MACHINE LEARNING

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Resultados 107 resultados LastUpdate Última actualización 28/11/2022 [18:36:00] pdf PDF xls XLS

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



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MACHINE LEARNING HYPERPARAMETER TUNING

NºPublicación: WO2022246378A1 24/11/2022

Solicitante:

GOOGLE LLC [US]

US_2022366318_PA

Resumen de: WO2022246378A1

A method (400), when executed by data processing hardware (144), causes the data processing hardware (144) to perform operations including receiving, from a user device (10), a hyperparameter optimization request (20) requesting optimization of one or more hyperparameters (22) of a machine learning model (210). The operations include obtaining training data (152) for training the machine learning model (210) and determining a set of hyperparameter permutations (232) of the hyperparameters. For each respective hyperparameter permutation in the set of hyperparameter permutations, the operations include training a unique machine learning model using the training data and the respective hyperparameter permutation and determining a performance (182) of the trained model. The operations include selecting, based on the performance of each of the trained unique machine learning models of the user device, one of the trained models. The operations include generating one or more predictions using the selected one of the trained models.

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EXTRACT, TRANSFORM, LOAD MONITORING PLATFORM

NºPublicación: US2022374442A1 24/11/2022

Solicitante:

CAPITAL ONE SERVICES LLC [US]

Resumen de: US2022374442A1

In some implementations, a monitoring device may receive configuration information associated with an extract, transform, load (ETL) pipeline that includes one or more data sources and one or more data sinks. The monitoring device may generate, based on the configuration information, lineage data related to a data flow from the one or more data sources to the one or more data sinks in the ETL pipeline. The monitoring device may generate one or more predicted quality metrics associated with the ETL pipeline using a machine learning model. The monitoring device may generate a visualization in which multiple nodes are arranged to indicate the data flow from the one or more data sources to the one or more data sinks and further in which the one or more predicted quality metrics are encoded within the visualization.

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DATA DRIFT MITIGATION IN MACHINE LEARNING FOR LARGE-SCALE SYSTEMS

NºPublicación: WO2022245476A1 24/11/2022

Solicitante:

MICROSOFT TECH LICENSING LLC [US]

US_2022366300_PA

Resumen de: WO2022245476A1

A cloud-based service uses an offline training pipeline to categorize training data for machine learning (ML) models into various clusters. Incoming test data that is received by a data center or in a cloud environment is compared against the categorized training data to identify the appropriate ML model to assign the test data. The comparison of the test data is done in real-time using a similarity metric that takes into account spatial and temporal factors of the test data relative to the categorized training data.

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DISEASE CLASSIFIER AND DYSBIOSIS INDEX TOOLS

NºPublicación: WO2022244005A1 24/11/2022

Solicitante:

TEL HASHOMER MEDICAL RES INFRASTRUCTURE AND SERVICES LTD [IL]

Resumen de: WO2022244005A1

Herein disclosed are a computer-implemented method, a system, and a kit for assessing/classifying the health status associated with a microbiome profile of a gastrointestinal (GI) sample. The method includes receiving data regarding expression levels of amplicon sequence variants (ASVs) of a V4 region of 16S rRNA in a GI sample of a subject, and utilizing a machine learning algorithm that is trained to distinguish a healthy state from a sick state, a score is computed, and a prediction is made for the presence of a general microbial response that is shared by a large variety of diseases.

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AGGREGATED FEATURE IMPORTANCE FOR FINDING INFLUENTIAL BUSINESS METRICS

NºPublicación: US2022374743A1 24/11/2022

Solicitante:

CAPITAL ONE SERVICES LLC [US]

US_2021287111_A1

Resumen de: US2022374743A1

Systems, methods, and apparatuses for determining feature importance of analytics data in predicting a response value include receiving data records, each data record including a response value and values of features associated with the response value; splitting the data records into datasets, each dataset including a part of the data records; generating a machine learning model using each of the datasets, the machine learning model outputting one or more predicting features having influence in predicting the response value for each of the datasets; determining an important feature based on the one or more predicting features; and generating report data indicating that a business metric associated with the important feature has the highest predicted influence among the features on predicting the response value.

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TEMPORAL EXPLANATIONS OF MACHINE LEARNING MODEL OUTCOMES

NºPublicación: US2022374744A1 24/11/2022

Solicitante:

FAIR ISAAC CORP [US]

US_2021004703_A1

Resumen de: US2022374744A1

In transactional systems where past transactions can have impact on the current score of a machine learning based decision model, the transactions that are most responsible for the score and the associated reasons are determined by the transactional system. A system and method identifies such past transactions that maximally impact the current score and allow for a more effective understanding of the scores generated by a model in a transactional system and explanation of specific transactions for automated decisioning, to explain the scores in terms of past transactions. Further an existing instance-based explanation system is used to identify the reasons for the score, and how the identified transactions influence these reasons. A combination of impact on score and impact on reasons determines the most impactful past transaction with respect to the most recent score being explained.

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Systems and Methods for Facial Recognition Training Dataset Adaptation with Limited User Feedback in Surveillance Systems

NºPublicación: US2022374656A1 24/11/2022

Solicitante:

FORTINET INC [US]

Resumen de: US2022374656A1

Various embodiments provide systems and methods for updating a training dataset so that the generated machine learning model can adapt to both short-term and long-term face variations including, for example, head pose, dressing, lighting conditions, and/or aging.

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MACHINE LEARNING PLATFORM FOR OPTIMIZING COMMUNICATION RESOURCES FOR COMMUNICATING WITH USERS

NºPublicación: US2022374736A1 24/11/2022

Solicitante:

HUMANA INC [US]

Resumen de: US2022374736A1

A system according to an embodiment optimizes communications with users using machine learning based models. The system receives user profile data for a set of users. For each user from the set of users, the system provides the user profile data as input to a machine learning based model and determines attributes describing the user, for example a measure of adherence rate for the user. The system ranks the set of users based on the predicted attributes. The system selects a subset of users from the set of users based on the ranking. For each selected user from the set of selected users, the system determines communication parameters for communicating with the selected user and sends a communication to the selected user based on the determined communication parameters.

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METHOD FOR ASSIGNING AT LEAST ONE QUERY TRIPLET TO AT LEAST ONE RESPECTIVE CLASS

NºPublicación: US2022374730A1 24/11/2022

Solicitante:

SIEMENS AG [DE]

WO_2021069505_A1

Resumen de: US2022374730A1

A computer-implemented method and system for assigning at least one query triplet to at least one respective class. The at least one respective class is true or false. The method includes the steps of providing the at least one query triplet and a knowledge graph with a plurality of triples and extracting at least one affirmative argument using reinforcement learning on the basis of the at least one query triplet and the knowledge graph. The at least one affirmative argument indicates that the at least one query triplet is true. The method further includes extracting at least one opposing argument using reinforcement learning on the basis of the at least one query triplet and the knowledge graph. The at least one opposing argument indicates that the at least one query triplet is false. The method further includes assigning the at least one query triplet to the at least one respective class using supervised machine learning depending on the at least two arguments.

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MODEL INTERPRETATION

NºPublicación: US2022374746A1 24/11/2022

Solicitante:

H2O AI INC [US]

US_2019325333_PA

Resumen de: US2022374746A1

Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.

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APPARATUS OF MACHINE LEARNING, MACHINE LEARNING METHOD, AND INFERENCE APPARATUS

NºPublicación: US2022374768A1 24/11/2022

Solicitante:

CANON MEDICAL SYSTEMS CORP [JP]

Resumen de: US2022374768A1

An apparatus of machine learning includes processing circuitry. The processing circuitry uses a first calibration model that receives, as input, first processing data and a first processing label assigned by a user to the first processing data and outputs calibration data relating to calibration of individual characteristics in label assignment by the first user, and trains a target model based on at least the first processing data and the calibration data or a calibrated label having individual characteristics calibrated using the calibration data.

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MASK CORRECTION METHOD, MASK CORRECTION DEVICE FOR DOUBLE PATTERNING AND TRAINING METHOD FOR LAYOUT MACHINE LEARNING MODEL

NºPublicación: US2022373877A1 24/11/2022

Solicitante:

UNITED MICROELECTRONICS CORP [TW]

CN_115373227_PA

Resumen de: US2022373877A1

A mask correction method, a mask correction device for double patterning, and a training method for a layout machine learning model are provided. The mask correction method for double patterning includes the following steps. A target layout is obtained. The target layout is decomposed into two sub-layouts, which overlap at a stitch region. A size of the stitch region is analyzed by the layout machine learning model according to the target layout. The layout machine learning model is established according to a three-dimensional information after etching. An optical proximity correction (OPC) procedure is performed on the sub-layouts.

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SYSTEMS AND METHODS FOR CONTENT MANAGEMENT OF LIVE OR STREAMING BROADCASTS AND VIDEO PUBLISHING SYSTEMS

NºPublicación: US2022377392A1 24/11/2022

Solicitante:

HUANG ERNEST [US]

US_2020389683_A1

Resumen de: US2022377392A1

Example implementations described herein are directed to systems, methods, and computer programs for the management of broadcasting image data for policy violations, which can include retrieving image data from memory for transmission to a device; processing the retrieved image data with a machine learning algorithm configured to detect a policy violation; for the processing indicative of the policy violation existing in the retrieved image data: modifying the retrieved image data; and transmitting the retrieved image data to the device.

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SAFETY NET ENGINE FOR MACHINE LEARNING-BASED NETWORK AUTOMATION

NºPublicación: US2022377005A1 24/11/2022

Solicitante:

CISCO TECH INC [US]

Resumen de: US2022377005A1

In one embodiment, a device obtains data regarding routing decisions made by a machine learning-based predictive routing engine for a network. The device determines, based on the data regarding the routing decisions, a behavior of the machine learning-based predictive routing engine. The device compares the behavior of the machine learning-based predictive routing engine to a behavioral policy for the machine learning-based predictive routing engine. The device adjusts operation of the machine learning-based predictive routing engine, when the behavior of the machine learning-based predictive routing engine violates the behavioral policy.

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MACHINE LEARNING TECHNIQUES FOR AUTOMATIC EVALUATION OF CLINICAL TRIAL DATA

NºPublicación: US2022375560A1 24/11/2022

Solicitante:

IQVIA INC [US]

US_2020410614_A1

Resumen de: US2022375560A1

Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.

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MULTI-PHASE PRIVACY-PRESERVING INFERENCING IN A HIGH VOLUME DATA ENVIRONMENT

NºPublicación: US2022374904A1 24/11/2022

Solicitante:

INT BUSINESS MACHINES CORPORATION [US]

Resumen de: US2022374904A1

A method, apparatus and computer program product that provides multi-phase privacy-preserving inferencing in a high throughput data environment, e.g., to facilitate fraud prediction, detection and prevention. In one embodiment, two (2) machine learning models are used, a first model that is trained in the clear on first transaction data, and a second model that is trained in the clear but on the first transaction data, and user data. The first model is used to perform inferencing in the clear on the high throughput received data. In this manner, the first model provides a first level evaluation of whether a particular transaction might be fraudulent. If a transaction is flagged in this first phase, a second more secure inference is then carried out using the second model. The inferencing performed by the second model is done on homomorphically encrypted data. Thus, only those transactions marked by the first model are passed to the second model for secure evaluation.

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AUTOMATIC DETECTION OF CLOUD-SECURITY FEATURES (ADCSF) PROVIDED BY SAAS APPLICATIONS

NºPublicación: US2022377098A1 24/11/2022

Solicitante:

NETSKOPE INC [US]

Resumen de: US2022377098A1

A method for scoring a cloud SaaS application to rate the level of cloud security provided by that application. The application URLs are crawled iteratively for data corresponding to a set of predetermined features using keyword strings. The features are determined to be those which are indicative of effective cloud security. The crawled data corresponding to features are stored in text files. The data are used for training and supervised machine learning algorithm to determine the probability score that a feature is present for that application. The feature scores are numerically combined to arrive at an overall cloud confidence index score (CCI) for that application. Every SaaS application is rated with a score between 1 and 100, depending on whether the selected features are present or not. The CCI score provides an easy way to determine the level of cloud security provided the application. It also provides a way to compare different SaaS applications as to their effectiveness in providing cloud security.

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SYSTEMS AND METHODS FOR MACHINE LEARNING ENHANCED INTELLIGENT BUILDING ACCESS ENDPOINT SECURITY MONITORING AND MANAGEMENT

NºPublicación: US2022375226A1 24/11/2022

Solicitante:

AMBIENT AI INC [US]

US_2020202136_PA

Resumen de: US2022375226A1

Systems and methods for correlating access-system primitives generated by an access control system and semantic primitives generated by a sensor data comprehension system.

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Inventory Item Prediction and Listing Recommendation

NºPublicación: US2022374805A1 24/11/2022

Solicitante:

EBAY INC [US]

CN_115375219_PA

Resumen de: US2022374805A1

An inventory prediction system is described that outputs a predicted inventory item not included in a user's known inventory using a cross-category directional graph that represents item categories as nodes. The inventory prediction system implements a prediction model trained using machine learning to output the predicted inventory item using the graph and at least one item from the user's known inventory. The inventory prediction system is further configured to generate a listing recommendation for the predicted inventory item. To do so, the inventory prediction system implements a logistic regression model trained using machine learning to calculate a probability that the listing recommendation should be generated using attributes of the predicted inventory item and attributes of currently trending items. The listing recommendation is generated to include a description of, and estimated value for, the predicted inventory item, together with an option to generate a sale listing for the predicted inventory item.

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SYSTEM AND METHOD FOR OPERATING AN EVENT-DRIVEN ARCHITECTURE

NºPublicación: US2022374301A1 24/11/2022

Solicitante:

SERVICENOW CANADA INC [CA]

WO_2021084509_A1

Resumen de: US2022374301A1

There is disclosed a method and system for operating an event-driven architecture. The event-driven architecture comprises a first machine-learning (ML) agent operating a first service and a second ML agent operating a second service. The first ML agent comprises a first model and first model metadata. The second ML agent comprises a second model and second model metadata. The method comprises generating, by the first ML agent, an event associated with event metadata. The event comprises results generated by the first model. The event metadata comprises an event identifier (ID). The first ML agent publishes the event in a virtualized dedicated space. The second ML agent determines whether the event is to be processed by the second ML agent. If a determination is made that the message is to be processed by the second ML agent, the second ML agent processes the event to generate an output.

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METHOD AND SYSTEM FOR EXPLAINABLE MACHINE LEARNING USING DATA AND PROXY MODEL BASED HYBRID APPROACH

NºPublicación: US2022374769A1 24/11/2022

Solicitante:

TATA CONSULTANCY SERVICES LTD [IN]

EP_4092586_PA

Resumen de: US2022374769A1

Conventionally three main approaches are utilized for explainability of blackbox ML systems: proxy or shadow model approaches, model inspection approaches and data based approaches. Most of the research work on explainability has followed one of the above approaches with each having its own limitations and advantages. Embodiments of the present disclosure provide a method and system for explainable Machine learning (ML) using data and proxy model based hybrid approach to explain outcomes of a ML model. The hybrid approach is based on Local Interpretable Model-agnostic Explanations (LIME) using Formal Concept Analysis (FCA) for structured sampling of instances. The approach combines the benefits of using a data-based approach (FCA) and proxy model-based approach (LIME).

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METHOD AND SYSTEM FOR EXPLAINABLE MACHINE LEARNING USING DATA AND PROXY MODEL BASED HYBRID APPROACH

NºPublicación: EP4092586A1 23/11/2022

Solicitante:

TATA CONSULTANCY SERVICES LTD [IN]

US_2022374769_PA

Resumen de: EP4092586A1

Conventionally three main approaches are utilized for explainability of blackbox ML systems: proxy or shadow model approaches, model inspection approaches and data based approaches. Most of the research work on explainability has followed one of the above approaches with each having its own limitations and advantages. Embodiments of the present disclosure provide a method and system for explainable Machine learning (ML) using data and proxy model based hybrid approach to explain outcomes of a ML model. The hybrid approach is based on Local Interpretable Model-agnostic Explanations (LIME) using Formal Concept Analysis (FCA) for structured sampling of instances. The approach combines the benefits of using a data-based approach (FCA) and proxy model-based approach (LIME).

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AUTOMATED DATA HIERARCHY EXTRACTION AND PREDICTION USING A MACHINE LEARNING MODEL

NºPublicación: WO2022240654A1 17/11/2022

Solicitante:

ORACLE INT CORP [US]

US_2022366298_PA

Resumen de: WO2022240654A1

Techniques are disclosed for revising training data used for training a machine learning model to exclude categories that are associated with an insufficient number of data items in the training data set. The system then merges any data items associated with a removed category into a parent category in a hierarchy of classifications. The revised training data set, which includes the recategorized data items and lacks the removed categories, is then used to train a machine learning model in a way that avoids recognizing the removed categories.

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INCORPORATION OF DECISION TREES IN A NEURAL NETWORK

NºPublicación: WO2022240391A1 17/11/2022

Solicitante:

GOOGLE LLC [US]

Resumen de: WO2022240391A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scheduling operations represented on a computation graph. One of the methods comprises receiving data representing a neural network comprising a plurality of layers arranged in a sequence; selecting one or more groups of layers each comprising one or more layers adjacent to each other in the sequence; generating a new machine learning model, comprising: for each group of layers, a respective decision tree that replaces the group of layers, wherein the respective decision tree receives as input a quantized version of the inputs to a respective first layer in the group and generates as output a quantized version of the outputs of a respective last layer in the group, wherein a tree depth of the respective decision tree is based at least in part on a number of layers of the group.

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ASSESSMENT OF IMAGE QUALITY FOR OPTICAL CHARACTER RECOGNITION USING MACHINE LEARNING

Nº publicación: US2022366179A1 17/11/2022

Solicitante:

ABBYY DEV INC [US]

Resumen de: US2022366179A1

Aspects of the disclosure provide for systems and processes for assessing image quality for optical character recognition (OCR), including but not limited to: segmenting an image into patches, providing the segmented image as an input into a first machine learning model (MLM), obtaining, using the first MLM, for each patch, first feature vectors representative of a reduction of imaging quality in a respective patch, and second feature vectors representative of a text content of the respective patch, providing to a second MLM the first feature vectors and the second feature vectors, and obtaining, using the second MLM, an indication of suitability of the image for OCR.

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