MACHINE LEARNING

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Resultados 168 resultados LastUpdate Última actualización 05/07/2020 [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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METHOD AND APPARATUS FOR DETERMINING AN IDENTITY OF AN UNKNOWN INTERNET-OF-THINGS (IoT) DEVICE IN A COMMUNICATION NETWORK

NºPublicación: US2020211721A1 02/07/2020

Solicitante:

UNIV SINGAPORE TECHNOLOGY & DESIGN [SG]
B G NEGEV TECHNOLOGIES AND APPLICATIONS LTD AT BEN GURION UNIV [IL]

WO_2018160136_PA

Resumen de: US2020211721A1

A method and apparatus for determining an identity of an unknown Internet-of-Things (IoT) device in a communication network is disclosed. The method includes the steps of receiving network traffic generated by the unknown IoT device, extracting device network behavior from the generated network traffic, and determining the identity of the unknown IoT device from a list of known IoT devices by applying a selected machine learning based classifier from a set of machine learning based classifiers to analyze the device network behavior. Each machine learning based classifier of the set is trained by a dataset including a plurality of features representing network behavior of a respective known IoT device from the list and the known IoT device's identity. The plurality of features is associated with the corresponding device network behavior of the generated network traffic.

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SYSTEMS AND METHODS FOR AUTOMATED VIDEO CLASSIFICATION

NºPublicación: WO2020139923A1 02/07/2020

Solicitante:

FACEBOOK INC [US]

Resumen de: WO2020139923A1

Systems, methods, and non-transitory computer-readable media can receive a set of video frames associated with a video. Dynamic regions in each video frame of the set of video frames can be filtered out, wherein each dynamic region represents a region in which a threshold level of movement is detected. A determination can be made for each video frame of the set of filtered video frames, whether the video frame comprises synthetic overlaid text based on a machine learning model.

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ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING BASED INCIDENT MANAGEMENT

NºPublicación: US2020210924A1 02/07/2020

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

Resumen de: US2020210924A1

In some examples, artificial intelligence and machine learning based incident management may include analyzing incident data related to a plurality of incidents associated with organization operations of an organization to train and test a machine learning classification model. Based on mapping of the organization operations to associated organizational key performance indicators, a corpus may be generated and used to determine an organizational key performance indicator that is impacted by each incident. New incident data related to a further plurality of incidents may be ascertained, and specified organizational key performance indicators associated with further organizational operations may be determined. Based on the corpus and the trained machine learning classification model, an output that includes an organization operation impacted by an incident, and a specified organizational key performance indicator associated with the organizational operation may be determined, and used to control an operation of a system associated with the organization.

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SET OF NEURAL NETWORKS

NºPublicación: US2020210814A1 02/07/2020

Solicitante:

DASSAULT SYSTEMES [FR]

EP_3674984_A1

Resumen de: US2020210814A1

The disclosure notably relates to a computer-implemented method of machine-learning. The method includes obtaining a dataset including 3D modeled objects which each represent a respective mechanical part and further includes providing a set of neural networks. Each neural network has respective weights. Each neural network is configured for inference of 3D modeled objects. The method further includes modifying respective weights of the neural networks by minimizing a loss. For each 3D modeled object, the loss selects a term among a plurality of terms. Each term penalizes a disparity between the 3D modeled object and a respective 3D modeled object inferred by a respective neural network of the set. The selected term is a term among the plurality of terms for which the disparity is the least penalized. This constitutes an improved method of machine-learning with a dataset including 3D modeled objects which each represent a respective mechanical part.

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SYSTEM AND METHOD FOR FAULT DETECTION OF COMPONENTS USING INFORMATION FUSION TECHNIQUE

NºPublicación: US2020210854A1 02/07/2020

Solicitante:

UTOPUS INSIGHTS INC [US]

Resumen de: US2020210854A1

An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.

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ADAPTIVE DEVICE TYPE CLASSIFICATION

NºPublicación: US2020210871A1 02/07/2020

Solicitante:

AVAST SOFTWARE SRO [CZ]

Resumen de: US2020210871A1

Systems and methods for device type classification system include a rules engine and a machine learning engine. The machine learning engine can be trained using device type data from multiple networks. The machine learning engine and the rules engine can receive data for devices on a network at a first point in time. The data can be submitted to a rules engine and the machine learning engine, which each produce device type probabilities for devices on the network. The device type probabilities from the rules engine and the machine learning engine can be processed to determine device types for one or more devices on the network. As more data becomes available at later points in time, the additional data can be provided to the rules engine and the machine learning engine to update the device type determinations for the network.

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MULTI-CLIENT SERVICE SYSTEM PLATFORM

NºPublicación: US2020210867A1 02/07/2020

Solicitante:

HUBSPOT INC [US]

Resumen de: US2020210867A1

A modular machine learning-as-a-service (MLAAS) system uses machine learning to respond to tasks without requiring machine learning modeling or design knowledge by its users. The MLAAS system receives an inference request including a model identifier and a target defining features for use in processing the inference request. The features correspond to a task for evaluation using a machine learning model associated with the model identifier. An inference outcome is generated by processing the inference request using the target as input to the model. Feedback indicating an accuracy of the inference outcome with respect to the task is later received and used to generate a training data set, which the MLAAS can use to further train model used to generate the inference outcome. As a result, the training of a machine learning model by the MLAAS system is limited to using data resulting from an inference performed using that model.

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SYSTEMS AND METHODS FOR MACHINE LEARNING IN PATIENT PLACEMENT

NºPublicación: US2020210868A1 02/07/2020

Solicitante:

TELETRACKING TECH INC [US]

EP_3675132_A1

Resumen de: US2020210868A1

The present disclosure relates to systems and methods for determining one or more appropriate treatment facilities for patient placement. A machine learning system may perform operations. The operations may receive a patient data set including patient attributes and patient location metrics; access a plurality of data sets for a plurality of medical facilities, the plurality of data sets include facility capacity, location, and capability metrics for each of the plurality of medical facilities; apply a model to match the patient data set with at least one of the plurality of medical facility data sets based on at least one of the patient attributes and patient location metrics and at least one of the facility capacity, location, and capability metrics; determine, based on the application of the model, one or more likelihoods of acceptance associated with each match.

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JOINT OPTIMIZATION OF ENSEMBLES IN DEEP LEARNING

NºPublicación: US2020210812A1 02/07/2020

Solicitante:

D5AI LLC [US]

WO_2019067542_PA

Resumen de: US2020210812A1

Computer-implemented, machine-learning systems and methods relate to a combination of neural networks. The systems and methods train the respective member networks both (i) to be diverse and yet (ii) according to a common, overall objective. Each member network is trained or retrained jointly with all the other member networks, including member networks that may not have been present in the ensemble when a member is first trained.

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MACHINE LEARNING BASED FUNCTION TESTING

NºPublicación: US2020210882A1 02/07/2020

Solicitante:

ESURANCE INSURANCE SERVICES INC [US]

Resumen de: US2020210882A1

A method for determining the performance metric of a function may include interpolating the performance metric of the function relative to a known performance metric of a reference function. The performance metric of the function may be interpolated based on a first difference in a performance of the function measured by applying a first machine learning model and a performance of the function measured by applying a second machine learning model. The performance metric of the function may be further interpolated based on a second difference in a performance of the reference function measured by applying the first machine learning model and a performance of the reference function measured by applying the second machine learning model. The function may be deployed to a production system if the performance metric of the function exceeds a threshold value. Related systems and articles of manufacture, including computer program products, are also provided.

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SYSTEMS AND METHODS FOR MACHINE LEARNING USING ADIABATIC QUANTUM COMPUTERS

NºPublicación: US2020210876A1 02/07/2020

Solicitante:

D WAVE SYSTEMS INC [CA]

CN_108351987_A

Resumen de: US2020210876A1

A computational system can include digital circuitry and analog circuitry, for instance a digital processor and a quantum processor. The quantum processor can operate as a sample generator providing samples. Samples can be employed by the digital processing in implementing various machine learning techniques. For example, the digital processor can operate as a restricted Boltzmann machine. The computational system can operate as a quantum-based deep belief network operating on a training data-set.

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DETERMINING AVAILABILITY OF NETWORK SERVICE

NºPublicación: US2020210885A1 02/07/2020

Solicitante:

HUGHES NETWORK SYSTEMS LLC [US]

Resumen de: US2020210885A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining availability of network service. In some implementations, a request indicating a location and a communication service level is received. A first subset of service providers or communication technologies is determined based on outputs generated by multiple first machine learning models each trained to predict service availability for different service providers or communication technologies. A second subset is selected from the first subset based on outputs generated by multiple second machine learning models trained to predict availability of different communication service levels for different service providers or communication technologies. At least one service provider or communication technology is selected from the second subset based on output generated by a third machine learning model. A response to the request indicating the selected service provider or communication technology is provided.

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SYSTEMS AND METHODS FOR PROTECTING AGAINST MALWARE CODE INJECTIONS IN TRUSTED PROCESSES BY A MULTI-TARGET INJECTOR

NºPublicación: US2020210580A1 02/07/2020

Solicitante:

ACRONIS INT GMBH [CH]

Resumen de: US2020210580A1

Disclosed are systems and methods for detecting multiple malicious processes. The described techniques identify a first process and a second process launched on a computing device. The techniques receive from the first process a first execution stack indicating at least one first control point used to monitor at least one thread associated with the first process, and receive from the second process a second execution stack indicating at least one second control point used to monitor at least one thread associated with the second process. The techniques determine that both the first process and the second process are malicious using a machine learning classifier on the at least one first control point and the at least one second control point. In response, the techniques generate an indication that an execution of the first process and the second process is malicious.

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Machine-Learning Driven Database Management

NºPublicación: US2020210387A1 02/07/2020

Solicitante:

TERADATA US INC [US]

Resumen de: US2020210387A1

A machine-learning driven Database Management System (DBMS) is provided. One or more machine-learning algorithms are trained on the database constructs and execution plans produced by a database optimizer for queries. The trained machine-learning algorithms provide predictors when supplied the constructs and plans for a given query. The predictors are processed by the DBMS to make resource, scheduling, and Service Level Agreement (SLA) compliance decisions with respect to the given query.

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MACHINE LEARNING ARTIFICIAL INTELLIGENCE SYSTEM FOR PREDICTING HOURS OF OPERATION

NºPublicación: US2020210910A1 02/07/2020

Solicitante:

CAPITAL ONE SERVICES LLC [US]

CA_3040646_A1

Resumen de: US2020210910A1

An artificial intelligence system for communicating predicted hours of operation to a client device. The system may include a processor in communication with a client device and a database; and a storage medium storing instructions. When executed, the instructions in the storage medium configure the processor to: receive, from the client device, a request for hours of operation of a merchant, the request specifying a day of the week; obtain, from the database in response to the request, a set of credit card authorizations associated with the merchant; determine a selected day authorizations subset by selecting, from the set of credit card authorizations, credit card authorizations issued on the specified day of the week; generate a posted transaction array based on the selected day authorizations subset, the posted transaction array may include a plurality of time intervals and numbers of transactions for the time intervals; generate a predictions list based on the posted transaction array, the predictions list including the time intervals and prediction indications for the time intervals; and communicate the predictions list to the client device.

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ST-GRAPH LEARNING BASED DECISION FOR AUTONOMOUS DRIVING VEHICLE

NºPublicación: US2020209872A1 02/07/2020

Solicitante:

BAIDU USA LLC [US]

Resumen de: US2020209872A1

In one embodiment, a data processing system for an autonomous driving vehicle (ADV) includes a processor, and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations. The operations include generating a station-time (ST) graph based on perception data obtained from one or more sensors of the ADV, the ST graph including representing a location of an obstacle at different points in time, obtaining a tensor based on the ST graph, the tensor including a plurality of layers, the plurality of layers including a first layer having data representing one or more obstacles on a path in which the ADV is moving, applying a machine-learning model to the plurality of layers of the tensor to generate a plurality of numerical values, the plurality of numerical values defining a potential path trajectory of the ADV, and determining a path trajectory of the ADV based on the plurality of numerical values.

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METHOD AND APPARATUS FOR CONTROLLING GAME APPLICATIONS

NºPublicación: US2020206621A1 02/07/2020

Solicitante:

SAMSUNG ELECTRONICS CO LTD [KR]

CN_109731336_A

Resumen de: US2020206621A1

A method and apparatus for controlling game applications are provided. In the method, when an operating system receives a game starting command, the operating system determines a manner to start a corresponding game application according to whether the game application has resided in a memory, and when a cold boot manner is used, the operating system triggers the game application to report an amount of memory required currently by the game application, and determines whether a requirement of running the game application is met according to the amount of memory required and an amount of memory currently used, or the operating system ensures to meet the requirement of running the game application through background application freezing and clearing. When the game application finishes running, the operating system uses a pre-trained machine learning model to predict running hotness of the game application on the terminal device according to a current operating parameter of the game application, sorts game applications on the terminal device according to the running hotness, and performs a corresponding residing process when determining that the game application needs to reside in the memory according to a sorting result. The method can efficiently shorten the time cost to start the game application.

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METHOD AND SYSTEM FOR MACHINE LEARNING BASED ITEM MATCHING BY CONSIDERING USER MINDSET

NºPublicación: US2020211036A1 02/07/2020

Solicitante:

TATA CONSULTANCY SERVICES LTD [IN]

Resumen de: US2020211036A1

Existing approaches for item matching that are used for retail strategies are based on similarity matching, however, do not consider user mindset, magnitude present across quantitative AVs and segment specific customer interest on certain qualitative AVs. Embodiments of the present disclosure provide a method and system for Machine Learning (ML) based item matching by considering user mindset, magnitude present across quantitative AVs and segment specific customer interest on certain qualitative AV. The item matching approach disclosed, performs data analytics at the AV level to identify possible close matching items from the list of available partially matching as well as non-matching items. The method disclosed primarily performs Attribute (AT) enrichment by quantizing all the qualitative AVs to be analyzed. Weights are assigned to all the quantized AVs based on a Demand Transfer (DT) value provided by a Customer Decision Tree (CDT), wherein the CDT captures the user mindset.

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SYSTEMS AND METHODS FOR INTELLIGENT AND INTERPRETIVE ANALYSIS OF SENSOR DATA AND GENERATING SPATIAL INTELLIGENCE USING MACHINE LEARNING

NºPublicación: US2020210762A1 02/07/2020

Solicitante:

AMBIENT AI INC [US]

US_2019347518_PA

Resumen de: US2020210762A1

Systems and methods for augmenting real-time semantic information to a spatial rendering of a predefined space and providing a real-time situational awareness feed.

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

NºPublicación: US2020198128A1 25/06/2020

Solicitante:

FANUC CORP [JP]

DE_102019219332_A1

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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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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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 MACHINE LEARNING ENHANCED INTELLIGENT BUILDING ACCESS ENDPOINT SECURITY MONITORING AND MANAGEMENT

NºPublicación: US2020202136A1 25/06/2020

Solicitante:

AMBIENT AI INC [US]

Resumen de: US2020202136A1

Systems and methods for correlating access-system primitives generated by an access control system and semantic primitives generated by a sensor data comprehension system.

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HEALTHCARE SYSTEMS AND METHODS USING VOICE INPUTS

Nº publicación: 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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