MACHINE LEARNING

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



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METHODS, SYSTEMS, AND FRAMEWORKS FOR DATA ANALYTICS USING MACHINE LEARNING

Publication No.: US2022292405A1 15/09/2022

Applicant:

BIOSYMETRICS INC [US]

US_2021035017_A1

Absstract of: US2022292405A1

Some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data, using machine learning, for input to a predictive model. The method may include receiving data from a data source, using at least one machine learning (ML) algorithm from a plurality of ML algorithms to obtain at least one combination of preprocessing steps, and computing an accuracy score for each of the at least one combination based on accuracy of prediction of the predictive model. The method may further include using at least one ML algorithm to optimize the feature selection of the predictive model, combining a plurality of datasets into a single dataset, and using a parallel computing network to provide a framework for executing such predictive model.

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DETECTING RANSOMWARE IN MONITORED DATA

Publication No.: US2022292196A1 15/09/2022

Applicant:

COMMVAULT SYSTEMS INC [US]

US_2022292188_PA

Absstract of: 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

Publication No.: US2022295495A1 15/09/2022

Applicant:

EAGLE TECH LLC [US]

Absstract of: 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

Publication No.: US2022292375A1 15/09/2022

Applicant:

TATA CONSULTANCY SERVICES LTD [IN]

Absstract of: 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

Publication No.: US2022292374A1 15/09/2022

Applicant:

SERVICENOW INC [US]

Absstract of: 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

Publication No.: US2022292373A1 15/09/2022

Applicant:

IBM [US]

Absstract of: 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

Publication No.: US2022292528A1 15/09/2022

Applicant:

NIELSEN CO US LLC [US]

WO_2019140263_A1

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

Publication No.: US2022292529A1 15/09/2022

Applicant:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

Absstract of: 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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SYSTEMS AND METHODS FOR IDENTIFYING DISTRACTED DRIVING EVENTS USING COMMON FEATURES

Publication No.: US2022292605A1 15/09/2022

Applicant:

BLUEOWL LLC [US]

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

Publication No.: EP4057205A1 14/09/2022

Applicant:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

US_2022292386_PA

Absstract of: 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

Publication No.: WO2022187487A1 09/09/2022

Applicant:

STATS LLC [US]

US_2022284311_PA

Absstract of: 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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METHOD AND SYSTEM FOR KEY PREDICTORS AND MACHINE LEARNING FOR CONFIGURING CELL PERFORMANCE

Publication No.: WO2022186867A1 09/09/2022

Applicant:

ENEVATE CORP [US]

US_2022285749_PA

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

Publication No.: WO2022184762A1 09/09/2022

Applicant:

ERICSSON TELEFON AB L M [SE]

Absstract of: 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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SOMATIC VARIANT CALLING FROM AN UNMATCHED BIOLOGICAL SAMPLE

Publication No.: US2022284984A1 08/09/2022

Applicant:

PERSONALIS INC [US]

WO_2021092070_A1

Absstract of: US2022284984A1

Methods for somatic variant calling from an unmatched biological samples is provided. The method can include obtaining nucleic acid sequence data corresponding to a biological sample of a subject. The method can also include aligning the nucleic acid sequence data to a reference genome. The method can also include identifying, based on the aligned nucleic acid sequence data, a set of candidate variants in said nucleic acid sequence data. The set of candidate variants may include one or more somatic variants and one or more germline variants. The method can also include, without using a nucleic acid sequencing data from a matching biological sample of the subject, processing the set of candidate variants using a trained machine-learning model to identify the somatic variants. The method can also include outputting a report that identifies the somatic variants.

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AUTOMATED EXTRACTION, INFERENCE AND NORMALIZATION OF STRUCTURED ATTRIBUTES FOR PRODUCT DATA

Publication No.: US2022284392A1 08/09/2022

Applicant:

HEARST MAGAZINE MEDIA INC [US]

Absstract of: US2022284392A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for automated extraction, inference and normalization of structured attributes for a Product Category Normalizer to access product records from external data sources. The Product Category Normalizer defines a product-description taxonomy of product categories represented by a classification tree. The Product Category Normalizer employs product attribute data and machine learning techniques to analyze the product data of an input product record. Based on the product data of the input product record, the Product Category Normalizer extracts and infers appropriate product data for a relevant product category in the classification tree for the item described by the input product record. The Product Category Normalizer normalizes the product data of the input product record. The Product Category Normalizer provides an output normalized product record related to a product category and product attributes of the classification tree.

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SOFTWARE PROCESS MODIFICATION PLATFORM FOR COMPLIANCE

Publication No.: US2022284323A1 08/09/2022

Applicant:

PAYPAL INC [US]

WO_2022183490_PA

Absstract of: 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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TIME-FACTORED PERFORMANCE PREDICTION

Publication No.: US2022284350A1 08/09/2022

Applicant:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

US_2019378048_PA

Absstract of: 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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VISUAL-SEMANTIC REPRESENTATION LEARNING VIA MULTI-MODAL CONTRASTIVE TRAINING

Publication No.: US2022284321A1 08/09/2022

Applicant:

ADOBE INC [US]

Absstract of: 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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EXTRACTING AND SURFACING TOPIC DESCRIPTIONS FROM REGIONALLY SEPARATED DATA STORES

Publication No.: US2022284052A1 08/09/2022

Applicant:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

Absstract of: 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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DATA PRUNING IN TREE-BASED FITTED Q ITERATION

Publication No.: US2022284306A1 08/09/2022

Applicant:

IBM [US]

Absstract of: US2022284306A1

A computer-implemented method is provided for data reduction in a memory device for machine learning. The method includes storing, in the memory device, data that has been used for training in a tree-based fitted Q iteration session which learns an action value function with an ensemble of decision trees from the data. The method further includes determining, by a processor device, samples to be removed from the data based on a number of samples which belong to leaf nodes of the decision trees. The method also includes removing, from the memory device, the determined samples from the data to reduce an amount of the data. The method additionally includes learning, by the processor device, a new ensemble of decision trees using the data from which the determined samples have been removed together with new data.

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

Publication No.: US2022285938A1 08/09/2022

Applicant:

AIR PROD & CHEM [US]

AU_2022201356_A1

Absstract of: US2022285938A1

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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Systems and methods for determining entity attribute representations

Publication No.: AU2021255654A1 08/09/2022

Applicant:

XERO LTD

WO_2021210992_PA

Absstract of: 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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Method And System For Key Predictors And Machine Learning For Configuring Cell Performance

Publication No.: US2022285749A1 08/09/2022

Applicant:

ENEVATE CORP [US]

WO_2022186867_PA

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

Publication No.: US2022283695A1 08/09/2022

Applicant:

EBAY INC [US]

US_2021232291_A1

Absstract of: 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

Nº publicación: US2022283575A1 08/09/2022

Applicant:

AIR PROD & CHEM [US]

AU_2022201354_A1

Absstract of: 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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