profile derived from a sample collected from a human subject in need thereof, and executing a trained machine learning classifier to predict the probability that the human subject has cancer based on the OTU profile. Also provided are methods for diagnosing and treating a human subject at risk of having cancer, among other things.

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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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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

Nº publicación: 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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