MACHINE LEARNING

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Resultados 91 resultados LastUpdate Última actualización 03/10/2022 [13:04:00] pdf PDF xls XLS

Solicitudes publicadas en los últimos 30 días / Applications published in the last 30 days



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SYSTEM AND METHOD FOR MODEL ORCHESTRATION

NºPublicación: US2022308777A1 29/09/2022

Solicitante:

GRID AI INC [US]

WO_2022076440_A1

Resumen de: US2022308777A1

A system for large-scale machine learning experiment execution, including: a platform configured to determine an experiment set from a run specification and schedule a run to one or more clusters; and a set of agents configured to receive the experiment set from the platform and facilitate individual experiment execution through a cluster orchestrator.

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DATABASE QUERY PROCESSING FOR HARDWARE COMPONENT IDENTIFICATION USING MULTI-ORDERED MACHINE LEARNING MODELS

NºPublicación: US2022306314A1 29/09/2022

Solicitante:

CAMP SYSTEMS INT INC [US]

Resumen de: US2022306314A1

A device receives a data query from a source, the data query associated with a hardware component. The device determines a likelihood that an aircraft of an aircraft entity associated with the data query will require the hardware component by retrieving a plurality of signals received from the aircraft entity that are associated with the hardware component, applying the plurality of signals to a first machine-learned model, and receiving, as output from the first machine-learned model, a likelihood that the aircraft will require the hardware component. The device inputs the likelihood into a second machine-learned model, and receives as output a score corresponding to the data query. The device assigns a rank to the data query based on its score as compared to scores of other data queries of a plurality of data queries, and displays an ordered list of the plurality of data queries.

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SYSTEM AND METHOD FOR MEASURING ADVERTISEMENTS EXPOSURE IN 3D COMPUTER GAMES

NºPublicación: WO2022201151A1 29/09/2022

Solicitante:

MIRAGE DYNAMICS LTD [IL]

Resumen de: WO2022201151A1

A system for measuring the level of exposure of players to advertisements displayed in computer games, comprising a workstation comprising at least one processor, for executing the computer game; a software module installed on the workstation being adapted to copy, at a predetermined rate, frames to be analyzed, from a frame buffer of the workstation, into a memory, such as shared memory; a computerized device comprising at least one processor, for processing and analyzing the copied frames by an independent deep learning application that runs in parallel to the game application. The deep learning application being adapted to extract features from each analyzed frame using a Convolutional Neural Network (CNN) model and localize the detection region of advertisements within the each analyzed frame using a Recurrent Neural Network (RNN) model.

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METHOD, SYSTEM AND COMPUTER PROGRAM PRODUCT FOR OPTIMIZING THE DEPLOYMENT OF ANALYTICAL PIPELINES

NºPublicación: EP4064047A1 28/09/2022

Solicitante:

FUNDACION TECNALIA RES & INNOVATION [ES]
UNIV DE LA IGLESIA DE DEUSTO [ES]

Resumen de: EP4064047A1

A computer-implemented method for optimizing the deployment of an analytical pipeline in an industrial production environment, is provided. The method comprises: providing an analytical pipeline (1) to be deployed and a description of an available infrastructure (2) for the deployment of the analytical pipeline (1), wherein the analytical pipeline (1) comprises a plurality of analytical phases, wherein the description of an available infrastructure (2) comprises a plurality of computing elements located in a cloud layer and a plurality of computing devices (801-80N) located in an edge layer; at an optimization module (5) implemented in a computing device: from the analytical pipeline (1) and the available infrastructure (2), extracting a set of variables x = [Xf1, Xf2, .., XfM], and providing the set of variables to a multicriteria solver (503); at the multicriteria solver (503) executing a machine-learning algorithm configured to, using the provided set of input variables x = [Xf1, Xf2, .., XfM], provide an optimal deployment for the analytical pipeline, wherein the machine-learning algorithm minimizes a cost fitness estimation function and maximizes a performance fitness estimation function and a resilience fitness estimation function, thus minimizing the cost of the deployment while maximizing its performance and resilience.

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INVERSE LITHOGRAPHY AND MACHINE LEARNING FOR MASK SYNTHESIS

NºPublicación: EP4062230A1 28/09/2022

Solicitante:

SYNOPSYS INC [US]

CN_115087924_PA

Resumen de: WO2021118808A1

Techniques relating to synthesizing masks for use in manufacturing a semiconductor device are disclosed. A plurality of training masks, for a machine learning (ML) model, are generated by synthesizing one or more polygons, relating to a design pattern for the semiconductor device, using Inverse Lithography Technology (ILT) (106). The ML model is trained using both the plurality of training masks generated using ILT, and the design pattern for the semiconductor device, as inputs (108). The trained ML model is configured to synthesize one or more masks, for use in manufacturing the semiconductor device, based on the design pattern (110).

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METHOD, DEVICE AND SYSTEM FOR CONFIGURING A COATING MACHINE

NºPublicación: EP4063083A1 28/09/2022

Solicitante:

SIEMENS AG [DE]

US_2022308535_PA

Resumen de: EP4063083A1

The present invention provides a method (300), device (102) and system (100) for configuring a coating machine for coating a surface of a product using a coating substance. In one aspect, the method (300) includes determining a value associated with one or more parameters from a plurality of parameters associated with the coating operation. Additionally, the method (300) includes predicting a value associated with at least one attribute associable with the coating substance based on the determined value associated with the one or more parameters using a trained machine learning model. Furthermore, the method (300) includes configuring the coating machine for coating the surface using the coating substance based on the predicted value associated with the at least one attribute associable with the coating substance. Moreover, the method (300) includes initiating a coating operation at the configured coating machine for coating the surface of the product using the coating substance.

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SYSTEM AND METHOD FOR USING MACHINE LEARNING FOR TEST DATA PREPARATION AND EXPECTED RESULTS PREDICTION

NºPublicación: WO2022197541A1 22/09/2022

Solicitante:

CIGNA INTELLECTUAL PROPERTY INC [US]

US_2022300399_PA

Resumen de: WO2022197541A1

A method, for automatically identifying test case data includes receiving a plurality of data records from one or more data producer applications and classifying data records of the plurality of data records into test data clustering model. The method also includes receiving input indicating one or more test case requirements and generating, based at least on the one or more test case requirements and the test data clustering models, at least one test case blueprint indicating at least one test data clustering model that corresponds to the one or more test case requirements. The method also includes, in response to instructions to perform a data test corresponding to the test case requirements, using the at least one test case blueprint to populate test case data using data records corresponding to the at least one test data clustering model indicated by the at least one test case blueprint.

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METHOD AND APPARATUS FOR MANAGING PREDICTED POWER RESOURCES FOR AN INDUSTRIAL GAS PLANT COMPLEX

NºPublicación: AU2022201356A1 22/09/2022

Solicitante:

AIR PROD & CHEM [US]

CN_115034424_PA

Resumen de: AU2022201356A1

There is provided a method of determining and utilizing predicted available power resources from one or more renewable power sources for one or more industrial gas plants comprising one or more storage resources. The method is executed by at least one hardware processor and comprises: obtaining historical time-dependent environmental data associated with the one or more renewable power sources; obtaining historical time-dependent operational characteristic data associated with the one or more renewable power sources; training a machine learning model based on the historical time-dependent environmental data and the historical time-dependent operational characteristic data; executing the trained machine learning model to predict available power resources for the one or more industrial gas plants for a pre-determined future time period; and controlling the one or more industrial gas plants in response to the predicted available power resources for the pre-determined future time period.

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DEFINING REDUNDANT ARRAY OF INDEPENDENT DISKS LEVEL FOR MACHINE LEARNING TRAINING DATA

NºPublicación: US2022300453A1 22/09/2022

Solicitante:

IBM [US]

Resumen de: US2022300453A1

One or more computer processors determine a storage strategy for each chunked data block in a training dataset based on a respective computed usefulness score and a series of usefulness thresholds, wherein the storage strategy comprises RAID strategies that include striping, mirroring, parity, and double parity. The one or more computer processors distribute each data block in the training dataset according to the respective determined storage strategy.

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CUSTOM GESTURE COLLECTION AND RECOGNITION SYSTEM HAVING MACHINE LEARNING ACCELERATOR

NºPublicación: US2022300080A1 22/09/2022

Solicitante:

KAIKUTEK INC [TW]

Resumen de: US2022300080A1

A custom gesture collection and recognition system having a machine learning accelerator includes a transmission unit, a first reception chain, a second reception chain, a customized gesture collection engine and a machine learning accelerator. The transmission unit transmits a transmission signal to detect a gesture. The first reception chain receives a first signal and generates first feature map data corresponding to the first signal. The second reception chain receives a second signal and generates second feature map data corresponding to the second signal. The first signal and the second signal are generated by the gesture reflecting the transmission signal. The customized gesture collection engine generates gesture data according to at least the first feature map data and the second feature map data. The machine learning accelerator performs machine learning with the gesture data. The accuracy and correctness of gesture recognition may be improved by means of machine learning.

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METHOD AND APPARATUS FOR MONITORING OPERATIONAL CHARACTERISTICS OF AN INDUSTRIAL GAS PLANT COMPLEX

NºPublicación: AU2022201354A1 22/09/2022

Solicitante:

AIR PROD & CHEM [US]

CN_115034398_PA

Resumen de: AU2022201354A1

There is provided a method of monitoring operational characteristics of an industrial gas plant complex comprising a plurality of industrial gas plants. The method being executed by at least one hardware processor and comprising: assigning a machine learning model to each of the industrial gas plants forming the industrial gas plant complex; training the respective machine learning model for each industrial gas plant based on received historical time-dependent operational characteristic data for the respective industrial gas plant; executing the trained machine learning model for each industrial gas plant to predict operational characteristics for each respective industrial gas plant for a pre-determined future time period; and comparing predicted operational characteristic data for each respective industrial gas plant for a pre determined future time period with measured operational characteristic data for the corresponding time period to identify deviations in industrial gas plant performance.

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WIRELESS COMMUNICATION-BASED CLASSIFICATION OF OBJECTS

NºPublicación: US2022299593A1 22/09/2022

Solicitante:

A D KNIGHT LTD [IL]

JP_2022535454_A

Resumen de: US2022299593A1

A method comprising receiving a dataset comprising data associated with a plurality of radio frequency (RF) wireless transmissions associated with a plurality of objects within a plurality of physical scenes, wherein the dataset comprises, with respect to each of the objects, at least: (i) signal parameters of the associated wireless transmissions, (ii) data included in the associated wireless transmissions, and (iii) locational parameters with respect to the object; at a training stage, training a machine learning model on a training set comprising the dataset and labels indicating a type of each of said objects; and at an inference stage, applying the trained machine learning model to a target dataset comprising signal parameters, data, and locational parameters obtained from wireless transmissions associated with a target object within a physical scene, to predict a type of the target object.

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Machine Learning Techniques for Generating Visualization Recommendations

NºPublicación: US2022300836A1 22/09/2022

Solicitante:

ADOBE INC [US]

Resumen de: US2022300836A1

A visualization recommendation system generates recommendation scores for multiple visualizations that combine data attributes of a dataset with visualization configurations. The visualization recommendation system maps meta-features of the dataset to a meta-feature space and configuration attributes of the visualization configurations to a configuration space. The visualization recommendation system generates meta-feature vectors that describe the mapped meta-features, and generates configuration attribute sets that describe the attributes of the visualization configurations. The visualization recommendation system applies multiple scoring models to the meta-feature vectors and configuration attribute sets, including a wide scoring model and a deep scoring model. In some cases, the visualization recommendation system trains the multiple scoring models using the meta-feature vectors and configuration attribute sets.

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END-TO-END MACHINE LEARNING PIPELINES FOR DATA INTEGRATION AND ANALYTICS

NºPublicación: US2022300850A1 22/09/2022

Solicitante:

DATA GRAN INC [US]

WO_2022197669_PA

Resumen de: US2022300850A1

Exemplary embodiments of the present disclosure provide for end-to-end data pipelines (including data source, transformation of data, Machine Learning algorithms and sending the output to applications) using graphical blocks representing executable code which translate into users being able to run and deploy ML models without coding. Embodiments of the present disclosure can organize data by workspaces and projects specified in the workspace, where multiple users can access and collaborate in the workspaces and projects. The pipelines can be specified for the projects and can allow a user to access and perform operations on data from disparate data sources using one or more operators include graphical blocks that represent executable code for one or more machine learning algorithms.

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PREDICTIVE DATA ANALYSIS TECHNIQUES USING GRAPH-BASED CODE RECOMMENDATION MACHINE LEARNING MODELS

NºPublicación: US2022300835A1 22/09/2022

Solicitante:

OPTUM TECH INC [US]

Resumen de: US2022300835A1

Solutions for more efficient and effective predictive code recommendation are disclosed. In one example, a method includes identifying a graph-based code recommendation machine learning model, wherein each inferred edge weight value of the graph-based code recommendation machine learning model is updated based at least in part on each compressed forward-adjusted temporal distance measure for an observed co-occurrence of any observed co-occurrences of a predictive code pair for the inferred edge weight value within one or more temporally-proximate occurrence subsets determined based at least in part on a plurality of training predictive code occurrences; processing the input predictive code using the graph-based code recommendation machine learning model to generate one or more related codes of the plurality of predictive codes for the input predictive code; and performing one or more prediction-based actions based at least in part on the one or more related codes.

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DATA MARK CLASSIFICATION TO VERIFY DATA REMOVAL

NºPublicación: US2022300837A1 22/09/2022

Solicitante:

IBM [US]

Resumen de: US2022300837A1

A method, computer system, and a computer program product for testing a data removal are provided. Data elements are marked with a respective mark per represented entity. The marked data elements, with labels indicating the respective marks, are input into a machine learning model to form a trained machine learning model. The trained machine learning model is configured to perform a dual task that includes a main task and a secondary task that includes a classification based on the labels. A forgetting mechanism is applied to the trained machine learning model to remove a data element including a test mark of the marked data elements. A test data element marked with the test mark is input into the revised machine learning model. The classification of the secondary task of an output of the revised machine learning model is determined for the input test data element.

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COMPRESSION OF UNIFORM RESOURCE LOCATOR SEQUENCES FOR MACHINE LEARNING-BASED DETECTION OF TARGET CATEGORY EXAMPLES

NºPublicación: US2022303306A1 22/09/2022

Solicitante:

AT & T IP I LP [US]

Resumen de: US2022303306A1

A processing system may identify a plurality of uniform resource locators associated with a target category of a plurality users of a communication network, identify a plurality of sequences of URLs, each sequence comprising URLs from among the plurality of URLs, each sequence associated with a user known to be of the target category, and train a machine learning model with the plurality of sequences to detect additional sequences that are indicative of the target category. The processing system may next obtain a set of URLs associated with an additional user, identify a sequence comprising URLs, from among the plurality of URLs, that are contained within the set of URLs, apply the sequence as an input to the machine learning model that has been trained, and obtain an output of the machine learning model quantifying a measure of which the sequence is indicative of the target category.

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METHOD, SYSTEM, AND COMPUTER PROGRAM FOR ARTIFICIAL INTELLIGENCE ANSWER

NºPublicación: US2022300715A1 22/09/2022

Solicitante:

42 MARU INC [KR]

US_2021081616_A1

Resumen de: US2022300715A1

Provided is an artificial intelligence (AI) answering system including a user question receiver configured to receive a user question from a user terminal; a first question extender configured to generate a question template by analyzing the user question and determine whether the user question and the generated question template match; a second question extender configured to generate a similar question template by using a natural language processing and a deep learning model when the user question and the generated question template do not match; a training data builder configured to generate training data for training the second question extender by using an neural machine translation (NMT) engine; and a question answering unit configured to transmit a user question result derived through the first question extender or the second question extender to the user terminal.

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EARLY PATTERN DETECTION IN DATA FOR IMPROVED ENTERPRISE OPERATIONS

NºPublicación: US2022300854A1 22/09/2022

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

Resumen de: US2022300854A1

Implementations of the present disclosure include receiving a goal, providing a problem-specific knowledge graph that is responsive to at least a portion of the goal, determining a set of events from the problem-specific knowledge graph, processing data representative of events in the set of events through a first machine learning (ML) model to provide a set of event scores, each event score in the set of event scores being associated with a respective event in the set of events, determining a sub-set of events based on the set of event scores, for each event in the sub-set of events, determining at least one action by processing a sequence of actions through a second ML model, and outputting the sub-set of events and a set of actions for execution of at least one action in the set of actions.

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DETERMINING CAUSES OF DISEASES SUCH AS CANCER, USING MACHINE LEARNING ANALYSIS OF GENETIC DATA

NºPublicación: US2022301710A1 22/09/2022

Solicitante:

UNIV JOHNS HOPKINS [US]

WO_2020247752_A1

Resumen de: US2022301710A1

This document describes technology that can be used for detecting an etiological factor of a disease in a subject having the disease, training data is received that includes data objects each recording i) a disease label, ii) at least one corresponding mutational signature, and iii) corresponding etiological tags. A first set of features based on single nucleotide mutations and a second set of features based on dinucleotide mutations are generated. A machine learning model is trained on the first set of features and on the second set of features. A classifier is generated that is configured to: operate by receiving a new-genomic-data-object, the new-genomic-data-object specific to the subject having the disease; and generate, from the new-genomic-data-object, a etiological-classification for the new-genomic-data-object, the etiological-classification indicating a corresponding etiological factor that matches one of the etiological tags.

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WIRELESS DEVICE POWER OPTIMIZATION UTILIZING ARTIFICIAL INTELLIGENCE AND/OR MACHINE LEARNING

NºPublicación: EP4059274A1 21/09/2022

Solicitante:

SCHLAGE LOCK CO LLC [US]

CA_3158478_PA

Resumen de: US2021144634A1

A method of reducing a power consumption of wireless communication circuitry of an edge device according to one embodiment includes determining a delivery traffic indication map (DTIM) interval of a wireless access point communicatively coupled to the edge device via the wireless communication circuitry of the edge device and adjusting a wake-up interval of the wireless communication circuitry of the edge device based on the DTIM interval to reduce the power consumption of the wireless communication circuitry of the edge device.

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SYSTEMS AND METHODS FOR MACHINE LEARNING APPROACHES TO MANAGEMENT OF HEALTHCARE POPULATIONS

NºPublicación: EP4058948A1 21/09/2022

Solicitante:

GEISINGER CLINIC [US]

KR_20220102634_PA

Resumen de: US2021151191A1

A method for providing treatment recommendations for a patient to a physician is disclosed. The method includes receiving health information associated with the patient, determining a first risk score for the patient based on the health information using a trained predictor model, determining a second risk score for the patient based on the health information and at least one artificially closed care gap included in the health information using the predictor model, determining a predicted risk reduction score based on the first risk score and the second risk score, determining a patient classification based on the predicted risk reduction score, and outputting a report based on at least one of the first risk score, the second risk score, or the predicted risk reduction score.

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SYSTEMS AND METHODS FOR IDENTIFYING DISTRACTED DRIVING EVENTS USING COMMON FEATURES

NºPublicación: US2022292605A1 15/09/2022

Solicitante:

BLUEOWL LLC [US]

Resumen de: US2022292605A1

A distracted driving analysis system for identifying distracted driving events is provided. The system includes a processor in communication with a memory device, and the processor is programmed to: (i) receive labeled training data, the labeled training data including driving event records (a) each labeled as an actual distracted driving event or a passenger event and (b) including phone usage by a user that occurred within a time period of a driving event, (ii) identify common features of the actual distracted driving events and the passenger events by processing the training data using a supervised machine learning algorithm, (iii) generate a trained model based at least in part upon the identified common features, (iv) process a new driving event, (v) assign the new driving event based at least in part upon features of the new driving event, and/or (vi) determine whether the new driving event is an actual distracted driving event or a passenger event.

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FORECASTING METHOD WITH MACHINE LEARNING

NºPublicación: US2022291420A1 15/09/2022

Solicitante:

THE TOMORROW COMPANIES INC [US]

US_2020132884_PA

Resumen de: US2022291420A1

The systems and methods described herein provide a mechanism for collecting information from a diverse suite of sensors and systems, calculating the current precipitation, atmospheric water vapor, or precipitable water and other atmospheric-based phenomena based upon these sensor readings, and predicting future precipitation and atmospheric-based phenomena.

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METHODS AND APPARATUS FOR CAMPAIGN MAPPING FOR TOTAL AUDIENCE MEASUREMENT

Nº publicación: US2022292528A1 15/09/2022

Solicitante:

NIELSEN CO US LLC [US]

WO_2019140263_A1

Resumen de: US2022292528A1

Example methods and apparatus disclosed herein include campaign mapping for total audience measurement. An example apparatus includes processor circuitry to train a machine learning model to determine first and second estimated duplication factors for respective first and second reference media campaigns; and determine, using the machine learning model, third estimated duplication factors for a query media campaign based on total exposure metrics associated with individual ones of media platforms for the query media campaign. The processor circuitry to select one of the first and second reference media campaigns based on a comparison of the third estimated duplication factors with each of the first and second estimated duplication factors; and determine fourth estimated duplication factors for the query media campaign based on (a) the respective first or second estimated duplication factors associated with the selected one of the first and second reference media campaigns and (b) the total exposure metrics.

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