MACHINE LEARNING

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Resultados 54 resultados LastUpdate Última actualización 22/02/2020 [17:44:00] pdf PDF

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

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IDENTIFICATION AND APPLICATION OF HYPERPARAMETERS FOR MACHINE LEARNING

NºPublicación: US2020057958A1 20/02/2020

Solicitante:

SALESFORCE COM INC [US]

Resumen de: US2020057958A1

Methods and systems are provided to determine suitable hyperparameters for a machine learning model and/or feature engineering process. A suitable machine learning model and associated hyperparameters are determined by analyzing a dataset. Suitable hyperparameter values for compatible machine learning models having one or more hyperparameters in common and a compatible dataset schema are identified. Hyperparameters may be ranked according to each of their respective influences on a model performance metrics, and hyperparameter values identified as having greater influence may be more aggressively searched.

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REDUCING INSTANCES OF INCLUSION OF DATA ASSOCIATED WITH HINDSIGHT BIAS IN A TRAINING SET OF DATA FOR A MACHINE LEARNING SYSTEM

NºPublicación: US2020057959A1 20/02/2020

Solicitante:

SALESFORCE COM INC [US]

Resumen de: US2020057959A1

Instances of data associated with hindsight bias in a training set of data for a machine learning system can be reduced. A first set of data, having a first set of fields, can be received. Data in a first field can be analyzed with respect to data in a second field corresponding to an event to be predicted. A result can be that the data in the first field is associated with hindsight bias. A second set of data, having a second set of fields, can be produced. The second set of fields can exclude the first field. One or more features associated with the second set of data can be generated. A third set of data, having the second set of fields and fields that correspond to the one or more features, can be produced. The training set of data can be produced using the third set of data.

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SIMILARITY BASED APPROACH FOR CLUSTERING AND ACCELERATING MULTIPLE INCIDENTS INVESTIGATION

NºPublicación: US2020057953A1 20/02/2020

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

Resumen de: US2020057953A1

Systems, methods, and apparatuses are provided for clustering incidents in a computing environment. An incident notification relating to an event (e.g., a potential cyberthreat or any other alert) in the computing environment is received and a set of features may be generated based on the incident notification. The set of features may be provided as an input to a machine-learning engine to identify a similar incident notification in the computing environment. The similar incident notification may include a resolved incident notification or an unresolved incident notification. An action to resolve the incident notification may be received, and the received action may thereby be executed. In some implementations, in addition to resolving the received incident notification, the action may be executed to resolve a similar unresolved incident notification identified by the machine-learning engine.

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MALICIOUS CLOUD-BASED RESOURCE ALLOCATION DETECTION

NºPublicación: WO2020036688A1 20/02/2020

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

US_2020053123_A1

Resumen de: WO2020036688A1

Methods, systems, and computer program products are described herein for detecting malicious cloud-based resource allocations. Such detection may be achieved using machine learning-based techniques that analyze sequences of cloud-based resource allocations to determine whether such sequences are performed with a malicious intent. For instance, a sequence classification model may be generated by training a machine learning-based algorithm on both resource allocation sequences that are known to be used for malicious purposes and resource allocation sequences that are known to be used for non-malicious or benign purposes. Using these sequences, the machine learning-based algorithm learns what constitutes a malicious resource allocation sequence and generates the sequence classification model. The sequence classification model is used to classify any sequence of resource allocation operations performed via a valid user's cloud services subscription provided thereto as being a malicious sequence or a non-malicious sequence.

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BRAIN OPERATING SYSTEM

NºPublicación: US2020057964A1 20/02/2020

Solicitante:

HOWARD NEWTON [US]

US_2020057965_A1

Resumen de: US2020057964A1

Embodiments may provide an intelligent adaptive system that combines input data types, processing history and objectives, research knowledge, and situational context to determine the most appropriate mathematical model, choose the computing infrastructure, and propose the best solution for a given problem. For example, a method implemented in a computer may comprise receiving, at the computer system, data relating to a problem to be solved, generating, at the computer system, a description of the problem, wherein the description conforms to defined format, obtaining, at the computer system, at least one machine learning model relevant to the problem, selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model relevant to the problem, and executing, at the computer system, the at least one machine learning model relevant to the problem using the selected computing infrastructure to generate at least one recommendation relevant to the problem.

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SYSTEMS AND METHODS FOR SELECTING TRAINING OBJECTS

NºPublicación: US2020057961A1 20/02/2020

Solicitante:

HUAWEI TECH CO LTD [CN]

Resumen de: US2020057961A1

Methods and systems are described for training a machine learning (ML) model to predict the gain of a target channel of a multi-channel amplifier device. An ML model may be pre-trained using an existing set of training objects. The trained ML model then can be utilized to suggest further useful training objects to be labelled that will improve the performance of the ML model by predicting more accurate target channel gains given the on/off value for the channel inputs.

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IDENTIFICATION AND APPLICATION OF HYPERPARAMETERS FOR MACHINE LEARNING

NºPublicación: WO2020037105A1 20/02/2020

Solicitante:

SALESFORCE COM INC [US]

US_2020057958_A1

Resumen de: WO2020037105A1

Methods and systems are provided to determine suitable hyperparameters for a machine learning model and/or feature engineering process. A suitable machine learning model and associated hyperparameters are determined by analyzing a dataset. Suitable hyperparameter values for compatible machine learning models having one or more hyperparameters in common and a compatible dataset schema are identified. Hyperparameters may be ranked according to each of their respective influences on a model performance metrics, and hyperparameter values identified as having greater influence may be more aggressively searched.

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SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR MACHINE-LEARNING-BASED TRAFFIC PREDICTION

NºPublicación: EP3610226A1 19/02/2020

Solicitante:

VISA INT SERVICE ASS [US]

US_2020033151_A1

Resumen de: WO2019245555A1

Described are a system, method, and computer program product for machine-learning-based traffic prediction. The method includes receiving historic transaction data including a plurality of transactions. The method also includes generating, using a machine-learning classification model, a transportation categorization for at least one consumer. The method further includes receiving at least one message associated with at least one transaction, identifying at least one geographic node of activity in the region, and generating an estimate of traffic intensity for the at least one geographic node of activity. The method further includes comparing the estimate of traffic intensity to a threshold of traffic intensity and, in response to determining that the estimate of traffic intensity satisfies the threshold: generating a communication configured to cause at least one navigation device to modify a navigation route; and communicating the communication to the at least one navigation device.

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MALICIOUS CLOUD-BASED RESOURCE ALLOCATION DETECTION

NºPublicación: US2020053123A1 13/02/2020

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

Resumen de: US2020053123A1

Methods, systems, and computer program products are described herein for detecting malicious cloud-based resource allocations. Such detection may be achieved using machine learning-based techniques that analyze sequences of cloud-based resource allocations to determine whether such sequences are performed with a malicious intent. For instance, a sequence classification model may be generated by training a machine learning-based algorithm on both resource allocation sequences that are known to be used for malicious purposes and resource allocation sequences that are known to be used for non-malicious or benign purposes. Using these sequences, the machine learning-based algorithm learns what constitutes a malicious resource allocation sequence and generates the sequence classification model. The sequence classification model is used to classify any sequence of resource allocation operations performed via a valid user's cloud services subscription provided thereto as being a malicious sequence or a non-malicious sequence.

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METHODS AND APPARATUS FOR MANAGEMENT OF A MACHINE-LEARNING MODEL TO ADAPT TO CHANGES IN LANDSCAPE OF POTENTIALLY MALICIOUS ARTIFACTS

NºPublicación: WO2020030913A1 13/02/2020

Solicitante:

SOPHOS LTD [GB]

Resumen de: WO2020030913A1

In some embodiments, an apparatus includes a memory and a processor. The processor can be configured to train a machine-learning(ML)model to output an identification of whether an artifact is malicious and (2) a confidence value associated with the identification of whether the artifact is malicious. The processor can further be configured to receive a set of artifacts during a set of time periods, and provide a representation of each artifact from the set of artifacts to obtain as an output of the MLmodel including an indication of whether that artifact is malicious and a confidence value associated with the indication. The processor can be further configured to calculate a confidence metric for each time period based on the confidence value associated with each artifact, and send an indication to retrain the MLmodel based on the confidence metric for at least one time period meeting a retraining criterion.

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Systems and Methods for Discovering Lookalike Mobile Devices

NºPublicación: US2020053515A1 13/02/2020

Solicitante:

XAD INC DBA GROUNDTRUTH [US]

US_2019045331_PA

Resumen de: US2020053515A1

The present disclosure provides methods and systems that utilize mobile device location events and machine learning and generate predicative classification/regression model for lookalike prediction. Location related features, together with other user level information, are extracted, transformed and used as model feature input, and a client specified list of mobile devices or their associated users are used as prediction target. This system makes efficient use of different types of location events and thus offers improved scale and performance. It also enjoys many benefits offered by a machine learning platform, such as automatic adaptation to different lists of seed lists, addition of new features and changes in data statistical properties.

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SYSTEM AND METHOD FOR CAPTURE AND ADAPTIVE DATA GENERATION FOR TRAINING FOR MACHINE VISION

NºPublicación: US2020050965A1 13/02/2020

Solicitante:

VIS MACHINA INC [US]

WO_2020033822_A1

Resumen de: US2020050965A1

A computer-implemented method of performing machine vision prediction of digital images using synthetically generated training assets comprises digitally capturing a plurality of assets; configuring each of the assets in the plurality of assets with a plurality of asset attributes; under computer program control, selecting a plurality of different combinations of parameters from among the plurality of asset attributes, and creating a plurality of sets of different synthetic dataset parameters; using computer graphics software, and example parameter values from among the synthetic dataset parameters, creating a synthetic dataset by compiling from a plurality of example images and metadata; configuring a plurality of machine learning trials and executing the trials to train a machine vision model, resulting in creating and storing a trained machine vision model; executing a validation of the trained machine vision model; and inferring a prediction using the trained machine vision model. Trained models are scored against success criteria and re-trained using pseudo-random sampling of different parameters clustered around failure points. As a result, machine vision models may be trained with high accuracy using large datasets of synthesized digital images that are richly parameterized, rather than human captured digital images.

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DEVICE AND METHOD FOR MULTI-CLASS CLASSIFICATION BY MACHINE LEARNING

NºPublicación: US2020050964A1 13/02/2020

Solicitante:

COMMISSARIAT ENERGIE ATOMIQUE [FR]

EP_3608847_A1

Resumen de: US2020050964A1

A method and a device for multi-class classification of an application relative to a classification of a transport mode by machine learning, including: (a) sensors configured to measure at successive instants physical quantities specific to said application, and (b) a microprocessor configured to: (b1) acquire said successive measurements from the sensors to calculate predictors from physical quantities of said measurements, (b2) construct continuous series (aj) of samples, each sample being a vector formed of a predetermined number of predictors, (b3) break down each series (aj) of samples into a set of sequences (Sk) each corresponding to a specific class among a set of predetermined classes, (b4) under-sample the sequences associated with the classes among said set of predetermined classes while conserving for each selected sequence a predetermined fraction of samples of said sequence, thereby forming classes associated with balanced samples, and (b5) construct a classifier (F) from said balanced samples by machine learning.

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DEVICE AND METHOD FOR AUTOMATIC MULTI-CLASS CLASSIFICATION BY MACHINE LEARNING

NºPublicación: EP3608847A1 12/02/2020

Solicitante:

COMMISSARIAT ENERGIE ATOMIQUE [FR]

US_2020050964_A1

Resumen de: US2020050964A1

A method and a device for multi-class classification of an application relative to a classification of a transport mode by machine learning, including: (a) sensors configured to measure at successive instants physical quantities specific to said application, and (b) a microprocessor configured to: (b1) acquire said successive measurements from the sensors to calculate predictors from physical quantities of said measurements, (b2) construct continuous series (aj) of samples, each sample being a vector formed of a predetermined number of predictors, (b3) break down each series (aj) of samples into a set of sequences (Sk) each corresponding to a specific class among a set of predetermined classes, (b4) under-sample the sequences associated with the classes among said set of predetermined classes while conserving for each selected sequence a predetermined fraction of samples of said sequence, thereby forming classes associated with balanced samples, and (b5) construct a classifier (F) from said balanced samples by machine learning.

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VEHICLE ROUTING GUIDANCE TO AN AUTHORITATIVE LOCATION FOR A POINT OF INTEREST

NºPublicación: US2020041298A1 06/02/2020

Solicitante:

UBER TECHNOLOGIES INC [US]

US_2018340787_PA

Resumen de: US2020041298A1

An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name, address, and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidate if any is an authoritative location for the POI. The candidate locations are compared using two models a metric-based scoring system and a machine learning model that may utilize a gradient boosted decision tree. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.

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SYSTEM AND METHOD FOR USING A USER-ACTION LOG TO LEARN TO CLASSIFY ENCRYPTED TRAFFIC

NºPublicación: US2020042897A1 06/02/2020

Solicitante:

VERINT SYSTEMS LTD [IL]

EP_3608845_A1

Resumen de: US2020042897A1

Machine learning techniques for classifying encrypted traffic with a high degree of accuracy. The techniques do not require decrypting any traffic and may not require any manually-labeled traffic samples. An automated system uses an application of interest to perform a large number of user actions of various types. The system further records, in a log, the respective times at which the actions were performed. The system further receives the encrypted traffic exchanged between the system and the application server, and records properties of this traffic in a time series. Subsequently, by correlating between the times in the log and the times at which the traffic was received, the system matches each of the user actions with a corresponding portion of the traffic, which is assumed to have been generated by the user action. The system thus automatically builds a labeled training set, which may be used to train a network-traffic classifier.

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METHODS AND APPARATUS FOR PERFORMING MACHINE LEARNING TO IMPROVE CAPABILITIES OF AN ARTIFICIAL INTELLIGENCE (AI) ENTITY USED FOR ONLINE COMMUNICATIONS

NºPublicación: US2020042515A1 06/02/2020

Solicitante:

SALESFORCE COM INC [US]

US_2018285413_PA

Resumen de: US2020042515A1

A method for providing query responses to a user via online chat establishes a first communication connection for online chat between a user interface and an artificial intelligence (AI) entity comprising a processor and a memory element configured to store a database of query answers; receives a user input query transmitted via the first communication connection; performs a lookup in the database of query answers, to locate a query answer corresponding to the user input query; when unable to locate a query answer, establishes a second communication connection for online chat between the user interface and a live agent interface that transmits responses dynamically provided by a human operator; evaluates a chat between the user interface and the live agent interface; identifies an answer to the user input query, based on evaluating the chat; and stores the answer to be provided by the AI entity in the future.

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PREFERENCE DATA REPRESENTATION AND EXCHANGE

NºPublicación: US2020042898A1 06/02/2020

Solicitante:

VULCAN INC [US]

WO_2020028691_A1

Resumen de: US2020042898A1

A system obtains preference information by observing interaction, on behalf of a user, with a first service. A machine learning model is trained, based on the preference information. The system stores configuration data for the machine learning model. When a second service is invoked, the system provides the configuration data based at least in part on determining that the first and second services share a common classification. The second service reconstitutes the machine learning model and adjusts the interaction based at least in part on predictions made using the reconstituted machine learning model.

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SYSTEMS AND METHODS FOR DETERMINING AN ESTIMATED TIME OF ARRIVAL

NºPublicación: US2020042885A1 06/02/2020

Solicitante:

BEIJING DIDI INFINITY TECHNOLOGY & DEV CO LTD [CN]

TW_201901474_A

Resumen de: US2020042885A1

The present disclosure relates to systems and methods for determining an estimated time of arrival. The systems may perform the methods to operate logical circuits to obtain a departure location associated with a terminal device and information relating to the departure location. The information may include one or more service providers. The system may operate the logical circuits to obtain a trained machine learning model. The system may operate the logical circuits to determine an estimated time of arrival for one of the one or more service providers to arrive at the departure location based on the information and the machine learning model.

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System, Method, and Computer Program Product for Machine-Learning-Based Traffic Prediction

NºPublicación: US2020033151A1 30/01/2020

Solicitante:

VISA INT SERVICE ASS [US]

US_10473478_B1

Resumen de: US2020033151A1

Described are a system, method, and computer program product for machine-learning-based traffic prediction. The method includes receiving historic transaction data including a plurality of transactions. The method also includes generating, using a machine-learning classification model, a transportation categorization for at least one consumer. The method further includes receiving at least one message associated with at least one transaction, identifying at least one geographic node of activity in the region, and generating an estimate of traffic intensity for the at least one geographic node of activity. The method further includes comparing the estimate of traffic intensity to a threshold of traffic intensity and, in response to determining that the estimate of traffic intensity satisfies the threshold: generating a communication configured to cause at least one navigation device to modify a navigation route; and communicating the communication to the at least one navigation device.

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METHOD AND DEVICE FOR ESTIMATING USER'S PHYSICAL CONDITION

NºPublicación: US2020034739A1 30/01/2020

Solicitante:

SAMSUNG ELECTRONICS CO LTD [KR]

Resumen de: US2020034739A1

An artificial intelligence (AI) system capable of imitating functions of the human brain, such as recognition, determination, etc., using a machine learning algorithm such as deep learning, and an application is provided. The AI system device, configured to estimate a user's physical condition, may receive first biometric data obtained by a wearable device worn by the user from the wearable device, obtain first sensing data for estimation of the user's physical condition via a sensor included in the device, and train a trained model for estimating the user's physical condition based on an artificial intelligence algorithm and by using the received first biometric data and the obtained first sensing data as training data.

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DEPLOYING MACHINE LEARNING MODELS AT COGNITIVE DEVICES FOR RESOURCES MANAGEMENT FOR ENTERPRISES

NºPublicación: US2020033904A1 30/01/2020

Solicitante:

AMBER FLUX PRIVATE LTD [IN]

US_2017322579_A1

Resumen de: US2020033904A1

A platform and method for using cognition for managing enterprise energy needs. Cognitive platform allows for dynamic management of energy consumption, demand and baseline calculations through a cognitive platform and cognitive device. Employees as well as internal and external stakeholders can set performance indicators and monitor the parameters against the energy performance indicators. Based on the initial knowledge, the system identifies improvements in order to reach the energy key performance indicators. Depending on the feedback, the system learns and improves the accuracy of the predictions and suits them to a given industry or given enterprise scenario. Enterprise-wide energy or environmental management covers policies, planning, key performance indicators, goals, targets, works flows, user management, asset mapping, input-output energy flows, conservation options, performance management, analytics. The system and method allow for monitoring and verification by internal or external stakeholders.

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CONFIGURATION MANAGEMENT DEVICE, CONFIGURATION MANAGEMENT METHOD, AND RECORDING MEDIUM

NºPublicación: US2020034723A1 30/01/2020

Solicitante:

NEC CORP [JP]

JP_WO2018174000_A1

Resumen de: US2020034723A1

A configuration management device 10 is provided with a generation means 11 for executing supervised machine learning on the basis of feature information that indicates the features of text data in which configuration information of a system is included and learning data in which the text data and the configuration information of the system are included, and thereby generating a prediction model used in predicting the configuration information of a system included in input data from input data that is the text data having the features indicated by the feature information.

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DEVICE, WHICH IS CONFIGURED TO OPERATE A MACHINE LEARNING SYSTEM

NºPublicación: US2020034715A1 30/01/2020

Solicitante:

BOSCH GMBH ROBERT [DE]

DE_202018104373_U1

Resumen de: US2020034715A1

A device for operating a machine learning system. The machine learning system is assigned a predefinable rollout, which characterizes a sequence in which each of the layers ascertains an intermediate variable. When assigning the rollout, each connection or each layer is assigned a control variable, which characterizes whether the intermediate variable of each of the subsequent connected layers is ascertained according to the sequence or regardless of the sequence. A calculation of an output variable of the machine learning system as a function of an input variable of the machine learning system is controlled as a function of the predefinable rollout. Also described is a method for operating the machine learning system.

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DETERMINING VALIDITY OF MACHINE LEARNING ALGORITHMS FOR DATASETS

Nº publicación: US2020034665A1 30/01/2020

Solicitante:

DATAROBOT INC [US]

WO_2020028440_A1

Resumen de: US2020034665A1

Apparatuses, systems, program products, and methods are disclosed for determining validity of machine learning algorithms for datasets. An apparatus includes a primary training module that is configured to train a first machine learning model for a first machine learning algorithm. An apparatus includes a primary validation module that is configured to validate a first machine learning model to generate an error data set. An apparatus includes a secondary training module that is configured to train a second machine learning model for a second machine learning algorithm using an error data set. A second machine learning algorithm may be configured to predict a suitability of a first machine learning model for analyzing an inference data set. An apparatus includes an action module that is configured to trigger an action in response to a predicted suitability of the first machine learning model not satisfying a predetermined suitability threshold.

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