MACHINE LEARNING

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Resultados 77 resultados LastUpdate Última actualización 25/07/2021 [00:51:00] pdf PDF xls XLS

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



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INTELLIGENT TRANSACTION OPTIMIZATION ASSISTANT

NºPublicación: US2021224834A1 22/07/2021

Solicitante:

IBM [US]

Resumen de: US2021224834A1

Embodiments for using an intelligent transaction optimization assistant by a processor. One or more actions to enhance a transaction experience of one or more users may be provided according to one or more selected constraints learned via a machine learning operation from previous transaction experiences, user behavior relating to the one or more previous transaction experiences, transaction experiences shared amongst entities associated with a social network, or a combination thereof.

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ENDPOINT DETECTION IN MANUFACTURING PROCESS BY NEAR INFRARED SPECTROSCOPY AND MACHINE LEARNING TECHNIQUES

NºPublicación: US2021224672A1 22/07/2021

Solicitante:

VIAVI SOLUTIONS INC [US]

TW_201843600_A

Resumen de: US2021224672A1

A device may receive training spectral data associated with a manufacturing process that transitions from an unsteady state to a steady state. The device may generate, based on the training spectral data, a plurality of iterations of a support vector machine (SVM) classification model. The device may determine, based on the plurality of iterations of the SVM classification model, a plurality of predicted transition times associated with the manufacturing process. A predicted transition time, of the plurality of predicted transition times, may identify a time, during the manufacturing process, that a corresponding iteration of the SVM classification model predicts that the manufacturing process transitioned from the unsteady state to the steady state. The device may generate, based on the plurality of predicted transition times, a final SVM classification model associated with determining whether the manufacturing process has reached the steady state.

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SYSTEMS AND METHODS FOR DISTRIBUTED INCIDENT CLASSIFICATION AND ROUTING

NºPublicación: US2021224676A1 22/07/2021

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

Resumen de: US2021224676A1

Aspects of the present disclosure relate to incident routing in a cloud environment. In an example, cloud provider teams utilize a scout framework to build a team-specific scout based on that team's expertise. In examples, an incident is detected and a description is sent to each team-specific scout. Each team-specific scout uses the incident description and the scout specifications provided by the team to identify, access, and process monitoring data from cloud components relevant to the incident. Each team-specific scout utilizes one or more machine learning models to evaluate the monitoring data and generate an incident-classification prediction about whether the team is responsible for resolving the incident. In examples, a scout master receives predictions from each of the team-specific scouts and compares the predictions to determine to which team an incident should be routed.

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Automated Account Opening Decisioning Using Machine Learning

NºPublicación: US2021224663A1 22/07/2021

Solicitante:

BOTTOMLINE TECH INC [US]

US_11003999_B1

Resumen de: US2021224663A1

A method for using machine learning techniques to analyze past decisions made by administrators concerning account opening requests and to recommend whether an account opening request should be allowed or denied. Further, the machine learning techniques determine various other products that the customer may be interested in and prioritizes the choices of options that the machine learning algorithm determines appropriate for the customer.

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MACHINE LEARNING BASED RECOMMENDATION OF VEHICLE

NºPublicación: US2021224666A1 22/07/2021

Solicitante:

HONDA MOTOR CO LTD [JP]

Resumen de: US2021224666A1

A vehicle recommendation device and a method for machine learning based recommendation of vehicle is provided. The vehicle recommendation device receives vehicle log data from a plurality of sensors associated with each of a first set of vehicles. The first set of vehicles are classified based on a set of vehicle types. The vehicle recommendation device generates vehicle trip data, associated with a plurality of trips of each of the first set of vehicles, based on the received vehicle log data. The vehicle recommendation device further determines a set of features, associated with the plurality of trips or with information about the first set of vehicles, based on the generated vehicle trip data. The vehicle recommendation device further generates a machine learning model which is trained based on the determined set of features to output a first type of vehicle from the set of vehicle types.

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PROVIDING OUTCOME EXPLANATION FOR ALGORITHMIC DECISIONS

NºPublicación: US2021226939A1 22/07/2021

Solicitante:

JUMIO CORP [US]

WO_2020171974_A1

Resumen de: US2021226939A1

Computer systems and methods are provided for training a machine learning system to determine an authentication decision and explanation information corresponding to the authentication decision. First authentication information for a first authentication request including a first image is received. First validation information corresponding to the first image and including a first authentication decision and first explanation information is received. Data storage of a machine learning system stores the first image and the first validation information. The machine learning system updates an authentication model based on the stored first image and first validation information. Second authentication information for a second authentication request is received. The machine learning system determines second validation information, including second explanation information, based on the updated authentication model. The second explanation information is provided for display to a user device.

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PREDICTIVE DIAGNOSTICS SYSTEM WITH FAULT DETECTOR FOR PREVENTATIVE MAINTENANCE OF CONNECTED EQUIPMENT

NºPublicación: US2021223769A1 22/07/2021

Solicitante:

JOHNSON CONTROLS TECH CO [US]

US_2018373234_PA

Resumen de: US2021223769A1

A building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system configured to receive the monitored variables from the connected equipment; generate a probability distribution of the plurality of monitored variables; determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, each sub-pattern corresponding with a fault, and each fault associated with a fault diagnosis; receive a current set of the monitored variables from the connected equipment; determine whether the current set of monitored variables correspond with one of the sub-patterns of the fault prediction model to facilitate predicting whether a corresponding fault will occur; and determining the fault diagnosis associated with the predicted fault.

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MACHINE LEARNING MODELING USING SOCIAL GRAPH SIGNALS

NºPublicación: US2021224661A1 22/07/2021

Solicitante:

SNAP INC [US]

US_11003997_B1

Resumen de: US2021224661A1

Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

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COMPUTER-IMPLEMENTED SYSTEMS CONFIGURED FOR AUTOMATED ELECTRONIC CALENDAR ITEM PREDICTIONS AND METHODS OF USE THEREOF

NºPublicación: US2021226808A1 22/07/2021

Solicitante:

CAPITAL ONE SERVICES LLC [US]

US_10735212_B1

Resumen de: US2021226808A1

In order to facilitate electronic meeting scheduling and coordination, systems and methods are disclosed including receiving, by a processor, a plurality of electronic meeting requests to schedule a meeting. The processor determines, for each electronic meeting request, meeting room needs. A meeting scheduling machine learning model is utilized to predict parameters of meeting room objects representing the candidate meeting rooms based at least in part on the meeting room needs, schedule information associated with a respective electronic meeting request and location information associated with the respective electronic meeting request. The processor causes an indication of the candidate meeting rooms to display in response to the electronic meeting request on a screen of computing devices associated with the respective attendees based at least in part on the predicted parameters. The processor receives a selection of the respective candidate meeting rooms from the respective attendees, and dynamically secures each candidate meeting room.

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RESOURCE-AWARE AND ADAPTIVE ROBUSTNESS AGAINST CONCEPT DRIFT IN MACHINE LEARNING MODELS FOR STREAMING SYSTEMS

NºPublicación: US2021224696A1 22/07/2021

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

Resumen de: US2021224696A1

Complex computer system architectures are described for detecting a concept drift of a machine learning model in a production environment, for adaptive optimization of the concept drift detection, for extracting embedded features associated with the concept drift using a shadow learner, and for adaptive adjustment of the machine learning model in production to mitigate the effect of predictive performance drop due to the concept drift.

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System and Methods For Detecting Vehicle Braking Events Using Data From Fused Sensors in Mobile Devices

NºPublicación: US2021221350A1 22/07/2021

Solicitante:

ARITY INTERNATIONAL LTD [IE]

US_2021056465_A1

Resumen de: US2021221350A1

One or more braking event detection computing devices and methods are disclosed herein based on fused sensor data collected during a window of time from various sensors of a mobile device found within an interior of a vehicle. The various sensors of the mobile device may include a GPS receiver, an accelerometer, a gyroscope, a microphone, a camera, and a magnetometer. Data from vehicle sensors and other external systems may also be used. The braking event detection computing devices may adjust the polling frequency of the GPS receiver of the mobile device to capture non-consecutive data points based on the speed of the vehicle, the battery status of the mobile device, traffic-related information, and weather-related information. The braking event detection computing devices may use classification machine learning algorithms on the fused sensor data to determine whether or not to classify a window of time as a braking event.

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MACHINE LEARNING-BASED TEXT RECOGNITION SYSTEM WITH FINE-TUNING MODEL

NºPublicación: US2021224695A1 22/07/2021

Solicitante:

HYPER LABS INC [US]

Resumen de: US2021224695A1

A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.

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PREDICTIVE DIAGNOSTICS SYSTEM WITH FAULT DETECTOR FOR PREVENTATIVE MAINTENANCE OF CONNECTED EQUIPMENT

NºPublicación: US2021223768A1 22/07/2021

Solicitante:

JOHNSON CONTROLS TECH CO [US]

US_2018373234_PA

Resumen de: US2021223768A1

A building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system configured to receive the monitored variables from the connected equipment; generate a probability distribution of the plurality of monitored variables; determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, each sub-pattern corresponding with a fault, and each fault associated with a fault diagnosis; receive a current set of the monitored variables from the connected equipment; determine whether the current set of monitored variables correspond with one of the sub-patterns of the fault prediction model to facilitate predicting whether a corresponding fault will occur; and determining the fault diagnosis associated with the predicted fault.

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Method, System, and Computer Program Product for Dynamically Scheduling Machine Learning Inference Jobs with Different Quality of Services on a Shared Infrastructure

NºPublicación: US2021224665A1 22/07/2021

Solicitante:

VISA INT SERVICE ASS [US]

Resumen de: US2021224665A1

A method, system, and computer program product for dynamically scheduling machine learning inference jobs receive or determine a plurality of performance profiles associated with a plurality of system resources, wherein each performance profile is associated with a machine learning model; receive a request for system resources for an inference job associated with the machine learning model; determine a system resource of the plurality of system resources for processing the inference job associated with the machine learning model based on the plurality of performance profiles and a quality of service requirement associated with the inference job; assign the system resource to the inference job for processing the inference job; receive result data associated with processing of the inference job with the system resource; and update based on the result data, a performance profile of the plurality of the performance profiles associated with the system resource and the machine learning model.

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PLATFORM FOR DOCUMENT CLASSIFICATION

NºPublicación: US2021216763A1 15/07/2021

Solicitante:

CAPITAL ONE SERVICES LLC [US]

US_2020302165_A1

Resumen de: US2021216763A1

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

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LOCATING UNDERGROUND FEATURES WITH SEISMIC DATA PROCESSING

NºPublicación: US2021215843A1 15/07/2021

Solicitante:

ENERGY AND ENVIRONMENTAL RES CENTER FOUNDATION [US]

Resumen de: US2021215843A1

Methods are presented for determining the location of underground features (e.g., CO2). One method includes capturing, by sensors distributed throughout a region, seismic traces associated with seismic signals generated by a seismic source. For multiple sensors, active noise is identified or passive noise is measured within each seismic trace and values for attributes associated with the active or passive noise are determined. Further, an unsupervised machine-learning model, based on the values of the attributes, is utilized to determine noise characteristics for multiple sensors. The sensors are grouped in clusters based on the noise characteristics for each sensor. For multiple clusters, a noise filter is created based on the noise characteristics of the sensors in the cluster, and the noise filter of the cluster is applied, for multiple sensors, to the seismic traces of the sensor. Additionally, the filtered seismic traces are analyzed to determine a location of CO2 underground.

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SYSTEMS AND METHODS ASSOCIATED WITH MULTI DATA TYPE MULTI DATA SET ARTIFICIAL INTELLIGENCE PACKAGES, MACHINE LEARNING PACKAGES AND MATHEMATICAL SYSTEMS

NºPublicación: US2021216911A1 15/07/2021

Solicitante:

NATAROS MARK [US]
FLOYD CHARLES GAIUS [US]
APPLIED ARTIFICIAL SCIENCES LLC [US]

Resumen de: US2021216911A1

Utilizing and creating custom multi discipline Artificial Intelligence by persons with minimum Artificial Intelligence knowledge while minimizing multi field expertise required and limiting data and component access requirements.

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PREDICTIVE MACHINE LEARNING FOR PREDICTING A RESONANCE FREQUENCY OF A CATALYST FOR THE SELECTIVE CATALYTIC REDUCTION OF NITROGEN OXIDES

NºPublicación: US2021215077A1 15/07/2021

Solicitante:

VITESCO TECH GMBH [DE]

CN_112639256_A

Resumen de: US2021215077A1

The subject matter of the present invention relates to trained machine-learning models (300), methods (200, 400) and apparatuses (500) allowing a future resonant frequency of a catalyst for selective reduction of nitrogen oxides (SCR) to be predicted, the resonant frequency being representative of a concentration of a reducing agent within the SCR. The SCR forms part of a system for after-treatment of a flow of exhaust gases of an internal combustion engine with which a motor vehicle is provided. The general principle of the invention is based on the observation of correlations between the resonant frequency of an SCR and the concentration of ammonia present within the SCR. This observation led the inventor to envision using machine learning to create a trained machine-learning model in order to predict the resonant frequency of an SCR. In the invention, the trained machine-learning model is a so-called predictive model in which significant correlations are discovered in a set of past observations and in which it is sought to generalize these correlations to cases that have not yet been observed.

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Intelligent Service Test Engine

NºPublicación: US2021216903A1 15/07/2021

Solicitante:

BANK OF AMERICA [US]

Resumen de: US2021216903A1

An illustrative computing system for an intelligent web service verification and validation system processes base input to identify a web service for testing. The intelligent web service verification and validation system processes user defined functional inputs, expected outputs, and assertions with a machine learning engine to provide functional inputs, expected outputs, and assertions based on provided input. The intelligent web service verification and validation system generates a test case pattern, such as a minimally sized test case pattern for regression testing. The intelligent web service verification and validation system executes testing of the service based on the test case pattern and logs test data in a data repository. The intelligent web service verification and validation system analyzes validation and test information, including inputs, outputs, and assertions, using a machine learning algorithm to improve future input and test case generation and testing procedures for the web service.

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Corrosion Rate Estimating Device and Method

NºPublicación: US2021215594A1 15/07/2021

Solicitante:

NIPPON TELEGRAPH & TELEPHONE [JP]

JP_2019203807_A

Resumen de: US2021215594A1

A corrosion rate estimation apparatus includes a corrosion rate measurement unit adapted to repeat water supply cycles in which water is supplied to soil in which metal to be evaluated is buried and measure a cycle number of each water supply cycle, one or more time points in each cycle, and a corrosion rate of the metal at the time point(s); a learning unit adapted to find a prediction model by accepting the cycle number, the time point(s) in the cycle, and the corrosion rate(s) as input and using a machine learning algorithm, the prediction model representing a future corrosion rate; and a corrosion rate estimation unit adapted to assign a cycle number and a time point in the cycle at which a corrosion rate is desired to be estimated to the prediction model and estimate a corrosion rate of the metal at the time point.

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METHODS AND SYSTEMS FOR PREDICTING USER-SPECIFIC DURABILITY OF A SHAVING DEVICE

NºPublicación: US2021216891A1 15/07/2021

Solicitante:

BIC VIOLEX SA [GR]

Resumen de: US2021216891A1

A computer-implemented method of analyzing shaving may include receiving contextual data associated with one or more users from one or more data sources; training a machine learning model using the received contextual data; receiving user data from a user; determining a durability cluster of the user based on the received user data and the trained machine learning model; and performing a shaving improvement action based on the determined durability cluster.

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SYSTEM AND METHOD FOR FAULT DETECTION OF COMPONENTS USING INFORMATION FUSION TECHNIQUE

NºPublicación: US2021216883A1 15/07/2021

Solicitante:

UTOPUS INSIGHTS INC [US]

WO_2020140022_A1

Resumen de: US2021216883A1

An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.

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Predicting Worksite Activities of Standard Machines Using Intelligent Machine Data

NºPublicación: US2021216889A1 15/07/2021

Solicitante:

CATERPILLAR INC [US]

WO_2021141735_A1

Resumen de: US2021216889A1

A set of machines can be deployed on a construction site or other worksite. The set of machines can include an intelligent machine and one or more standard machines. The intelligent machine can report location and activity data to an off-board computing system, while the standard machines can report location data to the off-board computing system. The off-board computing system can train a machine learning model based on the location and activity data from the intelligent machine, such that that the machine learning model can use location data about the standard machines to predict activities performed on the worksite by the standard machines.

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Predicting Rates of Hypoglycemia by a Machine Learning System

NºPublicación: US2021216894A1 15/07/2021

Solicitante:

SANOFI SA [FR]

CN_112567473_A

Resumen de: US2021216894A1

Systems, methods, and computer products can predict rates of hypoglycemia in patients. One of the methods includes receiving data representing medical records of a patient, the patient having been diagnosed with diabetes mellitus. The method includes determine an predicted rate of hypoglycemic events using a machine learning system, the machine being trained using data representing the medical records of a plurality of patients and the corresponding rate of hypoglycemic events for the respective patients. The methods also includes producing the predicted rate for the patient.

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ENHANCED VALIDITY MODELING USING MACHINE-LEARNING TECHNIQUES

Nº publicación: US2021216893A1 15/07/2021

Solicitante:

LIVE NATION ENTERTAINMENT INC [US]

Resumen de: US2021216893A1

The present disclosure generally relates to a primary load management system configured to execute machine learning and artificial intelligence techniques to generate predictions of access-right requests that are or are likely to be invalid before the access-right requests are processed for assignment to users or user devices. More particularly, the present disclosure relates to systems and methods that collect a data set representing characteristics of user devices as the user devices interact with various systems of the primary load management system, train a machine-learning model to predict invalid access-right requests using the collected data set, and execute the trained machine-learning model to process new access-right requests to generate predictions as to whether or not the new access-right requests are invalid.

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