MACHINE LEARNING

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Resultados 127 resultados LastUpdate Última actualización 29/09/2020 [23:47:00] pdf PDF xls XLS

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



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LINK ADAPTATION OPTIMIZATION WITH CONTEXTUAL BANDITS

NºPublicación: WO2020190182A1 24/09/2020

Solicitante:

ERICSSON TELEFON AB L M [SE]

Resumen de: WO2020190182A1

Methods and systems for dynamically selecting a link adaptation policy, LAP. In some embodiments, the method includes using channel quality information, additional information, and a machine learning, ML, model to select a LAP from a set of predefined LAPs, the set of predefined LAPs comprising a first LAP and a second LAP. In some embodiments, the additional information comprises: neighbor cell information about a second cell served by a second TRP, distance information indicating a distance between a UE and a first TRP, and/or gain information indicating a radio propagation gain between the UE and the serving node. The method further includes the first TRP transmitting data to the UE using the selected LAP.

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MACHINE LEARNING APPARATUS, CONTROL DEVICE, LASER MACHINE, AND MACHINE LEARNING METHOD

NºPublicación: US2020301403A1 24/09/2020

Solicitante:

FANUC CORP [JP]

JP_2020151725_A

Resumen de: US2020301403A1

A machine learning apparatus able to obtaining an optimal shift amount of an assist gas. The machine learning apparatus comprises a state-observation section configured to observe machining condition data included in a machining program given to the laser machine, and measurement data of a dimension of dross generated at a cutting spot of the workpiece when the machining program is executed, as a state variable representing a current state of an environment in which the workpiece is cut; and a learning section configured to learn the shift amount in association with cutting quality of the workpiece, using the state variable.

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A DATA-DRIVEN SYSTEM & METHOD FOR ASSESSING & EVALUATING INNOVATION

NºPublicación: WO2020188599A1 24/09/2020

Solicitante:

RAUT NITESH [IN]

Resumen de: WO2020188599A1

Described herein is a system for designing machine learning and deep learning models for an embedded platform. The system includes a storage subsystem configured to store a plurality of search results a program generation subsystem operatively coupled to the storage subsystem and configured to create a program for a selected economics & analytics system in the economic dataset model and a display device subsystem for displaying the augmented and weighted information received respectively from the storage subsystem and program generation subsystem in augmented reality. The storage subsystem includes a scientific dataset containing scientific and technology related data, the scientific dataset comprising a bifurcating module for classifying the scientific and technology related data into a dynamic component and a static component, and an economic dataset model for containing a plurality of models related to a plurality of economics and analytics systems.

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NETWORK ROUTE STABILITY CHARACTERIZATION

NºPublicación: US2020304386A1 24/09/2020

Solicitante:

HEWLETT PACKARD ENTPR DEV LP [US]

Resumen de: US2020304386A1

A device may determine sample points associated with network routes within a network during a time interval, wherein each sample point that is associated with a respective network route comprises an amount of uptime for the respective network route during the time interval and a total frequency of state changes for the respective network route during the time interval. The device may generate, using an unsupervised machine learning mechanism, clusters of the sample points and may label the network routes with route stability labels based at least in part on the clusters. The device may generate, using a supervised machine learning mechanism, a route stability classifier based at least in part on the route stability labels for the network routes, and may determine, using the route stability classifier, a route stability of a new network route within the network.

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PREDICTION METHOD, MODEL LEARNING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR STORING PREDICTION PROGRAM

NºPublicación: US2020300495A1 24/09/2020

Solicitante:

FUJITSU LTD [JP]

JP_2020154785_A

Resumen de: US2020300495A1

A prediction method implemented by a computer, the method includes: receiving a classification model from a server, the classification model being a model for classifying logs of an electronic device into two or more classes, the server being a computer configured to distribute the classification model; calculating, with respect to different time points, a prediction error by using a predicted value outputted by the classification model and an actual measured value observed at each of the different time points; performing sequential machine learning for the classification model to have the prediction error satisfy a certain condition; and when a cumulative sum with respect to the prediction error of the sequential machine learning is equal to or greater than a threshold, requesting the server apparatus to relearn the classification model.

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CONTROLLING ITEM FREQUENCY USING A MACHINE-LEARNED MODEL

NºPublicación: US2020302333A1 24/09/2020

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

Resumen de: US2020302333A1

Techniques for controlling item frequency using machine learning are provides. In one technique, two prediction models are trained: one based on interaction history of multiple content items by multiple entities and the other based on predicted interaction rates and an impression count for each of multiple content items. In response to a request, a particular entity associated with the request is identified and multiple candidate content items are identified. For each identified candidate content item, the first prediction model is used to determine a predicted interaction rate, an impression count of the candidate content item is determined with respect to the particular entity, the second prediction model is used to generate an adjustment based on the impression count, and an adjusted entity interaction rate is generated based on the predicted interaction rate and the adjustment. A particular candidate content item is selected based on the generated adjusted entity interaction rates.

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METHODS AND SYSTEMS FOR OPTIMIZING THERAPY USING STIMULATION MIMICKING NATURAL SEIZURES

NºPublicación: US2020298007A1 24/09/2020

Solicitante:

NEUROPACE INC [US]

Resumen de: US2020298007A1

Systems, methods, and devices for automatic generation of a stimulation therapy that mimics electrographic activity in the brain at natural seizure termination define a stimulation therapy to be generated by an implanted component of a medical device system and delivered to a subject through identifying data characterizing a patient's seizures, especially at termination. A machine learning model identifies the seizures or seizure types from which to establish a canonical seizure or seizure type, and an algorithm translates the canonical seizure or seizure type into data that can be used to characterize a stimulation therapy. The systems, methods, and devices, include those configured to deliver the stimulation therapy that emulates the canonical seizure or seizure type when the seizure is detected, with the aim of terminating the seizure sooner than it would terminate without intervention.

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METHOD FOR GENERATING RULESETS USING TREE-BASED MODELS FOR BLACK-BOX MACHINE LEARNING EXPLAINABILITY

NºPublicación: US2020302318A1 24/09/2020

Solicitante:

ORACLE INT CORP [US]

Resumen de: US2020302318A1

Herein are techniques to generate candidate rulesets for machine learning (ML) explainability (MLX) for black-box ML models. In an embodiment, an ML model generates classifications that each associates a distinct example with a label. A decision tree that, based on the classifications, contains tree nodes is received or generated. Each node contains label(s), a condition that identifies a feature of examples, and a split value for the feature. When a node has child nodes, the feature and the split value that are identified by the condition of the node are set to maximize information gain of the child nodes. Candidate rules are generated by traversing the tree. Each rule is built from a combination of nodes in a tree traversal path. Each rule contains a condition of at least one node and is assigned to a rule level. Candidate rules are subsequently optimized into an optimal ruleset for actual use.

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SELF-LEARNING SELECTION OF INFORMATION-ANALYSIS RUNTIMES

NºPublicación: US2020302343A1 24/09/2020

Solicitante:

IBM [US]

Resumen de: US2020302343A1

A self-learning computer-based system has access to multiple runtime modules that are each capable of performing a particular algorithm. Each runtime module implements the algorithm with different code or runs in a different runtime environment. The system responds to a request to run the algorithm by selecting the runtime module or runtime environment that the system predicts will provide the most desirable results based on parameters like accuracy, performance, cost, resource-efficiency, or policy compliance. The system learns how to make such predictions through training sessions conducted by a machine-learning component. This training teaches the system that previous module selections produced certain types of results in the presence of certain conditions. After determining whether similar conditions currently exist, the system uses rules inferred from the training sessions to select the runtime module most likely to produce desired results.

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Generic Event Stream Processing for Machine Learning

NºPublicación: US2020304550A1 24/09/2020

Solicitante:

INTUIT INC [US]

US_10715570_B1

Resumen de: US2020304550A1

A method includes establishing a network connection with a source computing device and an application services computing device, receiving, via the network connection, a source event stream at the application services computing device, and extracting a sample of the source event stream. The method further includes partitioning the sample of the source event stream into fields, identifying a field data type of a field of the multiple fields in the sample, identifying a distribution of values of the field in the sample, and extrapolating, from the sample of the source event stream, extrapolated functions for the fields. Extrapolating an extrapolated function is dependent on the field data type and the distribution of the field. The method further includes transforming, based on the plurality of extrapolated functions in the configuration file, the source event stream to obtain a transformed event stream, and analyzing, by a target machine learning model, the transformed event stream.

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METHOD FOR VERIFYING LACK OF BIAS OF DEEP LEARNING AI SYSTEMS

NºPublicación: US2020302309A1 24/09/2020

Solicitante:

PROSPER FUNDING LLC [US]

US_2020302315_A1

Resumen de: US2020302309A1

A method including receiving an unknown vector including a data structure populated with unknown features describing a user. The method also includes executing a primary machine learning model (MLM) trained using a prediction data set to predict a score representing a prediction regarding the user. The prediction data set includes the unknown vector stripped of a biased data set including markers set that directly indicate that the user belongs to a cohort against which bias is to be avoided. The method also includes executing a supervisory MLM trained using the prediction data set to predict whether the user belongs to the cohort. The method also includes performing, using an industry tool, a computer-implemented action using the score after executing the primary MLM and the supervisory MLM

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TECHNIQUES FOR TRAINING MACHINE LEARNING

NºPublicación: US2020302241A1 24/09/2020

Solicitante:

BLUHAPTICS INC [US]

WO_2019113510_PA

Resumen de: US2020302241A1

A system and method are provided for training a machine learning system. In an embodiment, the system generates a three-dimensional model of an environment using a video sequence that includes individual frames taken from a variety of perspectives and environmental conditions. An object in the environment is identified and labeled, in some examples, by an operator, and a three-dimensional model of the object is created. Training data for the machine learning system is created by applying the label to the individual video frames of the video sequence, or by applying a rendering of the three-dimensional model to additional images or video sequences.

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MODEL GENERATION SYSTEM, DESIGN INFORMATION ACQUISITION SYSTEM, DESIGN SUPPORT SYSTEM, MODEL GENERATION METHOD, AND DESIGN INFORMATION ACQUISITION METHOD

NºPublicación: US2020302314A1 24/09/2020

Solicitante:

MURATA MANUFACTURING CO [JP]

JP_2020154860_A

Resumen de: US2020302314A1

A model generation system is a system for supporting the design of a transmitter circuit including a power amplifier. The model generation system includes an acquisition unit and a model generation unit. The acquisition unit acquires amplification performance data relating to performance of the transmitter circuit. The model generation unit generates an inference model on the basis of input data and output data using machine learning in which at least the amplification performance data is the input data and a characteristic value of the transmitter circuit is the output data.

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LEARNING METHOD, LEARNING APPARATUS, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR STORING LEARNING PROGRAM

NºPublicación: US2020302305A1 24/09/2020

Solicitante:

FUJITSU LTD [JP]

JP_2020154843_A

Resumen de: US2020302305A1

A learning method implemented by a computer, includes: creating an input data tensor including a local dimension and a universal dimension by partitioning series data into local units, the series data including a plurality of elements, each of the plurality of elements in the series data being logically arranged in a predetermined order; and performing machine learning by using tensor transformation in which a transformation data tensor obtained by transforming the input data tensor with a transformation matrix is outputted using a neural network, wherein the learning includes rearranging the transformation matrix so as to maximize a similarity to a matching pattern serving as a reference in the tensor transformation regarding the universal dimension of the input data tensor, and updating the matching pattern in a process of the machine learning regarding the local dimension of the input data tensor.

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OUTLIER QUANTIZATION FOR TRAINING AND INFERENCE

NºPublicación: US2020302330A1 24/09/2020

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

WO_2020190546_A1

Resumen de: US2020302330A1

Machine learning may include training and drawing inference from artificial neural networks, processes which may include performing convolution and matrix multiplication operations. Convolution and matrix multiplication operations are performed using vectors of block floating-point (BFP) values that may include outliers. BFP format stores floating-point values using a plurality of mantissas of a fixed bit width and a shared exponent. Elements are outliers when they are too large to be represented precisely with the fixed bit width mantissa and shared exponent. Outlier values are split into two mantissas. One mantissa is stored in the vector with non-outliers, while the other mantissa is stored outside the vector. Operations, such as a dot product, may be performed on the vectors in part by combining the in-vector mantissa and exponent of an outlier value with the out-of-vector mantissa and exponent.

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PLATFORM FOR DOCUMENT CLASSIFICATION

NºPublicación: US2020302165A1 24/09/2020

Solicitante:

CAPITAL ONE SERVICES LLC [US]

US_10503971_B1

Resumen de: US2020302165A1

A device obtains image data associated with a document. Using a first machine learning model, the device determines, for the document, a first classification of one of a plurality of document types and a first confidence score associated with the first classification, and a second classification of one of the plurality of document types and a second confidence score associated with the second classification based on the image data. The device determines a difference between the first confidence score and the second confidence score, compares the difference and a threshold value, and accept the first classification of the document when the difference satisfies the threshold value.

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IMPLEMENTATION OF MACHINE-LEARNING BASED QUERY CONSTRUCTION AND PATTERN IDENTIFICATION THROUGH VISUALIZATION IN USER INTERFACES

NºPublicación: US2020303071A1 24/09/2020

Solicitante:

HVH PREC ANALYTICS LLC [US]

WO_2020132468_PA

Resumen de: US2020303071A1

A computer system, computer-implemented method, and computer program product include a processor(s) (executing code) that obtains a data set(s) related to a patient population diagnosed with a medical condition a database(s). The processor(s) identifies common features, generates patterns of the common features, and generates machine learning algorithms based on the patterns to identify presence or absence of the given medical condition in an undiagnosed patient. The processor(s) compiles a training set of data and tunes the machine learning algorithms with the training set of data. The processor(s) integrates the machine learning algorithms into a graphical user interface. The processor(s) obtains data related to the undiagnosed patient via the interface and applies the machine learning algorithms to determine a probability (numerical value indicating a percentage of commonality between the data related to the undiagnosed patient and the one or more patterns) and display the probability as a score in the interface.

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ENTERPRISE NETWORK THREAT DETECTION

NºPublicación: US2020304528A1 24/09/2020

Solicitante:

SOPHOS LTD [GB]

US_2020074360_A1

Resumen de: US2020304528A1

In a threat management platform, a number of endpoints log events in an event data recorder. A local agent filters this data and feeds a filtered data stream to a central threat management facility. The central threat management facility can locally or globally tune filtering by local agents based on the current data stream, and can query local event data recorders for additional information where necessary or helpful in threat detection or forensic analysis. The central threat management facility also stores and deploys a number of security tools such as a web-based user interface supported by machine learning models to identify potential threats requiring human intervention and other models to provide human-readable context for evaluating potential threats.

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METHODS FOR USING MACHINE LEARNING AND MECHANISTIC MODELS FOR BIOLOGICAL FEATURE MAPPING WITH MULTIPARAMETRIC MRI

NºPublicación: EP3709877A2 23/09/2020

Solicitante:

MAYO FOUND MEDICAL EDUCATION & RES [US]

WO_2019100032_PA

Resumen de: WO2019100032A2

Described here are systems and methods for generating and implementing a hybrid machine learning and mechanistic model to produce biological feature maps, or other measurements of biological features, based on an input of multiparametric magnetic resonance or other images. The hybrid model can include a combination of a machine learning model and a mechanistic model that takes as an input multiparametric MRI, or other imaging, data to generate biological feature maps (e.g., tumor cell density maps), or other measures or predictions of biological features (e.g., tumor cell density). The hybrid models have capabilities of learning individual-specific relationships between imaging features and biological features.

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DISTRIBUTED LEARNING MODEL FOR FOG COMPUTING

NºPublicación: US2020293925A1 17/09/2020

Solicitante:

CISCO TECH INC [US]

US_2020293942_A1

Resumen de: US2020293925A1

The disclosed technology relates to a process for metered training of fog nodes within the fog layer. The metered training allows the fog nodes to be continually trained within the fog layer without the need for the cloud. Furthermore, the metered training allows the fog node to operate normally as the training is performed only when spare resources are available at the fog node. The disclosed technology also relates to a process of sharing better trained machine learning models of a fog node with other similar fog nodes thereby speeding up the training process for other fog nodes within the fog layer.

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SYSTEMS AND METHODS FOR AIDING HIGHER EDUCATION ADMINISTRATION USING MACHINE LEARNING MODELS

NºPublicación: US2020294167A1 17/09/2020

Solicitante:

ELLUCIAN COMPANY L P [US]

WO_2020186009_A1

Resumen de: US2020294167A1

Systems and methods applicable, for instance, to using machine learning models to aid higher education administration. Various machine learning model-based tools can be provided. Further provided can be various infrastructure software modules.

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PARTICLE-BASED INVERSE KINEMATIC RENDERING SYSTEM

NºPublicación: US2020294299A1 17/09/2020

Solicitante:

ELECTRONIC ARTS INC [US]

US_10535174_B1

Resumen de: US2020294299A1

The present disclosure provides embodiments of a particle-based inverse kinematic analysis system. The inverse kinematic system can utilize a neural network, also referred to as a deep neural network, which utilizes machine learning processes in order to create poses that are more life-like and realistic. The system can generate prediction models using motion capture data. The motion capture data can be aggregated and analyzed in order to train the neural network. The neural network can determine rules and constraints that govern how joints and connectors of a character model move in order to create realistic motion of the character model within the game application.

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METHOD AND APPARATUS FOR TRANSFERRING FROM ROBOT CUSTOMER SERVICE TO HUMAN CUSTOMER SERVICE

NºPublicación: US2020294063A1 17/09/2020

Solicitante:

ALIBABA GROUP HOLDING LTD [KY]

KR_20200095516_A

Resumen de: US2020294063A1

Methods, systems, and devices, including computer programs encoded on computer storage media for transferring a robot customer service to a human customer service are provided. One of the methods includes: obtaining conversation characteristics from at least one round of conversations between the robot customer service and a customer; obtaining state characteristics of the customer; inputting the conversation characteristics and the state characteristics into a confidence score evaluation model to obtain a confidence score evaluation value; and when the confidence score evaluation value meets a robot-to-human intervention condition, transferring the customer to the human customer service. The confidence score evaluation model is a machine learning model, comprising a linear sub-model input with the conversation characteristics and a deep neural network sub-model input with the state characteristics.

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AUTOMATED DETECTION OF CODE REGRESSIONS FROM TIME-SERIES DATA

NºPublicación: US2020293900A1 17/09/2020

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

WO_2020185475_A1

Resumen de: US2020293900A1

In non-limiting examples of the present disclosure, systems, methods and devices for detecting and classifying service issues associated with a cloud-based service are presented. Operational event data for a plurality of operations associated with the cloud-based application service may be monitored. A statistical-based unsupervised machine learning model may be applied to the operational event data. A subset of the operational event data may be tagged as potentially being associated with a code regression, wherein the subset comprises a time series of operational event data. A neural network may be applied to the time series of operational event data, and the time series of operational event data may be flagged for follow-up if the neural network classifies the time series as relating to a positive code regression category.

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APPLICATION FUNCTIONALITY OPTIMIZATION

Nº publicación: US2020293943A1 17/09/2020

Solicitante:

AUTODESK INC

Resumen de: US2020293943A1

A method, apparatus, and system provide the ability to optimize execution of an application. An application is acquired. The application includes functions, and each function has a corresponding feature flag that determines whether the corresponding function is executed. Execution conditions of execution of the application are monitored at run-time (in a machine learning module). The machine learning module recognizes a pattern relating to the execution conditions to determine a stress relating to the execution of the application. During execution of the application, the machine learning module toggles the feature flags based on the pattern and the stress such that the corresponding functions do not execute.

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