MACHINE LEARNING

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Resultados 87 resultados LastUpdate Última actualización 30/09/2022 [15:01:00] pdf PDF xls XLS

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



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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]

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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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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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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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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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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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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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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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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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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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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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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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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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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DETECTING RANSOMWARE IN SECONDARY COPIES OF CLIENT COMPUTING DEVICES

NºPublicación: US2022292188A1 15/09/2022

Solicitante:

COMMVAULT SYSTEMS INC [US]

US_2022292196_PA

Resumen de: US2022292188A1

An information management system includes one or more client computing devices in communication with a storage manager and a secondary storage computing device. The storage manager manages the primary data of the one or more client computing devices and the secondary storage computing device manages secondary copies of the primary data of the one or more client computing devices. Each client computing device may be configured with a ransomware protection monitoring application that monitors for changes in their primary data. The ransomware protection monitoring application may input the changes detected in the primary data into a machine-learning classifier, where the classifier generates an output indicative of whether a client computing device has been affected by malware and/or ransomware. Using a virtual machine host, a virtual machine copy of an affected client computing device may be instantiated using a secondary copy of primary data of the affected client computing device.

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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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SYSTEM AND METHOD TO CAPTURE DATA TRANSITIONS AND SELECT OPTIMAL DATA INTENSIVE MACHINE LEARNING MODEL

NºPublicación: US2022292309A1 15/09/2022

Solicitante:

IBM [US]

Resumen de: US2022292309A1

A computer-implemented system, method and computer program product for capturing data transitions and selecting machine learning models that includes: providing a machine learning model trained with a previous training data set; receiving a new data set; comparing the new data set to the previous data set; and identifying and recording new, removed, and/or changed set of attributes added to the new data set. Future possible data state transitions are generated based upon the present data state; and an implication tree is generated based upon the present data state, the pass-through data states, and the future possible data state transitions. Performance metrics of each node in the implication tree are clustered, the nodes demonstrating a high variance optionally are discarded; reachability scores for remaining nodes are calculated; and a node (representing a machine learning model to run) is selected based upon its reachability score.

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METHOD AND SYSTEM FOR MACHINE LEARNING BASED USER EXPERIENCE EVALUATION FOR INFORMATION TECHNOLOGY SUPPORT SERVICES

NºPublicación: US2022292386A1 15/09/2022

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

EP_4057205_A1

Resumen de: US2022292386A1

Embodiments of this disclosure include a method and system for machine learning based evaluation of user experience on information technology (IT) support service. The method may include obtaining a field data of an IT support service ticket and obtaining a multi-score prediction engine. The method may further include predicting metric scores of a plurality of IT support service metrics for the support service ticket based on the field data by executing the multi-score prediction engine. The method may further include obtaining system-defined weights and user-defined weights for the plurality of service metrics and calculating a support service score for the support service ticket based on the metric scores, the system-defined weights, and the user-defined weights. The method may further include evaluating user experience based on the support service score.

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INTERPRETABLE MODEL CHANGES

NºPublicación: US2022292391A1 15/09/2022

Solicitante:

IBM [US]

Resumen de: US2022292391A1

In a method for interpreting output of a machine learning model, a processor receives a first interpretable rule set. A processor may also receive a second interpretable rule set generated from a dataset and model-predicted labels classifying the dataset. A processor may also generate a difference metric and mapping between the first interpretable rule set and the second interpretable rule set.

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UTILIZING MACHINE LEARNING MODELS TO GENERATE INITIATIVE PLANS

NºPublicación: US2022292393A1 15/09/2022

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

Resumen de: US2022292393A1

A device may receive and process client data, with a first machine learning model, to determine current state data identifying a current state of a client. The device may process the current state data and prior client data, with a second machine learning model, to determine a problem statement for the client and future state data of the client. The device may utilize the second machine learning model to identify initiatives for the client, and costs of the initiatives, based on the problem statement, the current state data, and the future state data, and to assign benefits and priorities to the initiatives. The device may process the initiatives, the benefits and priorities of the initiatives, and the costs of the initiatives, with the second machine learning model, to generate an initiative plan for solving a problem of the problem statement, and may perform actions based on the initiative plan.

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IDENTIFYING OPTIMAL WEIGHTS TO IMPROVE PREDICTION ACCURACY IN MACHINE LEARNING TECHNIQUES

Nº publicación: US2022292401A1 15/09/2022

Solicitante:

IBM [US]

DE_112020005610_T5

Resumen de: US2022292401A1

A computer-implemented method, system and computer program product for improving prediction accuracy in machine learning techniques. A teacher model is constructed, where the teacher model generates a weight for each data case. The current student model is then trained using training data and the weights generated by the teacher model. After training the current student model, the current student model generates state features, which are used by the teacher model to generate new weights. A candidate student model is then trained using training data and these new weights. A reward is generated by comparing the current student model with the candidate student model using training and testing data, which is used to update the teacher model if a stopping rule has not been satisfied. Upon a stopping rule being satisfied, the weights generated by the teacher model are deemed to be the “optimal” weights which are returned to the user.

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