MACHINE LEARNING

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Resultados 104 resultados LastUpdate Última actualización 20/10/2021 [02:59:00] pdf PDF xls XLS

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



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ALLOCATING RESOURCES FOR IMPLEMENTING A WELL-PLANNING PROCESS

NºPublicación: WO2021207462A1 14/10/2021

Solicitante:

SAUDI ARABIAN OIL CO [SA]
ARAMCO SERVICES CO [US]

US_2021317726_A1

Resumen de: WO2021207462A1

Methods and systems, including computer programs encoded on a computer storage medium can be used for an integrated methodology that can be used by a computing system to automate processes for generating, and updating (e.g., in real-time), subsurface reservoir models. The methodology and automated approaches employ technologies relating to machine learning and artificial intelligence (Al) to process seismic data and information relating to seismic facies. The geological model is used to determine resource requirements for implementing a well-planning process and to generate a field development plan.

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PRINTING RELEVANT CONTENT

NºPublicación: US2021318840A1 14/10/2021

Solicitante:

HEWLETT PACKARD DEVELOPMENT CO [US]

WO_2020106355_A1

Resumen de: US2021318840A1

The present subject matter relates to techniques of printing contents within a webdocument that are relevant for printing. In one example, the web document including a plurality of content may be received and thereafter, each content in the web document may be classified as one of relevant or non-relevant for printing. In one example, the classification may be done by analyzing metadata associated with the contents using machine learning techniques. Further, the contents classified as relevant may be sent for printing.

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LEARNING APPARATUS, COMMUNICATION SYSTEM, AND LEARNING METHOD

NºPublicación: US2021319358A1 14/10/2021

Solicitante:

NEC CORP [JP]

JP_2021166361_A

Resumen de: US2021319358A1

A learning apparatus includes an acquisition unit configured to acquire communication data transmitted to a network, and a learning unit configured to perform machine learning by using the communication data acquired by the acquisition unit as teacher data. The network includes a communication apparatus and a communication apparatus, and the acquisition unit acquires communication data between the communication apparatuses.

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ALLOCATING RESOURCES FOR IMPLEMENTING A WELL-PLANNING PROCESS

NºPublicación: US2021317726A1 14/10/2021

Solicitante:

SAUDI ARABIAN OIL CO [SA]

WO_2021207462_A1

Resumen de: US2021317726A1

Methods and systems, including computer programs encoded on a computer storage medium can be used for an integrated methodology that can be used by a computing system to automate processes for generating, and updating (e.g., in real-time), subsurface reservoir models. The methodology and automated approaches employ technologies relating to machine learning and artificial intelligence (AI) to process seismic data and information relating to seismic facies.

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Machine Learning of Firewall Insights

NºPublicación: US2021320903A1 14/10/2021

Solicitante:

GOOGLE LLC [US]

Resumen de: US2021320903A1

A computer-implemented method causes data processing hardware to perform operations for training a firewall utilization model. The operations include receiving firewall utilization data for firewall connection requests during a utilization period. The firewall utilization data includes hit counts for each sub-rule associated with at least one firewall rule. The operations also include generating training data based on the firewall utilization data. The training data includes unused sub-rules corresponding to sub-rules having no hits during the utilization period and hit sub-rules corresponding to sub-rules having more than zero hits during the utilization period. The operations also include training a firewall utilization model on the training data. The operations further include, for each sub-rule associated with the at least one firewall rule, determining a corresponding sub-rule utilization probability indicating a likelihood the sub-rule will be used for a future connection request.

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Systems and Methods for Dataset Merging using Flow Structures

NºPublicación: US2021318851A1 14/10/2021

Solicitante:

VIRTUALITICS INC [US]

Resumen de: US2021318851A1

Systems and methods for dataset merging using flow structures in accordance with embodiments of the invention are illustrated. Flow structures can be generated and sent to various computing devices to generate both the front-end and back-end of a customized computing system that can perform any number of various processes including those that merge datasets. In many embodiments, machine learning and/or natural language processing can be performed by the customized application.

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VERIFICATION OF STOCHASTIC GRADIENT DESCENT

NºPublicación: US2021319353A1 14/10/2021

Solicitante:

IBM [US]

Resumen de: US2021319353A1

An example operation includes one or more of computing, by a data owner node, updated gradients on a loss function based on a batch of private data and previous parameters of a machine learning model associated with a blockchain, encrypting, by the data owner node, update information, recording, by the data owner, the encrypted update information as a new transaction on the blockchain, and providing the update information for an audit.

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DETERMINING DRIVER AND VEHICLE CHARACTERISTICS BASED ON AN EDGE-COMPUTER DEVICE

NºPublicación: US2021319332A1 14/10/2021

Solicitante:

ALLSTATE INSURANCE CO [US]

Resumen de: US2021319332A1

Methods, computer-readable media, software, and apparatuses may collect, in real-time and via an edge-computing device located in a vehicle, vehicle driving event data including data indicative of driving characteristics associated with an operation of the vehicle. The edge-computing device may analyze, based on a machine learning model, characteristics of the vehicle driving event data. The edge-computing device may, based on the machine learning model, determine at least one of: a driving behavior, a driver rating, occurrence of a collision, and vehicle diagnostics, and the information may be displayed via a graphical user interface to a user in the vehicle.

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PROVIDING INTELLIGENT STORAGE LOCATION SUGGESTIONS

NºPublicación: US2021319345A1 14/10/2021

Solicitante:

DROPBOX INC [US]

US_2018129950_A1

Resumen de: US2021319345A1

One or more embodiments of a content system provide machine-learned storage location recommendations for storing content items. Specifically, an online content management system can train a machine-learning model to identify a storage pattern from previously stored content items in a plurality of storage locations corresponding to a user account of a user. Training the machine-learning model includes training a plurality of classifiers for the plurality of storage locations. The online content management system uses the classifiers to determine whether a content item is similar to the content items in any of the storage locations, and based on the output of the classifiers, provides graphical elements indicating recommended storage locations within a graphical user interface. The user can select a graphical element to move the content item to the corresponding storage location.

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DETERMINING SKILL ADJACENCIES USING A MACHINE LEARNING MODEL

NºPublicación: US2021319334A1 14/10/2021

Solicitante:

IBM [US]

Resumen de: US2021319334A1

A computer-implemented method, a computer program product, and a computer system for determining skill adjacencies using a machine learning model. A computer calculates first similarity scores between first skill vectors obtained from one or more training datasets and second similarity scores between the first skill vectors and skill category vectors pre-calculated from job requisitions, using both a reference corpus word embedding model and a target corpus word embedding model. The computer generates features extracted from the first similarity scores and the second similarity scores. Based on the features, the computer trains a machine learning model for classifying combinations of skills as adjacent and non-adjacent. The machine learning model is used to determine skill adjacencies between skills extracted from the job requisitions and skills extracted from resumes.

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MATERIAL PROPERTY PREDICTION METHOD AND MATERIAL PROPERTY PREDICTION DEVICE

NºPublicación: US2021319336A1 14/10/2021

Solicitante:

HITACHI LTD [JP]

JP_2021165990_A

Resumen de: US2021319336A1

Provided are a material property prediction method and a material property prediction device capable of material search considering the interaction between partial structures by using explanatory variables that can be determined without using measured values. A material property prediction method using machine learning that builds a prediction model of the objective variable from explanatory variables based on a partial structure of a material, the material property prediction method including (a) a step of performing a first-principles calculation based on the partial structure of the material and randomly selected explanatory variables, and (b) a step of performing unsupervised classification machine learning and supervised learning based on the result of the first-principles calculation obtained in the above step (a) to build a prediction model, in which the sum of squares of the values obtained by the first-principles calculation is included in the explanatory variables in the step (b).

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METHODS AND SYSTEM FOR TRAINING AND IMPROVING MACHINE LEARNING MODELS

NºPublicación: US2021319337A1 14/10/2021

Solicitante:

HELIOS SPORTS INC [US]

Resumen de: US2021319337A1

A Sports Detection System including Sports Detection Device having an artificial intelligence (AI) recognition embedded therein and configured to run an Action Detection Model (ADM) that identifies and stores one or more individual sports actions on the Sports Detection Device for later offloading onto a secondary computing device. Methods for training and improving the ADM include tagging time-aligned portions of sensed and video data to be confirmed by profilers where the feedback can be run through a supervised learning algorithm to generate or update an ADM. The process of identifying and tagging identified portions of time-aligned data can be aided by integrating data mining and pattern recognition techniques.

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FAST AND SCALABLE MULTI-TENANT SERVE POOL FOR CHATBOTS

NºPublicación: US2021319347A1 14/10/2021

Solicitante:

ORACLE INT CORP [US]

US_2021319360_A1

Resumen de: US2021319347A1

Techniques are disclosed for providing a scalable multi-tenant serve pool for chatbot systems. A query serving system (QSS) receives a request to serve a query for a skillbot. The QSS includes: (i) a plurality of deployments in a serving pool, and (ii) a plurality of deployments in a free pool. The QSS determines whether a first deployment from the plurality of deployments in the serving pool can serve the query based on an identifier of the skillbot. In response to determining that the first deployment cannot serve the query, the QSS selects a second deployment from the plurality of deployments in the free pool to be assigned to the skillbot, and loads a machine-learning model associated with the skillbot into the second deployment, wherein the machine-learning model is trained to serve the query for the skillbot. The query is served using the machine-learning model loaded into the second deployment.

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MODEL TRAINING, IMAGE PROCESSING METHOD, DEVICE, STORAGE MEDIUM, AND PROGRAM PRODUCT

NºPublicación: US2021319262A1 14/10/2021

Solicitante:

BEIJING BAIDU NETCOM SCI & TECH CO LTD [CN]

EP_3876163_A2

Resumen de: US2021319262A1

The present application provides a model training, image processing method, device, storage medium, and program product relating to deep learning technology, which are able to screen auxiliary image data with image data for learning a target task, and further fuse the target image data and the auxiliary image data, so as to train a built and to-be-trained model with the fusion-processed fused image data. This implementation can increase the amount of data for training the model, and the data for training the model is determined is based on the target image data, which is suitable for learning the target task. Therefore, the solution provided by the present application can train an accurate target model even if the amount of target image data is not sufficient.

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MACHINE LEARNING MODEL CONFIDENCE SCORE VALIDATION

NºPublicación: US2021319340A1 14/10/2021

Solicitante:

DATALOOP LTD [IL]

Resumen de: US2021319340A1

A method comprising: receiving, as input, an image for classification by a trained machine learning model, generate a data set comprising a plurality of transformations of the image; applying, to each of the transformations in the data set, the trained machine learning model, to obtain a classification with respect to the transformation, wherein the classification has an associated confidence score; computing (i) a consensus classification based on all of the obtained classifications with respect to each of the transformations, and (ii) a consensus confidence score corresponding to the consensus classification, based on all of the associated confidence scores; and outputting the consensus classification and the corresponding consensus confidence score, as a classification result with respect to the image.

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SYSTEM AND METHOD FOR TESTING MACHINE LEARNING

NºPublicación: US2021319338A1 14/10/2021

Solicitante:

ROYAL BANK OF CANADA [CA]

Resumen de: US2021319338A1

A machine learning failure discriminator machine is described, along with corresponding systems, methods, and non-transitory computer readable media. The approach operates in relation to an iterative machine learning model and includes a phased approach to extract p-values from the iterative machine learning model based on modified versions of the training or validation data sets. The p-values are then used to identify whether various null hypotheses can be rejected, and accordingly, to generate an output data structure indicative of an estimated failure reason, if any. The output data structure may be made available on an API or on a graphical user interface.

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METHOD AND SERVER FOR TRAINING MACHINE LEARNING ALGORITHM FOR RANKING OBJECTS

NºPublicación: US2021319359A1 14/10/2021

Solicitante:

YANDEX EUROPE AG [CH]

Resumen de: US2021319359A1

Method and server for training a Machine Learning Algorithm (MLA) for ranking objects in response to a query are disclosed. The training includes use of a ranking quality metric function that is one of a flat and a discontinuous function to determine a performance score of the MLA. The method includes generating relevance scores for a set of training objects based on data associated with the set of training objects and a training query, generating noise-induced relevance scores for the set of training objects by combining the relevance scores and noise values, generating the performance score for the MLA based on the noise-induced relevance scores, determining a policy gradient value for adjusting relevance scores to be generated by the MLA for the in-use objects in response to the in-use query, and applying the policy gradient value for training the MLA to rank in-use objects in response to an in-use query.

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Systems and Methods for Machine Learning in Hyperbolic Space

NºPublicación: US2021319339A1 14/10/2021

Solicitante:

GOOGLE LLC [US]

Resumen de: US2021319339A1

Generally, the present disclosure provides systems and methods for performing machine learning in hyperbolic space. Specifically, techniques are provided which enable the learning of a classifier (e.g., large-margin classifier) for data defined within a hyperbolic space (e.g., which may be particularly beneficial for data that possesses a hierarchical structure).

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PROACTIVE DEFENSE OF UNTRUSTWORTHY MACHINE LEARNING SYSTEM

NºPublicación: US2021319099A1 14/10/2021

Solicitante:

VISA INT SERVICE ASS [US]

CN_112789634_A

Resumen de: US2021319099A1

Methods and systems for inducing model shift in a malicious computer's machine learning model is disclosed. A data processor can determine that a malicious computer uses a machine learning model with a boundary function to determine outcomes. The data processor can then generate transition data intended to shift the boundary function and then provide the transition data to the malicious computer. The data processor can repeat generating and providing the transition data, thereby causing the boundary function to shift over time.

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METHOD AND SYSTEM FOR GENERATING ANNOTATED TRAINING DATA

NºPublicación: US2021319363A1 14/10/2021

Solicitante:

YIELD SYSTEMS OY [FI]

WO_2020039121_PA

Resumen de: US2021319363A1

A method of generating an annotated synthetic training data for training a machine learning module for processing an operational data set includes creating a first procedural model for the object, the first procedural model having a first set of parameters relating to the object; creating a second procedural model for the background, the second procedural model having a second set of parameters relating to the background; creating the task environment model pertaining to the machine learning task using the first and the second procedural models; creating a synthetic data set using the task environment model; and allocating at least one parameter of the first set of parameters as an annotation for the simulation data to generate the annotated synthetic training data.

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METHOD, APPARATUS AND DEVICE FOR GENERATING MODEL AND STORAGE MEDIUM

NºPublicación: US2021319366A1 14/10/2021

Solicitante:

BEIJING BAIDU NETCOM SCI & TECH CO LTD [CN]

EP_3893169_A2

Resumen de: US2021319366A1

The present disclosure discloses a method, apparatus and device for generating a model and a storage medium. A method can include: acquiring sample resource features and sample labels; determining a first screening factor according to the sample resource features and the sample labels, and determining first resource features from the sample resource features according to the first screening factor; determining a second screening factor, and determining second resource features from the first resource features based on the second screening factor, and obtaining features of a target model based on the second resource features; and training a machine learning model, by taking the features of the target model as an input of the target model, and taking the sample labels corresponding to the features of the target model as an output of the target model, to obtain the trained target model.

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MULTI-LEVEL CACHING FOR DYNAMIC DEEP LEARNING MODELS

NºPublicación: US2021319369A1 14/10/2021

Solicitante:

INTEL CORP [US]

Resumen de: US2021319369A1

Systems, apparatuses and methods provide technology for model generation with intermediate stage caching and re-use, including generating, via a model pipeline, a multi-level set of intermediate stages for a model, caching each of the set of intermediate stages, and responsive to a change in the model pipeline, regenerating an executable for the model using a first one of the cached intermediate stages to bypass regeneration of at least one of the intermediate stages. The multi-level set of intermediate stages can correspond to a hierarchy of processing stages in the model pipeline, where using the first one of the cached intermediate stages results in bypassing regeneration of a corresponding intermediate stage and of all intermediate stages preceding the corresponding intermediate stage in the hierarchy. Further, regenerating an executable for the model can include regenerating one or more intermediate stages following the corresponding intermediate stage in the hierarchy.

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SYSTEMS AND METHODS FOR AUTOMATED OUTBOUND PROFILE GENERATION

NºPublicación: WO2021205224A1 14/10/2021

Solicitante:

COUPANG CORP [KR]

US_2021312378_A1

Resumen de: WO2021205224A1

One aspect of the present disclosure is directed to a computer-implemented system for generating an automated outbound profile. The system may include may perform steps including: receiving data comprising a capacity of a fulfillment center (FC); receiving, a plurality of product identifiers associated with incoming products to the FC; periodically collecting and storing transactional logs for the products at the FC using the product identifier; determining a current inventory for the products stored at the FC using the product identifier; generating an outbound profile for the FC using at least one of the transactional logs and the current inventory using a machine learning algorithm; wherein the outbound profile comprises an expected percentage of outgoing products for a plurality of categories of products; and managing network outbound using the generated outbound profile of the FC by comparing the outbound profile to actual outbound capacity of the FC.

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USE OF GENETIC ALGORITHMS TO DETERMINE A MODEL TO IDENTITY SAMPLE PROPERTIES BASED ON RAMAN SPECTRA

NºPublicación: WO2021207160A1 14/10/2021

Solicitante:

GENENTECH INC [US]

Resumen de: WO2021207160A1

Techniques are disclosed for using a genetic algorithm to identify a processing pipeline that transforms spectra into a form usable to generate predicted characteristics of corresponding samples. The genetic algorithm is used to generate and evaluate multiple candidate solutions specifying various pre-processing and machine-learning-processing configurations. The processing pipeline is defined based on the candidate solutions.

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METHOD, APPARATUS AND DEVICE FOR GENERATING MODEL AND STORAGE MEDIUM

Nº publicación: EP3893169A2 13/10/2021

Solicitante:

BEIJING BAIDU NETCOM SCI & TECH CO LTD [CN]

US_2021319366_A1

Resumen de: EP3893169A2

The present disclosure discloses a method, apparatus and device for generating a model and a storage medium. A specific implementation comprises: acquiring sample resource features and sample labels; determining a first screening factor according to the sample resource features and the sample labels, and determining first resource features from the sample resource features according to the first screening factor; determining a second screening factor, and determining second resource features from the first resource features based on the second screening factor, and obtaining features of a target model based on the second resource features; and training a machine learning model, by taking the features of the target model as an input of the target model, and taking the sample labels corresponding to the features of the target model as an output of the target model, to train a machine learning model and obtain the trained target model.

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