MACHINE LEARNING

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Resultados 88 resultados LastUpdate Última actualización 29/09/2022 [15:02:00] pdf PDF xls XLS

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



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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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DETECTING RANSOMWARE IN MONITORED DATA

NºPublicación: US2022292196A1 15/09/2022

Solicitante:

COMMVAULT SYSTEMS INC [US]

US_2022292188_PA

Resumen de: US2022292196A1

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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SYSTEMS AND METHODS FOR CONTROLLING COMMUNICATIONS BASED ON MACHINE LEARNED INFORMATION

NºPublicación: US2022295495A1 15/09/2022

Solicitante:

EAGLE TECH LLC [US]

Resumen de: US2022295495A1

Systems and methods for operating a communication device. The methods comprise: receiving a signal at the communication device; performing, by the communication device, one or more machine learning algorithms using at least one feature of the signal as an input to generate a plurality of scores (each score representing a likelihood that the signal was modulated using a given modulation type of a plurality of different modulation types); assigning a modulation class to the signal based on the plurality of scores; determining whether a given wireless channel is available based at least on the modulation class assigned to the signal; and selectively using the given wireless channel for communicating signals based on results of the determining.

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METHOD AND SYSTEM FOR IDENTIFYING PREDICTABLE FIELDS IN AN APPLICATION FOR MACHINE LEARNING

NºPublicación: US2022292375A1 15/09/2022

Solicitante:

TATA CONSULTANCY SERVICES LTD [IN]

Resumen de: US2022292375A1

This disclosure relates generally to identifying predictable fields in an application for machine learning (ML). With the availability of several choices for machine learning techniques, it is difficult to choose the most effective option on a specific application. In addition, the functionality/usage of fields within an application may vary across applications subject to the application's domain. Hence ML may not be efficient for all datatypes/fields. Therefore, the disclosure provides a method and system for identifying predictable fields in an application before ML technique for the predictable fields. The predictable fields are identified based on the domain of the application using a grouping technique, a pattern identification technique and optimization techniques. Further ML techniques are recommended only on identified predictable fields, thereby making the ML process more effective on the application in relevance with the application's domain.

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DYNAMIC PARAMETER COLLECTION TUNING

NºPublicación: US2022292374A1 15/09/2022

Solicitante:

SERVICENOW INC [US]

Resumen de: US2022292374A1

Collected data of a first set of parameters is received via a network from one or more devices. Using machine learning, at least a portion of the collected data of the first set of parameters is analyzed to automatically identify one or more additional data parameters to be obtained to verify a detection of an incident pattern. The one or more additional data parameters are indicated to be obtained to at least a portion of the one or more devices. Collected data responsive to the indicated one or more additional data parameters is received. Based at least in part on the responsive collected data, the detection of the incident pattern is verified and a responsive action is performed.

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MANAGING USER MACHINE LEARNING (ML) MODELS

NºPublicación: US2022292373A1 15/09/2022

Solicitante:

IBM [US]

Resumen de: US2022292373A1

A method for receiving an end-user model access data set, deriving a plurality of patterns of actions typically performed by the end-user based on analysis of the end-user model access data set, and deriving a first model deployment protocol to automatically deploy selected ML models of the plurality of ML models for the end-user when the end-user works with ML models based on the plurality of patterns of actions.

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METHODS AND APPARATUS FOR CAMPAIGN MAPPING FOR TOTAL AUDIENCE MEASUREMENT

NºPublicación: 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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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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UTILIZING MACHINE LEARNING FOR OPTIMIZATION OF PLANNING AND VALUE REALIZATION FOR PRIVATE NETWORKS

NºPublicación: US2022292529A1 15/09/2022

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

Resumen de: US2022292529A1

A device may receive network data, business data, and user configuration data associated with an entity that is a candidate for a private network and may process the business data and the user configuration data, with a classification machine learning model, to determine a network hardware equipment prediction. The device may process the network data and the business data, with a first linear regression machine learning model, to determine a business output prediction and may utilize a second linear regression machine learning model to determine a data consumption prediction based on the network hardware equipment prediction. The device may process the network hardware equipment prediction, the business output prediction, and the data consumption prediction, with a machine learning model, to determine a financial profitability prediction for the private network and may perform one or more actions based on the financial profitability prediction.

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

NºPublicación: EP4057205A1 14/09/2022

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

US_2022292386_PA

Resumen de: EP4057205A1

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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METHOD AND SYSTEM FOR GENERATING IN-GAME INSIGHTS

NºPublicación: WO2022187487A1 09/09/2022

Solicitante:

STATS LLC [US]

US_2022284311_PA

Resumen de: WO2022187487A1

A computing system receives event data that includes play-by-play information for an event. The computing system accesses a database that includes a knowledge graph related to the event. The knowledge graph includes a plurality of nodes and a plurality of edges. Each node of the plurality of nodes represents a player or a team involved in the event. The plurality of edges connects nodes of the plurality of nodes. The computing system updates the knowledge graph based on the play-by-play information. The computing system generates, via a first machine learning model, one or more insights based on the updated knowledge graph. The computing system scores, via a second machine learning model, a score for each of the one or more insights. The computing system presents a highest ranking insight of the one or more insights to one or more end users.

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AUTOMATED ROOT CAUSE ANALYSIS OF NETWORK ISSUES IN A CELLULAR NETWORK USING MACHINE LEARNING

NºPublicación: WO2022184762A1 09/09/2022

Solicitante:

ERICSSON TELEFON AB L M [SE]

Resumen de: WO2022184762A1

A computer-implemented method for analyzing issues in a cellular network is provided. The cellular network includes a plurality of cells and wherein the plurality of cells includes source cells and neighbor cells The method includes obtaining data for each cell of the plurality of cells. The method further includes building a network graph, using the data obtained, representing features of the cells, wherein, for each source cell, each neighbor cell of the source cell is ranked based on the data obtained for the neighbor cell. The method further includes identifying, using the network graph, sub-graphs for each source cell indicating a network issue, wherein each sub-graph represents features of the source cell and a set of neighbor cells selected based on rank and features of the neighbor cells. The method further includes, for each network issue of the source cell for each sub-graph, ranking each feature for each neighbor cell represented in the sub-graph for the source cell and identifying a set of ranked neighbor features. The method further includes, for each network issue of the source cell for each sub-graph, ranking each feature of the source cell for each sub-graph and identifying a set of ranked source features. The method further includes identifying, using the set of ranked neighbor features and the set of ranked source features, a feature set for all sub-graphs, wherein the feature set includes all or a reduced set of features. The method further includes, for ea

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METHOD AND SYSTEM FOR KEY PREDICTORS AND MACHINE LEARNING FOR CONFIGURING CELL PERFORMANCE

NºPublicación: WO2022186867A1 09/09/2022

Solicitante:

ENEVATE CORP [US]

US_2022285749_PA

Resumen de: WO2022186867A1

A method for key predictors and machine learning for configuring battery cell performance may include providing a cell comprising a cathode, a separator, and a silicon-dominant anode; measuring a plurality of parameters of the cell; and using a machine learning model to determine cell performance based on the plurality of measured parameters. The plurality of parameters may include initial coulombic efficiency and/or second cycle coulombic efficiency. Cells may be classified based on the determined cell performance and similarly performing cells may be binned together. A battery pack may be provided with a plurality of cells. The plurality of cells may be assessed during cycling using the machine learning model. One or more of the plurality of cells may be replaced from the battery pack when the assessing determines a different performance of the one or more of the plurality of cells. The battery pack may be in an electric vehicle.

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EXTRACTING AND SURFACING TOPIC DESCRIPTIONS FROM REGIONALLY SEPARATED DATA STORES

NºPublicación: US2022284052A1 08/09/2022

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

Resumen de: US2022284052A1

Extracting and surfacing information corresponding to individual logical topics from enterprise data stores that are separated across multiple geographic regions. A clustering service creates, by utilizing machine learning toolkits that are agnostic to the region in which data is stored, individual topics that have references to multiple shards of data that are stored in different geographic regions. The clustering service also shards the knowledge base state according to the regions from which pieces of data for the particular logical topic was extracted. For example, a first shard containing information extracted from a first document may be stored in a first region whereas a second shard containing information extracted from a second document may be stored in a second region. Responsive to user activity associated with the topic, a serving platform may identify and reconstitute these shards that are stored in different regions so as to surface the regionally extracted and sharded information on that topic to a user.

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Machine Learning-Based Interactive Visual Monitoring Tool for High Dimensional Data Sets Across Multiple KPIs

NºPublicación: US2022283695A1 08/09/2022

Solicitante:

EBAY INC [US]

US_2021232291_A1

Resumen de: US2022283695A1

Described are computing systems and methods configured to detect a small, but meaningful, anomaly within one or more metrics associated with a platform. The system displays visuals of the metrics so that a user monitoring the platform can effectively notice a problem associated with the anomaly and take appropriate action to remediate the problem. An operational visual includes a radar-based visual with a heatmap arranging metrics, and a node representing a state of the metrics. Moreover, the system uses an ensemble of unsupervised machine learning algorithms for multi-dimensional clustering of hundreds of thousands of monitored metrics. Via the visuals and the implementation of the machine learning algorithms, the described techniques provide an improved way of representing and simulating many metrics being monitored for a platform. Moreover, the techniques are configured to expose actionable and useful information associated with the platform in a manner that can be effectively interpreted.

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

NºPublicación: US2022283575A1 08/09/2022

Solicitante:

AIR PROD & CHEM [US]

AU_2022201354_A1

Resumen de: US2022283575A1

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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Method And System For Key Predictors And Machine Learning For Configuring Cell Performance

NºPublicación: US2022285749A1 08/09/2022

Solicitante:

ENEVATE CORP [US]

WO_2022186867_PA

Resumen de: US2022285749A1

Methods and systems are provided for key predictors and machine learning for configuring cell performance. One or more parameters relating to operation of a cell may be measured, via a measurement apparatus, with the cell including a cathode, a separator, and a silicon-dominant anode, and cell performance may be managed, based on the one or more parameters, with the managing including assessing the cell performance using a machine learning model. The cell may be within a battery pack that includes a plurality of cells, each of which including a cathode, a separator, and a silicon-dominant anode. One or more of the plurality of cells from the battery pack in response to a determination, based on the assessing, of a different performance of the one or more of the plurality of cells. The battery pack may be in an electric vehicle.

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Systems and methods for determining entity attribute representations

NºPublicación: AU2021255654A1 08/09/2022

Solicitante:

XERO LTD

WO_2021210992_PA

Resumen de: AU2021255654A1

A computer implemented method for determining entity attributes. The method comprises determining one or more entity identifiers, determining an entity server address of the entity based on the one or more entity identifiers, wherein the entity server address points to an entity server; verifying the entity server address transmitting a message for request for information to the entity server address, receiving entity information from the entity server; and providing, to a machine learning model, the received entity information. The machine learning model is trained to generate a numerical representations of entities based on the entity information.

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ALIGNING KNOWLEDGE GRAPHS USING SUBGRAPH TYPING

NºPublicación: US2022284309A1 08/09/2022

Solicitante:

BOSCH GMBH ROBERT [DE]

Resumen de: US2022284309A1

Systems and methods for aligning knowledge graphs. A subgraph type is assigned to each node of a plurality of nodes in a first knowledge graph and a second knowledge graph. The assigned subgraph type for each node is determined based on the labels of a plurality of edges and/or other nodes coupled to the node in the knowledge graph. An initial matching is performed to identify a plurality of candidate node-node pairs each including one node from each knowledge graph. A candidate node-node pair is identified as a valid node-node mapping based at least in part on a determination that the subgraph type combination of the candidate node-node pair matches a subgraph type combination of another candidate node-node pair that has previously been confirmed as a valid node-node mapping. In some implementations, a machine-learning model is trained to use the aligned knowledge graphs.

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Method and System for Generating In-Game Insights

NºPublicación: US2022284311A1 08/09/2022

Solicitante:

STATS LLC [US]

WO_2022187487_PA

Resumen de: US2022284311A1

A computing system receives event data that includes play-by-play information for an event. The computing system accesses a database that includes a knowledge graph related to the event. The knowledge graph includes a plurality of nodes and a plurality of edges. Each node of the plurality of nodes represents a player or a team involved in the event. The plurality of edges connects nodes of the plurality of nodes. The computing system updates the knowledge graph based on the play-by-play information. The computing system generates, via a first machine learning model, one or more insights based on the updated knowledge graph. The computing system scores, via a second machine learning model, a score for each of the one or more insights. The computing system presents a highest ranking insight of the one or more insights to one or more end users.

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UTILIZING MACHINE LEARNING MODELS TO DETERMINE ENGAGEMENT STRATEGIES FOR DEVELOPERS

NºPublicación: US2022284318A1 08/09/2022

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

Resumen de: US2022284318A1

A device may receive developer profile data identifying developers associated with different technology communities and technology domains and may assign attributes and weights to the developer profile data to generate weighted developer profile data. The device may utilize a first machine learning model, with the weighted developer profile data, to calculate similarity indexes identifying developers associated with multiple technology communities, and may process the weighted developer profile data and the similarity indexes, with a second machine learning model, to calculate developer activity scores for the technology domains and for developer classifications. The device may identify, based on the developer activity scores, a particular developer profile with a greatest developer activity score, and may process the particular developer profile, with a third machine learning model, to determine an engagement strategy for addressing the particular developer. The device may perform one or more actions based on the engagement strategy.

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INTELLIGENT GUIDANCE USING MACHINE LEARNING FOR USER NAVIGATION OF MULTIPLE WEB PAGES

NºPublicación: US2022284319A1 08/09/2022

Solicitante:

IBM [US]

Resumen de: US2022284319A1

Facilitating web page selection by a user navigating multiple web pages on a computer can include determining, using a computer, a probability of each of a plurality of web pages being visited by a user. The probability can be determined using machine learning and user operational behavior analysis based on an analysis of web page features. Using a cognitive analysis, the open web pages can be ordered based on the probability of each of the web pages being visited by the user. The ordering can include ranking web pages based on a highest probability of a web page being visited. The open web pages can be managed based on the ordering of the open web pages where the managing includes prompting the user to visit a web page of the open web pages in response to the ranking of an open web page in the order.

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VISUAL-SEMANTIC REPRESENTATION LEARNING VIA MULTI-MODAL CONTRASTIVE TRAINING

NºPublicación: US2022284321A1 08/09/2022

Solicitante:

ADOBE INC [US]

Resumen de: US2022284321A1

Systems and methods for multi-modal representation learning are described. One or more embodiments provide a visual representation learning system trained using machine learning techniques. For example, some embodiments of the visual representation learning system are trained using cross-modal training tasks including a combination of intra-modal and inter-modal similarity preservation objectives. In some examples, the training tasks are based on contrastive learning techniques.

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TIME-FACTORED PERFORMANCE PREDICTION

NºPublicación: US2022284350A1 08/09/2022

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

US_2019378048_PA

Resumen de: US2022284350A1

Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.

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SOFTWARE PROCESS MODIFICATION PLATFORM FOR COMPLIANCE

Nº publicación: US2022284323A1 08/09/2022

Solicitante:

PAYPAL INC [US]

WO_2022183490_PA

Resumen de: US2022284323A1

Methods and systems are presented for providing a computer platform that manages the impacts of government regulations on existing software processes of an online service provider. A regulation document is obtained from a government agency. The regulation document is processed, and legal obligations relevant to an online service provider are extracted from the regulation document. An ensemble machine learning model is used to recommend, for each of the legal obligations, software controls that can be implemented within one or more software processes of the online service provider to mitigate a risk of the legal obligations. The ensemble machine learning model may include an attribute-based model and a text-based model. An explainable visual interface is provided to present the recommended software controls and context that indicates to a user how the software controls are determined for the legal obligations.

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