MACHINE LEARNING

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Resultados 61 resultados LastUpdate Última actualización 03/04/2020 [19:47:00] pdf PDF xls XLS

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



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METHOD FOR PROTECTING A MACHINE LEARNING ENSEMBLE FROM COPYING

NºPublicación: EP3629249A1 01/04/2020

Solicitante:

NXP BV [NL]

Resumen de: EP3629249A1

A method is provided for protecting a machine learning ensemble. In the method, a plurality of machine learning models is combined to form a machine learning ensemble. A plurality of data elements for training the machine learning ensemble is provided. The machine learning ensemble is trained using the plurality of data elements to produce a trained machine learning ensemble. During an inference operating phase, an input is received by the machine learning ensemble. A piecewise function is used to pseudo-randomly choose one of the plurality of machine learning models to provide an output in response to the input. The use of a piecewise function hides which machine learning model provided the output, making the machine learning ensemble more difficult to copy.

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Method and apparatus for providing a machine learning approach for a point-based map matcher

NºPublicación: EP3625690A1 25/03/2020

Solicitante:

HERE GLOBAL BV [NL]

WO_2018213099_A1

Resumen de: US10060751B1

An approach is provided for point-based map matchers using machine learning. The approach involves retrieving points collected within proximity to a map feature represented by a link of a geographic database. The probe points are collected from sensors of devices traveling near the map feature. The approach also involves determining a probe feature set for each probe point comprising probe attribute values, and determining a link feature set for the link comprising link attribute values. The apparatus further involves classifying, using a machine learning classifier, each probe point to determine a matching probability based on the probe feature set and the link feature to indicate a probability that each probe point is classified as map-matched to the link. The machine learning classifier is trained using ground truth data comprising reference probe points with known map-matches to respective reference links, and comprising known probe attribute values and known link attribute values.

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SYSTEMS AND METHODS FOR REAL TIME CONFIGURABLE RECOMMENDATION USING USER DATA

NºPublicación: EP3627399A1 25/03/2020

Solicitante:

TATA CONSULTANCY SERVICES LTD [IN]

US_2020090056_A1

Resumen de: EP3627399A1

Business to Consumer (B2C) systems face a challenge of engaging users since offers are created using static rules generated using clustering on large transactional data generated over a period of time. Moreover, the offer creation and assignment engine is disjoint to the transactional system which led to significant gap between history used to create offers and current activity of users. Systems and methods of the present disclosure provide a meta-model based configurable auto-tunable recommendation model generated by ensembling optimized machine learning and deep learning models to predict a user's likelihood to take an offer and deployed in real time. Furthermore, the offer given to the user is based on a current context derived from the user's recent behavior that makes the offer relevant and increases probability of conversion of the offer to a sale. The system achieves low recommendation latency and scalable high throughput by virtue of the architecture used.

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LEARNING PROGRAM, LEARNING METHOD, AND LEARNING APPARATUS

NºPublicación: EP3627402A1 25/03/2020

Solicitante:

FUJITSU LTD [JP]

JP_2020046888_A

Resumen de: EP3627402A1

A learning program causes a computer to execute a process including: generating(S16), from pieces of training data(21) each including explanatory variables and an objective variable, a hypothesis set(23) in which a plurality of hypotheses meeting a specific condition, each of the plurality of hypotheses being a combination of the explanatory variables, each of the pieces of training data being classified into any of the plurality of hypotheses; and performing(S18) a machine learning process to calculate a weight of each of the plurality of hypotheses included in the hypothesis set on a basis of whether each of the plurality of hypotheses includes each of the pieces of training data.

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UTILIZING MACHINE LEARNING MODELS TO AUTOMATICALLY GENERATE CONTEXTUAL INSIGHTS AND ACTIONS BASED ON LEGAL REGULATIONS

NºPublicación: US2020090059A1 19/03/2020

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

CA_3053081_A1

Resumen de: US2020090059A1

A device may receive input data associated with a legal regulation, and may process the input data to generate a record that includes: the input data in a knowledge representation format and a semantic representation format, data identifying a feature, data identifying an industry classification, or data identifying an entity of interest. The device may process the record, with machine learning models, to determine output data that includes: data indicating that the legal regulation is inconsistent, data indicating that the legal regulation is outdated, data indicating a sentiment for the legal regulation, data indicating a prescriptive nature of the legal regulation, data indicating a complexity of the legal regulation, data indicating a misrepresentation in the legal regulation, data indicating a compliance burden associated with the legal regulation, or data indicating an industry performance impact of the legal regulation. The device may perform actions based on the output data.

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SYSTEM FOR SIMPLIFIED GENERATION OF SYSTEMS FOR BROAD AREA GEOSPATIAL OBJECT DETECTION

NºPublicación: US2020089930A1 19/03/2020

Solicitante:

DIGITALGLOBE INC [US]

US_2018189544_A1

Resumen de: US2020089930A1

A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.

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METHOD AND APPARATUS FOR ALLOCATING BANDWIDTH BASED ON MACHINE LEARNING IN PASSIVE OPTICAL NETWORK

NºPublicación: US2020092622A1 19/03/2020

Solicitante:

ELECTRONICS & TELECOMMUNICATIONS RES INST [KR]

Resumen de: US2020092622A1

A method and apparatus for allocating a bandwidth based on machine learning in a passive optical network, the method including generating an inference model to predict a consumed bandwidth required for transmission by learning unstructured data of a PON including an OLT and traffic data corresponding to state information of the PON collected from the PON, predicting a consumed bandwidth with respect to a queue corresponding to a class requiring a low-latency service among classes of ONUS connected to the OLT based on the generated inference model, performing a VBA with respect to the queue corresponding to the class requiring the low-latency service based on the predicted consumed bandwidth, and performing a DBA with respect to a queue corresponding to a class not requiring the low-latency service using a transmission bandwidth which remains after the VBA is performed.

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SYSTEMS AND METHODS FOR REAL TIME CONFIGURABLE RECOMMENDATION USING USER DATA

NºPublicación: US2020090056A1 19/03/2020

Solicitante:

TATA CONSULTANCY SERVICES LTD [IN]

EP_3627399_A1

Resumen de: US2020090056A1

Business to Consumer (B2C) systems face a challenge of engaging users since offers are created using static rules generated using clustering on large transactional data generated over a period of time. Moreover, the offer creation and assignment engine is disjoint to the transactional system which led to significant gap between history used to create offers and current activity of users. Systems and methods of the present disclosure provide a meta-model based configurable auto-tunable recommendation model generated by ensembling optimized machine learning and deep learning models to predict a user's likelihood to take an offer and deployed in real time. Furthermore, the offer given to the user is based on a current context derived from the user's recent behavior that makes the offer relevant and increases probability of conversion of the offer to a sale. The system achieves low recommendation latency and scalable high throughput by virtue of the architecture used.

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MACHINE LEARNING MODEL REPOSITORY

NºPublicación: US2020090038A1 19/03/2020

Solicitante:

KENSCI INC [US]

US_2019130262_PA

Resumen de: US2020090038A1

Embodiments are directed towards a machine learning repository for managing machine learning (ML) model envelopes, ML models, model objects, or the like. Questions and model objects may be received by a ML model answer engine. Machine learning (ML) model envelopes may be received based on the questions. The model objects may be compared to parameter models associated with the ML model envelopes. ML model envelopes may be selected based on the comparison such that the model objects satisfy the parameter models of each of the selected ML model envelopes. ML models included in each selected ML model envelope may be executed to provide score values for the model objects and the score values may be included in a report.

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METHOD AND SYSTEM FOR HYBRID AI-BASED SONG CONSTRUCTION

NºPublicación: US2020090632A1 19/03/2020

Solicitante:

BELLEVUE INVEST GMBH & CO KGAA [DE]

Resumen de: US2020090632A1

According to an embodiment, there is provided a system and method for automatic AI-based song construction based on ideas of a user. It provides and benefits from a combination of expert knowledge resident in an expert engine which contains rules for a musically correct song generation and machine learning in an AI-based audio loop selection engine for the selection of fitting audio loops from a database of audio loops.

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SYSTEM AND METHOD FOR OPERATING A FOOD PREFERENCE ALGORITHM

NºPublicación: US2020090060A1 19/03/2020

Solicitante:

NUTRISTYLE INC [US]

Resumen de: US2020090060A1

A method of operating a food preference algorithm involves retrieving a meal framework including at least one food component category from a meal framework database through operation of a meal selector configured by a preferences profile in a user profile, generating a meal profile including at least one food item retrieved from a food item database through operation of a food component selector configured by the meal framework and the preferences profile, operating a machine learning food preferences algorithm, and applying the updated food preferences control to the preferences profile.

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REGRESSION-TREE COMPRESSED FEATURE VECTOR MACHINE FOR TIME-EXPIRING INVENTORY UTILIZATION PREDICTION

NºPublicación: US2020090116A1 19/03/2020

Solicitante:

AIRBNB INC [US]

JP_2019516184_A

Resumen de: US2020090116A1

This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.

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COMPUTER-READABLE RECODING MEDIUM, LEARNING METHOD, PREDICTION METHOD, LEARNING APPARATUS, AND PREDICTION APPARATUS

NºPublicación: US2020090064A1 19/03/2020

Solicitante:

FUJITSU LTD [JP]

JP_2020046888_A

Resumen de: US2020090064A1

A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process including: generating, from pieces of training data each including explanatory variables and an objective variable, a hypothesis set in which a plurality of hypotheses meeting a specific condition, each of the plurality of hypotheses being a combination of the explanatory variables, each of the pieces of training data being classified into any of the plurality of hypotheses; and performing a machine learning process to calculate a weight of each of the plurality of hypotheses included in the hypothesis set on a basis of whether each of the plurality of hypotheses includes each of the pieces of training data.

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A RELIABLE TOOL FOR EVALUATING BRAIN HEALTH

NºPublicación: WO2020053849A1 19/03/2020

Solicitante:

QUANTALX NEUROSCIENCE LTD [IL]

Resumen de: WO2020053849A1

Systems and a computer implemented method for classifying a brain status of a subject, from a neural activity response of the subject to an induced TMS stimulation; the method comprising: constructing a machine learning classifier (MLC) configured to classify a subject's brain status; training the MLC using a training set, the training set comprising pairs of training output-classification vectors and their corresponding training input vectors, all extracted from a database of subjects with known brain status classifications; and applying the trained MLC on an input vector comprising features extracted from a tested-subject's brain neural activity response to the induced TMS stimulation, to obtain an output classification vector for the tested-subject's brain status.

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MULTI-STAGE MACHINE-LEARNING MODELS TO CONTROL PATH-DEPENDENT PROCESSES

NºPublicación: WO2020056060A1 19/03/2020

Solicitante:

CEREBRI AI INC [US]

US_2020082261_A1

Resumen de: WO2020056060A1

Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.

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SOLDERING PROCESS PARAMETER SUGGESTION METHOD AND SYSTEM THEREOF

NºPublicación: EP3623097A1 18/03/2020

Solicitante:

DELTA ELECTRONICS INC [TW]

JP_2020040119_A

Resumen de: EP3623097A1

A soldering process method includes steps of: establishing a material component database; establishing a working parameter database; analyzing material and component characteristics required for a new soldering process; comparing the characteristics with information in the material component database; selecting operating parameters corresponding to the material and component characteristics similar to those required for the new soldering process; performing the soldering process using the operating parameters corresponding to the material and component characteristics similar to those required for the new soldering process; measuring and recording the soldering process execution information and the final product information; determining whether the final product of the solder process meets the quality control requirements; using the machine learning method to fit the soldering process execution information and the final product information of the solder process to get the operating parameters for the next soldering process when the final product does not meet the quality control requirements.

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SOLDERING PROCESS PARAMETER SUGGESTION METHOD AND SYSTEM THEREOF

NºPublicación: US2020082278A1 12/03/2020

Solicitante:

DELTA ELECTRONICS INC [TW]

JP_2020040119_A

Resumen de: US2020082278A1

A soldering process method includes steps of: establishing a material component database; establishing a working parameter database; analyzing material and component characteristics required for a new soldering process; comparing the characteristics with information in the material component database; selecting operating parameters corresponding to the material and component characteristics similar to those required for the new soldering process; performing the soldering process using the operating parameters corresponding to the material and component characteristics similar to those required for the new soldering process; measuring and recording the soldering process execution information and the final product information; determining whether the final product of the solder process meets the quality control requirements; using the machine learning method to fit the soldering process execution information and the final product information of the solder process to get the operating parameters for the next soldering process when the final product does not meet the quality control requirements.

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Manufacturing Device For Three-Dimensional Shaped Object, Manufacturing System For Three-Dimensional Shaped Object, And Manufacturing Method For Three-Dimensional Shaped Object

NºPublicación: US2020081416A1 12/03/2020

Solicitante:

SEIKO EPSON CORP [JP]

JP_2020040246_A

Resumen de: US2020081416A1

A manufacturing device for a three-dimensional shaped object has artificial intelligence to perform machine learning. The manufacturing device includes: an acquisition unit acquiring monitoring data of the three-dimensional shaped object and improvement condition data; a housing unit housing reference data of the monitoring data; a storage unit storing the monitoring data acquired by the acquisition unit; an inference unit classifying the monitoring data acquired by the acquisition unit into normal data and abnormal data, based on the reference data of the monitoring data housed in the housing unit, updating an inference criterion by machine learning, based on the monitoring data stored in the storage unit and classified as the normal data, and inferring what abnormality occurs when an abnormality is generated in updated data of the monitoring data newly acquired by the acquisition unit; and a decision unit deciding the improvement condition according to the abnormality inferred by the inference unit.

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INSPECTION APPARATUS AND MACHINE LEARNING METHOD

NºPublicación: US2020082297A1 12/03/2020

Solicitante:

FANUC CORP [JP]

JP_2020042669_A

Resumen de: US2020082297A1

An inspection apparatus of the present disclosure includes: a machine learning device that performs machine learning on a basis of state data acquired from an inspection target and label data indicating an inspection result related to the inspection target to generate a learning model; a learning model evaluation index calculation unit that calculates a learning model evaluation index related to the learning model generated by the machine learning device as an evaluation index to be used to evaluate the learning model; an inspection index acquisition unit that acquires an inspection index to be used in the inspection; and a learning model selection unit that displays the learning model evaluation index and the inspection index so as to be comparable with each other regarding the learning model generated by the machine learning device, receives selection of the learning model by an operator, and outputs a result of the selection.

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SYSTEM AND METHOD OF MACHINE LEARNING AND AUTONOMOUS EXECUTION ON USER PREFERENCES FOR USE IN GARMENTS

NºPublicación: US2020081462A1 12/03/2020

Solicitante:

POLAR SEAL LTD [HK]

WO_2018234961_PA

Resumen de: US2020081462A1

The present invention relates to a system with active learning and execution of user's preference functionalities for use in a garment. The present system includes a sensor module, an optional user input panel and/or interface, a printed circuit board, a power source and an output. In an event that a user of the present system voluntarily changes the output setting during the operation of the system, the system performs an active learning action to execute the output setting initiated by the user over a passive learning action triggered by a change in sensor data with respect to the changing environment. In other event, the present system performs passive learning action with respect to the changing environment and also any comparative data from similar user of a particular instance. The present invention also relates to a power management unit and how to use the same in a garment.

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MULTI-STAGE MACHINE-LEARNING MODELS TO CONTROL PATH-DEPENDENT PROCESSES

NºPublicación: US2020082261A1 12/03/2020

Solicitante:

CEREBRI AI INC [US]

WO_2020056060_A1

Resumen de: US2020082261A1

Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.

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Rejecting Biased Data Using a Machine Learning Model

NºPublicación: US2020082300A1 12/03/2020

Solicitante:

GOOGLE LLC [US]

WO_2020055581_A1

Resumen de: US2020082300A1

A method for rejecting biased data using a machine learning model includes receiving a cluster training data set including a known unbiased population of data and training a clustering model to segment the received cluster training data set into clusters based on data characteristics of the known unbiased population of data. Each cluster of the cluster training data set includes a cluster weight. The method also includes receiving a training data set for a machine learning model and generating training data set weights corresponding to the training data set for the machine learning model based on the clustering model. The method also includes adjusting each training data set weight of the training data set weights to match a respective cluster weight and providing the adjusted training data set to the machine learning model as an unbiased training data set.

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QUESTION ANSWERING SYSTEM, QUESTION ANSWERING PROCESSING METHOD, AND QUESTION ANSWERING INTEGRATED SYSTEM

NºPublicación: US2020074342A1 05/03/2020

Solicitante:

HITACHI LTD [JP]

JP_2020035135_A

Resumen de: US2020074342A1

A question answering system for presenting an answer to an input question includes: a user input processing unit configured to receive the input question; a machine learning platform configured to generate question information for searching corresponding to the received input question by using a learned model for inferring question information from an input question; a word vector platform and an annotated question search platform configured to generate question information for searching corresponding to the received input question without using the learned model; an answer search processing unit configured to search for an answer corresponding to the question information by using the question information generated by at least one of the machine learning platform and the word vector platform/the annotated question search platform; and a dialog output processing unit configured to present the answer obtained through the searching.

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Systems and Methods for Creating an Optimal Prediction Model and Obtaining Optimal Prediction Results Based on Machine Learning

NºPublicación: US2020074325A1 05/03/2020

Solicitante:

UNIV NATIONAL CHIAO TUNG [TW]

Resumen de: US2020074325A1

The present invention provides Systems and Methods for Creating an Optimal Prediction Model and Obtaining Optimal Prediction Results Based on Machine Learning. In the method for creating an optimal prediction model, the steps are first to input a plural training data and at least one of machine learning algorithms, then convert the training data into a relay format. The method is further to select the automated predictive features, optimize the machine learning algorithm parameter, and then optimize the iterative prediction model. After that, a prediction model and an accuracy assessment data are outputted. In the process of obtaining the prediction result, the data to be predicted is converted into a relay format, and an automated program is used for iterative prediction to generate and output the prediction result and accuracy evaluation data.

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Decision Support System for Individualizing Radiotherapy Dose

Nº publicación: US2020069973A1 05/03/2020

Solicitante:

SIEMENS HEALTHCARE GMBH [DE]
CLEVELAND CLINIC FOUND [US]

Resumen de: US2020069973A1

For decision support in a medical therapy, machine learning provides a machine-learned generator for generating a prediction of outcome for therapy personalized to a patient. The outcome prediction may be used to determine dose. To assist in decision support, a regression analysis of the cohort used for machine training relates the outcome from the machine-learned generator to the dose and an actual control time (e.g., time-to-event). The dose that minimizes side effects while minimizing risk of failure to a time for any given patient is determined from the outcome for that patient and a calibration from the regression analysis.

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