MACHINE LEARNING

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



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RESERVOIR MODELING AND WELL PLACEMENT USING MACHINE LEARNING

Publication No.: WO2022170358A1 11/08/2022

Applicant:

SCHLUMBERGER TECHNOLOGY CORP [US]
SCHLUMBERGER CA LTD [CA]
SERVICES PETROLIERS SCHLUMBERGER [FR]
GEOQUEST SYSTEMS BV [NL]

Absstract of: WO2022170358A1

A method includes receiving data representing reservoir properties for a subsurface volume, conducting an uncertainty analysis by simulating different model realizations representing the subsurface volume, identifying a first hot spot of the subsurface volume based on the uncertainty analysis, the first hot spot representing an area having a high predicted performance, relative to other areas of the subsurface volume, based on the simulating of the different model realizations representing the subsurface volume, identifying a second hot spot of the subsurface volume using a machine learning model that is trained to predict well performance based on the one or more reservoir properties, evaluating the first and second hot spots for well placement based on the predicted well performance at the first and second hot spots, respectively, and selecting at least one of the first hot spot or the second hot spot for well construction.

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SCHEMA AUGMENTATION SYSTEM FOR EXPLORATORY RESEARCH

Publication No.: US2022253719A1 11/08/2022

Applicant:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

WO_2022169719_A1

Absstract of: US2022253719A1

In examples, a schema augmentation system for exploratory research leverages intelligence from a machine learning model to augment such tasks by leveraging intelligence derived from machine learning capabilities. Augmenting tasks include schematization of content, such as information units and groupings of information units. Based on the schematization of such content, semantic proximities for information units are determined. The semantic proximities may be used to identify and present potentially relevant information units, for example to accelerate the exploratory research task at hand. As such, users engaged in consumption of heterogeneous content (e.g., across client applications and/or content sources), may receive machine-augmented support to find potential information units. To optimize machine training, user input may be received, such that the system may intelligently augment the user's exploratory research task based on the semantic coherence of the content processed from information units and associated user behavior.

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SECURE STORAGE AND PROCESSING OF DATA FOR GENERATING TRAINING DATA

Publication No.: WO2022169584A1 11/08/2022

Applicant:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

US_2022253540_A1

Absstract of: WO2022169584A1

Techniques for securely storing and processing data for training data generation are provided. In one technique, multiple encrypted records are retrieved from a first persistent storage. For each encrypted record, that record is decrypted in memory to generate a decrypted record that comprises multiple attribute values. Then, based on the attribute values and a definition of multiple features of a machine-learned model, multiple feature values are generated and stored, along with a label, in a training instance, which is then stored in a second persistent storage. One or more machine learning techniques are used to train the machine-learned model based on training data that includes the training instances that are stored in the second persistent storage.

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ASSESSING PROJECT QUALITY USING CONFIDENCE ANALYSIS OF PROJECT COMMUNICATIONS

Publication No.: US2022253787A1 11/08/2022

Applicant:

IBM [US]

Absstract of: US2022253787A1

An embodiment trains a machine-learning model using a first training corpus of general items indicative of varying levels of confidence. The embodiment also prepares a second training corpus that includes domain-specific items indicative of varying levels of confidence extracted from communications from members of a project group associated with a project. The embodiment retrains the machine-learning model using the second training corpus and generates a confidence score for the project based on confidence values assigned by the machine-learning model to each of a plurality of project-related communication items from members of the project group. The embodiment also detects that the confidence score is below a predetermined threshold confidence level and, in response, initiates a communication to members of the project group conveying information regarding an automated remedial action for the project.

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DEVICES AND METHODS FOR LATTICE POINTS ENUMERATION

Publication No.: US2022253670A1 11/08/2022

Applicant:

INST MINES TELECOM [FR]

KR_20220027155_PA

Absstract of: US2022253670A1

A lattice prediction device for predicting a number of lattice points falling inside a bounded region in a given vector space is provided. The bounded region is defined by a radius value, a lattice point representing a digital signal in a lattice constructed over the vector space. The lattice is defined by a lattice generator matrix comprising components. The lattice prediction device comprises a computation unit configured to determine a predicted number of lattice points by applying a machine learning algorithm to input data derived from the radius value and the components of lattice generator matrix.

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SYSTEM FOR SECURE OBFUSCATION OF ELECTRONIC DATA WITH DATA FORMAT PRESERVATION

Publication No.: US2022253544A1 11/08/2022

Applicant:

BANK OF AMERICA [US]

Absstract of: US2022253544A1

Embodiments of the invention are directed to systems, methods, and computer program products for utilizing machine learning to identify data which is to be obfuscated in a format-preserving manner, which allows the obfuscated or masked data to appear as though it is original data. Because this type of obfuscation technique may require a higher degree of computational power than other techniques, there is a need to be able to dynamically choose when to implement format preservation based on a variety of factors. By using machine learning techniques, the present invention provides the functional benefit of analyzing both the data to be obfuscated, as well as available computational resources, to determine when it is appropriate to apply a format-preserving masking algorithm to the data. Accordingly, the present invention may ensure that organizational data is appropriately masked while preventing the resource strain associated with preserving the format of all original data.

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SECURE STORAGE AND PROCESSING OF DATA FOR GENERATING TRAINING DATA

Publication No.: US2022253540A1 11/08/2022

Applicant:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

WO_2022169584_A1

Absstract of: US2022253540A1

Techniques for securely storing and processing data for training data generation are provided. In one technique, multiple encrypted records are retrieved from a first persistent storage. For each encrypted record, that record is decrypted in memory to generate a decrypted record that comprises multiple attribute values. Then, based on the attribute values and a definition of multiple features of a machine-learned model, multiple feature values are generated and stored, along with a label, in a training instance, which is then stored in a second persistent storage. One or more machine learning techniques are used to train the machine-learned model based on training data that includes the training instances that are stored in the second persistent storage.

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ELECTRONIC SYSTEM FOR IDENTIFYING FAULTY CODE AND VULNERABILITIES IN SOFTWARE PROGRAMS USING LINKED EVALUATION TOOLS

Publication No.: US2022253532A1 11/08/2022

Applicant:

BANK OF AMERICA [US]

Absstract of: US2022253532A1

Systems, computer program products, and methods are described herein for dynamically generating linked security tests. The present invention may be configured to perform security tests on an application, generate, based on the results of the security tests, security test sequences that include at least one security test that the application failed, perform the security test sequences on the application, and, iteratively and until the application passes each security test sequence in an iteration, generate additional security test sequences. The present invention may be further configured to provide results of the security tests and security test sequences to one or more machine learning models to generate supplementary security test sequences and determine probabilities of the application failing the supplementary security test sequences.

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MACHINE-LEARNING BASED PERSONALIZATION

Publication No.: US2022253496A1 11/08/2022

Applicant:

SITECORE CORP A/S [DK]

US_2020272672_A1

Absstract of: US2022253496A1

A system, method, and apparatus provide the ability to generate and deliver personalized digital content. Multiple content tests are performed by presenting different variants of content to a set of different consumers of one or more consumers. A machine learning (ML model is generated and trained based on an analysis of results of the multiple content tests. Based on the ML model, personalization rules, that specify a certain variance for a defined set of facts, are output. The personalization rules are exposed to an administrative user who selects one or more of the personalization rules. A request for content is received from a requesting consumer. Based on similarities between the defined set of facts and the requesting consumer, a subset of the selected personalization rules are selected. The content is personalized and delivered to the requesting consumer based on the further selected personalization rules.

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ENHANCED SENSOR OPERATION

Publication No.: US2022256123A1 11/08/2022

Applicant:

FORD GLOBAL TECH LLC [US]
UNIV MICHIGAN STATE [US]

DE_102022102443_PA

Absstract of: US2022256123A1

A two-dimensional image of a vehicle occupant in a vehicle is collected. The collected two-dimensional image is input to a machine learning program trained to output one or more reference points of the vehicle occupant, each reference point being a landmark of the vehicle occupant. One or more reference points of the vehicle occupant in the two-dimensional image is output from the machine learning program. A location of the vehicle occupant in an interior of the vehicle is determined based on the one or more reference points. A vehicle component is actuated based on the determined location. For each of the one or more reference points, a similarity measure is determined between the reference point and a three-dimensional reference point, the similarity measure based on a distance between the reference point and the three-dimensional reference point.

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THIN DATA WARNING AND REMEDIATION SYSTEM

Publication No.: US2022255881A1 11/08/2022

Applicant:

TRIANGLE IP INC [US]

US_11323388_B1

Absstract of: US2022255881A1

The present disclosure describes a patent management system and method for remediating insufficiency of input data for a machine learning system. A plurality of data vectors using data are extracted from a plurality of data sources. A user input with respect to an input data context is received, the input data context correspond to a subset of the plurality of data elements. An input vector based on the user input is generated and a set of matching data vectors are determined from the plurality of data vectors based on the input vector. An insufficiency of the input data is determined based on a comparison of a number of matching data vectors with a first pre-determined threshold, and/or a variance with a second pre-determined threshold. Further, the set of matching data vectors are expanded by modifying the input vector when the input data is determined to be insufficient.

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Method and System for Determining and Reclassifying Valuable Words

Publication No.: US2022253728A1 11/08/2022

Applicant:

AWOO INTELLIGENCE INC [TW]

Absstract of: US2022253728A1

Method and system for determining and reclassifying valuable words, wherein a large amount of text and valuable words are pre-inputted into a word processing server for machine learning. Moreover, the word processing server is trained on the valuable words and many labels associated with the valuable words such that it can learn and determines the valuable words in the text that meet the definition of the valuable word. The valuable word is further extracted from the text and re-classified after extraction. In addition, each valuable word is provided with various relevance labels to facilitate the subsequent application of the valuable words.

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MACHINE LEARNING MODEL FOR ENTITY RESOLUTION

Publication No.: US2022253725A1 11/08/2022

Applicant:

CAPITAL ONE SERVICES LLC [US]

Absstract of: US2022253725A1

In some implementations, a system may define common attributes of a first dataset and a second dataset. The system may generate a candidate set of mappings between one or more entities in the first dataset and one or more entities in the second dataset based on candidate generation criteria associated with a related pair of common attributes. The system may generate feature sets for the candidate set of mappings based on the common attributes and a featurization configuration. The system may train a machine learning model for performing entity resolution between the first dataset and the second dataset. The system may perform entity resolution between the first dataset and the second dataset based on the feature sets for the candidate set of mappings using the trained machine learning model.

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AMPLIFYING SOURCE CODE SIGNALS FOR MACHINE LEARNING

Publication No.: US2022253723A1 11/08/2022

Applicant:

IBM [US]

Absstract of: US2022253723A1

Embodiments are disclosed for a method. The method includes identifying one or more source code signals in a source code. The method also include generating an amplified code based on the identified signals and the source code. The amplified code is functionally equivalent to the source code. Further, the amplified code includes one or more amplified signals. The method additionally includes providing the amplified code for a machine learning model that is trained to perform a source code relevant task.

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AUTOMATED RECOMMENDATIONS FOR TASK AUTOMATION

Publication No.: US2022253790A1 11/08/2022

Applicant:

WORKFUSION INC [US]

US_2021042684_A1

Absstract of: US2022253790A1

In one embodiment, a method for providing recommendations for workflow alteration is disclosed. Task results for completion of a first set of iterations of a workflow are received. Training data may be extracted from the task results. The training data may be used to build a machine learning model for altering at least a portion of the workflow. An automation forecast that assesses the effects of altering the workflow for a second set of the iterations of the task may be generated, and a workflow alteration recommendation may be provided. Based on automation parameters, such as a minimum required level of accuracy, and the automation forecast, a recommendation regarding whether to automate the task may be included in the workflow alteration recommendation. Finally, based on the recommendation, an automated process may be generated to handle at least a portion of the task.

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HYDROCARBON OIL FRACTION PREDICTION WHILE DRILLING

Publication No.: US2022253726A1 11/08/2022

Applicant:

SAUDI ARABIAN OIL CO [SA]

Absstract of: US2022253726A1

A method includes building a mud-gas hydrocarbon oil fraction database comprising historical data, training a machine learning model using the historical data in the mud-gas hydrocarbon oil fraction database, drilling a new wellbore, processing drilling mud returns, from the new wellbore, through a gas sampler comprising a gas chromatograph and a gas mass spectrometer, retrieving real-time mud-gas data from the gas sampler, and generating a real-time hydrocarbon oil fraction log for the new wellbore by processing the real-time mud-gas data through the trained machine learning model and producing estimated hydrocarbon oil fraction data.

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A MACHINE LEARNING APPROACH TO MULTI-DOMAIN PROCESS AUTOMATION AND USER FEEDBACK INTEGRATION

Publication No.: WO2022167904A1 11/08/2022

Applicant:

SISCALE AI INC [US]

US_2022245511_A1

Absstract of: WO2022167904A1

Embodiments relate to multi-domain process automation with user feedback integration. Some embodiments include a method performed by one or more computing devices. The one or more computing devices generate, using a machine learning (ML) model, predictions for records. The one or more computing devices receive at least one of single user feedback or multiple user feedback for the predictions. The one or more computing devices generate a user validated record pool based on the single user feedback or multiple user feedback. The one or more computing devices update the ML model using the user validated record pool.

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CONFIGURING AN INSTANCE OF A SOFTWARE PROGRAM USING MACHINE LEARNING

Publication No.: WO2022165168A1 04/08/2022

Applicant:

SPLUNK INC [US]

US_2022244934_A1

Absstract of: WO2022165168A1

Disclosed are embodiments of a system for receiving, from a product management system, a model trained to select one state from a set of predefined states based on a state of an installation of a software program on a computing device. Each of the predefined states are associated with a configuration of the software program and each configuration of the software program are associated with operational parameter values of the software program. The system further determines a state of the installation of the software program, inputs the determined state into the model, obtains, from the model, and based on the determined state, the selection of the one state from the set of predefined states. Finally, the system adjusts a parameter of the software program according to the selected one predefined state.

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ENTITY SELECTION METRICS

Publication No.: WO2022162343A1 04/08/2022

Applicant:

BENEVOLENTAI TECH LIMITED [GB]

Absstract of: WO2022162343A1

Embodiments of present disclosure provide a system, apparatus and method(s) for generating a set of metrics for evaluating entities used with a predictive machine learning model, the method comprising: selecting one or more sets of entities from a data sources for generating a plurality of predictions aggregated from said one or more sets of entities using one or more pre-trained predictive models; selecting a subset of predictions from the plurality of predictions based on said one or more sets of entities in relation to the data source; extracting metadata from the data source associated with the subset of predictions, where the metadata comprises entity metadata and predicted metadata; generating the set of metrics based on the metadata extracted and the subset of predictions; and outputting the set of metrics for evaluation.

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SYSTEM AND COMPUTER-IMPLEMENTED METHOD FOR VALIDATION OF LABEL DATA

Publication No.: WO2022164491A1 04/08/2022

Applicant:

UIPATH INC [US]

US_2022237800_A1

Absstract of: WO2022164491A1

A system and a computer-implemented method for validating label data includes receiving the label data and segmenting it into one or more parts using a first machine learning model. Further, from the segmented label data a first plurality of attributes, including text and images, are extracted. The method further includes receiving ground truth data associated with the label data and extracting a second plurality of attributes from the ground truth data. The first and second plurality of attributes are then compared using a second machine learning model and the result of comparison are displayed on a three pane user interface. Further, the label data is validated based on the displayed results.

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DISTRIBUTED MACHINE LEARNING WITH NEW LABELS USING HETEROGENEOUS LABEL DISTRIBUTION

Publication No.: WO2022162677A1 04/08/2022

Applicant:

ERICSSON TELEFON AB L M [SE]
GAUTHAM KRISHNA GUDUR [IN]

Absstract of: WO2022162677A1

A method for distributed machine learning (ML) which includes providing a first dataset including a first set of labels to a plurality of local computing devices including a first local computing device and a second local computing device. The method further includes receiving, from the first local computing device, a first set of ML model probabilities values from training a first local ML model using the first set of labels. The method further includes receiving, from the second local computing device, a second set of ML model probabilities values from training a second local ML model using the first set of labels and one or more labels different from any label in the first set of labels. The method further includes generating a weights matrix using the received first set of ML model probabilities values and the received second set of ML model probabilities values. The method further includes generating a third set of ML model probabilities values by sampling using the generated weights matrix.

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METHOD & APPARATUS FOR DETERMINING AND/OR PREDICTING SLEEP AND RESPIRATORY BEHAVIOURS FOR MANAGEMENT OF AIRWAY PRESSURE

Publication No.: US2022241530A1 04/08/2022

Applicant:

NOVARESP TECH INC [CA]

WO_2021168588_A1

Absstract of: US2022241530A1

Devices, systems and methods are provided for controlling the operation of a breathing assistance device for a user. The controller may include an input for receiving sensor data to measure at least one airflow parameter of the user's airflow; a memory unit that stores at least one machine learning model and at least one classifier or predictor; and a processor that is configured to perform measurements and to generate a control signal for adjusting the operation of the breathing assistance device for a current monitoring time period by: obtaining measured air pressure and/or airflow data and measured FOT data during a current monitoring time period; performing feature extraction on the measured data to obtain feature values that are used by the machine learning model employed by the at least one classifier or predictor to determine a property of the user; and adjusting the control signal based on the determined property.

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DEVICES AND METHODS FOR MACHINE LEARNING ASSISTED PRECODING

Publication No.: US2022247460A1 04/08/2022

Applicant:

INST MINES TELECOM [FR]

KR_20220031624_PA

Absstract of: US2022247460A1

A precoder for precoding a vector of information symbols is provided. The precoder includes a radius determination unit configured to determine a search sphere radius, the determination of the search sphere radius comprising applying a machine learning algorithm to input data dependent on the vector of information symbols and on a predefined precoding matrix; a sphere encoding unit configured to determine a perturbation vector from lattice points found inside a spherical region by applying a sphere search-based sequential algorithm, the spherical region having as a radius the search sphere radius, and a precoding unit configured to precode the vector of information symbols using the perturbation vector and a precoding matrix.

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A system and method for training machine-learning algorithms for processing biology-related data, a microscope and a trained machine learning algorithm

Publication No.: US2022246244A1 04/08/2022

Applicant:

LEICA MICROSYSTEMS [DE]

CN_114450751_PA

Absstract of: US2022246244A1

A system (100) comprises one or more processors (110) and one or more storage devices (120), wherein the system (100) is configured to generate a first high-dimensional representation of the biology-related language-based input training data (102) by a language recognition machine-learning algorithm executed by the one or more processors (110). Further, the system (100) is configured to generate biology-related language-based output training data based on the first high-dimensional representation by the language recognition machine-learning algorithm and adjust the language recognition machine-learning algorithm based on a comparison of the biology-related language-based input training data (102) and the biolo-gy-related language-based output training data. Additionally. the system (100) is configured to generate a second high-dimensional representation of the biology-related image-based input training data (104) by a visual recognition machine-learning algorithm executed by the one or more processors (110) and adjust the visual recognition machine-learning algorithm based on a comparison of the first high-dimensional representation and the second high-dimensional representation.

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PERSONALIZED LEARNING SYSTEM AND METHOD FOR THE AUTOMATED GENERATION OF STRUCTURED LEARNING ASSETS BASED ON USER DATA

Nº publicación: US2022246052A1 04/08/2022

Applicant:

CEREGO JAPAN KK [JP]

US_2022084429_A1

Absstract of: US2022246052A1

Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.

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