MACHINE LEARNING

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Resultados 91 resultados LastUpdate Última actualización 03/10/2022 [13:04:00] pdf PDF xls XLS

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



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SYSTEM FOR IDENTIFYING AND DEVELOPING FOOD INGREDIENTS FROM NATURAL SOURCES BY MACHINE LEARNING AND DATABASE MINING COMBINED WITH EMPIRICAL TESTING FOR A TARGET FUNCTION

NºPublicación: WO2022204122A1 29/09/2022

Solicitante:

SHIRU INC [US]
HUME JASMIN [US]
DUBOURG FELONNEAU GEOFFROY [US]
KUNIBE AKEMI [US]
AKEVA EYAL [US]
LEE LAWRENCE [US]

US_2022104515_A1

Resumen de: WO2022204122A1

This disclosure provides a technology for developing alternative protein sources for use in industrial food production. The technology mines natural sources by a process that is done partly in silico. Instead of sampling and testing a vast library of compounds, machine learning and implementation narrows the field of functional candidates by predictive modeling based on known protein structure. Candidate proteins that are selected by this analysis are then produced and screened in a high-throughput manner by recombinant expression and testing to determine whether they have a target function. Multiple cycles of the machine learning, database mining, expression and testing are done to yield potential ingredients suitable for assessment as part of a commercial food product.

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VEHICULAR SEARCH RECOMMENDATIONS USING MACHINE LEARNING

NºPublicación: US2022309556A1 29/09/2022

Solicitante:

CAPITAL ONE SERVICES LLC [US]

Resumen de: US2022309556A1

Systems, methods, and computer readable media for vehicular search recommendations using machine learning. A computing model may receive a query for a vehicular recommendation. The model may be trained based on historical queries, webpages visited, and attributes of vehicles. The model may generate a decision tree comprising a plurality of paths for processing the query, the plurality of paths comprising a subset of a plurality of available paths for processing the query, each path associated with a plurality of search phases. The model may select, based on the decision tree, a first path of the plurality of paths for processing the query. The model may select, based on the first path, a first search phase of the plurality of search phases as corresponding to the query. The model may then return a search result corresponding to the first search phase as responsive to the query.

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INTELLIGENT PLANNING, EXECUTION, AND REPORTING OF CLINICAL TRIALS

NºPublicación: US2022310216A1 29/09/2022

Solicitante:

TRIALS AL INC [US]

US_2019206521_PA

Resumen de: US2022310216A1

Machine learning based methods for planning, execution, and reporting of clinical trials, incorporating a patient burden index are disclosed. In one aspect, there is a method for determining a patient burden index. The method includes parsing a protocol for a clinical trial. The method further includes providing factor data for each of a plurality of patients. The method further includes calculating a patient burden index for each of the plurality of patients based on the parsed protocol and the provided factor data for each of the plurality of patients.

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SELECTING REPRESENTATIVE FEATURES FOR MACHINE LEARNING MODELS

NºPublicación: US2022309384A1 29/09/2022

Solicitante:

IBM [US]

Resumen de: US2022309384A1

A set of input features, each feature having a value, can be processed to determine pairwise correlations between features of the set. The features can be arranged into groups based on correlations with one another. Each feature can also be analyzed to determine a predictive value. A representative feature of each group can be selected based on the predictive value.

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Automatic Identification of Improved Machine Learning Models

NºPublicación: US2022309379A1 29/09/2022

Solicitante:

IBM [US]

Resumen de: US2022309379A1

Identifying new machine learning models with improved metrics is provided. A new machine learning model is searched for that is relevant to a current machine learning model running within a client device and has improved metrics over current metrics of the current machine learning model. It is determined whether a relevant new machine learning model having improved metrics over the current metrics of the current machine learning model was found in the search. In response to determining that a relevant new machine learning model having improved metrics was found in the search, it is determined whether the relevant new machine learning model is compatible with the current machine learning model. In response to determining that the relevant new machine learning model is compatible with the current machine learning model, the relevant new machine learning model is automatically implemented in the client device.

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QUANTUM-ENHANCED FEATURES FOR CLASSICAL MACHINE LEARNING

NºPublicación: US2022309386A1 29/09/2022

Solicitante:

IBM [US]

WO_2022200475_A1

Resumen de: US2022309386A1

Systems and techniques that facilitate quantum-enhanced features for classical machine learning are provided. In various embodiments, a system can comprise a receiver component that can access a classical dataset. In various aspects, the system can further comprise a feature component that can generate one or more machine learning input features based on a quantum transformation of the classical data set. In various instances, the system can further comprise an execution component that can execute a classical machine learning model on the one or more machine learning input features.

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METHOD AND APPARATUS FOR IMPROVING PERFORMANCE OF CLASSIFICATION ON THE BASIS OF MIXED SAMPLING

NºPublicación: US2022309401A1 29/09/2022

Solicitante:

ELECTRONICS & TELECOMMUNICATIONS RES INST [KR]

Resumen de: US2022309401A1

A method of improving performance of classification on the basis of mixed sampling is applied. The present invention is directed to providing a method and apparatus for improving the performance of classification on the basis of mixed sampling that are capable of, when learning a model that classifies types using deep learning by dividing the entire data set and using the divided data set for training, validation, and testing, applying different sampling techniques by types of data according to the characteristics of the training data in order to improve the classification performance.

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AUTOMATICALLY PREDICTING TRANSACTION LIKELIHOOD INFORMATION AND TRANSACTION-RELATED TEMPORAL INFORMATION USING MACHINE LEARNING TECHNIQUES

NºPublicación: US2022309363A1 29/09/2022

Solicitante:

DELL PRODUCTS LP [US]

Resumen de: US2022309363A1

Methods, apparatus, and processor-readable storage media for automatically predicting transaction likelihood information and transaction-related temporal information using machine learning techniques are provided herein. An example computer-implemented method includes obtaining historical data pertaining to completed transactions within an enterprise system; determining, for at least one of the completed transactions, a set of multiple transaction-related features by processing at least a portion of the obtained historical data; training at least one machine learning model using at least a portion of the set of multiple determined transaction-related features; predicting transaction likelihood information and transaction-related temporal information associated with input data attributed to at least one pending transaction within the enterprise system by processing at least a portion of the input data using the at least one machine learning model; and performing one or more automated actions based on one or more of the predicted transaction likelihood information and the predicted transaction-related temporal information.

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EFFICIENT AND ACCURATE REGIONAL EXPLANATION TECHNIQUE FOR NLP MODELS

NºPublicación: US2022309360A1 29/09/2022

Solicitante:

ORACLE INT CORP [US]

Resumen de: US2022309360A1

Herein are techniques for topic modeling and content perturbation that provide machine learning (ML) explainability (MLX) for natural language processing (NLP). A computer hosts an ML model that infers an original inference for each of many text documents that contain many distinct terms. To each text document (TD) is assigned, based on terms in the TD, a topic that contains a subset of the distinct terms. In a perturbed copy of each TD, a perturbed subset of the distinct terms is replaced. For the perturbed copy of each TD, the ML model infers a perturbed inference. For TDs of a topic, the computer detects that a difference between original inferences of the TDs of the topic and perturbed inferences of the TDs of the topic exceeds a threshold. Based on terms in the TDs of the topic, the topic is replaced with multiple, finer-grained new topics. After sufficient topic modeling, a regional explanation of the ML model is generated.

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QUANTUM-ENHANCED FEATURES FOR CLASSICAL MACHINE LEARNING

NºPublicación: WO2022200475A1 29/09/2022

Solicitante:

IBM [US]
IBM DEUTSCHLAND [DE]

US_2022309386_PA

Resumen de: WO2022200475A1

Systems and techniques that facilitate quantum-enhanced features for classical machine learning are provided. In various embodiments, a system can comprise a receiver component that can access a classical dataset. In various aspects, the system can further comprise a feature component that can generate one or more machine learning input features based on a quantum transformation of the classical data set. In various instances, the system can further comprise an execution component that can execute a classical machine learning model on the one or more machine learning input features.

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SYSTEMS AND METHODS FOR END-TO-END MACHINE LEARNING WITH AUTOMATED MACHINE LEARNING EXPLAINABLE ARTIFICIAL INTELLIGENCE

NºPublicación: WO2022200624A2 29/09/2022

Solicitante:

DATAWALK SPOLKA AKCYJNA [PL]

Resumen de: WO2022200624A2

The present disclosure provides systems and methods for end-to-end machine learning. A method of the present disclosure may comprise one or more operations of data ingestion, data preparation, feature storage, model building, and productionizing by the model. The methods and systems of the present disclosure may use an Automated Machine Learning (AutoML) algorithm and eXplainable Artificial Intelligence (XAI).

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EFFICIENT AND ACCURATE REGIONAL EXPLANATION TECHNIQUE FOR NLP MODELS

NºPublicación: WO2022203783A1 29/09/2022

Solicitante:

ORACLE INT CORP [US]

US_2022309360_PA

Resumen de: WO2022203783A1

Herein are techniques for topic modeling and content perturbation that provide machine learning (ML) explainability (MLX) for natural language processing (NLP). A computer hosts an ML model that infers an original inference for each of many text documents that contain many distinct terms. To each text document (TD) is assigned, based on terms in the TD, a topic that contains a subset of the distinct terms. In a perturbed copy of each TD, a perturbed subset of the distinct terms is replaced. For the perturbed copy of each TD, the ML model infers a perturbed inference. For TDs of a topic, the computer detects that a difference between original inferences of the TDs of the topic and perturbed inferences of the TDs of the topic exceeds a threshold. Based on terms in the TDs of the topic, the topic is replaced with multiple, finer-grained new topics. After sufficient topic modeling, a regional explanation of the ML model is generated.

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Intelligent Agent

NºPublicación: US2022309369A1 29/09/2022

Solicitante:

BANK OF AMERICA [US]

US_2021406713_A1

Resumen de: US2022309369A1

A computing system aggregates information from a plurality of information channels associated with a computing device and a user of the computing device. A user configures the access for the computing system to specific information channels at a user interface. Based on a knowledge base and by machine learning techniques, the computing system analyzes the aggregated information to identify information relevant to an intelligent action for execution on behalf of the user. The computing system identifies the intelligent action in the context of the user's preferences and permissions granted to the computing system. The computing system initiates execution of the intelligent action based on a confidence level derived from analysis of information contained the knowledge base and historical decisioning information. The computing system receives feedback for an executed action and incorporates the feedback in the knowledge base for future decisioning based on aggregated information.

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INTERACTIVE MACHINE LEARNING OPTIMIZATION

NºPublicación: US2022309391A1 29/09/2022

Solicitante:

IBM [US]

Resumen de: US2022309391A1

Methods, computer program products, and systems are presented. The method, computer program products, and systems can include, for instance: examining an enterprise dataset, the enterprise dataset defined by enterprise collected data; selecting one or more synthetic dataset in dependence on the examining, the one or more synthetic dataset including data other than data collected by the enterprise; training a set of predictive models using data of the one or more synthetic dataset to provide a set of trained predictive models; testing the set of trained predictive models with use of holdout data of the one or more synthetic dataset; and presenting prompting data on a displayed user interface of a developer user in dependence on result data resulting from the testing, the prompting data prompting the developer user to direct action with respect to one or more model of the set of predictive models.

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SYSTEMS AND METHODS FOR CUSTOMIZING A USER WORKSPACE ENVIRONMENT USING A.I-BASED ANALYSIS

NºPublicación: US2022309367A1 29/09/2022

Solicitante:

ACRONIS INT GMBH [CH]

Resumen de: US2022309367A1

Disclosed herein are systems and method for customizing a user workspace environment using artificial intelligence-based analysis. In one exemplary aspect, a method comprises detecting user actions in a user workspace environment that provides access to a plurality of workspace elements. The method comprises generating a plurality of rules for customizing the user workspace environment, wherein each rule links a set of input parameters of a machine learning algorithm as criteria with an output user action and an output identifier of the workspace element, and wherein each rule assigns at least one customization action that (1) reduces an amount of steps to perform in the user workspace environment to access the workspace element associated with the output identifier and (2) reduces a processing time to perform the output user action. When a criterion is fulfilled, the method comprises executing a corresponding customization action that alters the user workspace environment.

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ARTIFICIAL-INTELLIGENCE ARCHITECTURE FOR DETECTING DOCUMENT MANIPULATION

NºPublicación: US2022309365A1 29/09/2022

Solicitante:

LENDBUZZ INC [US]

Resumen de: US2022309365A1

The present disclosure generally relates to techniques for constructing an artificial-intelligence (AI) architecture. The present disclosure relates to techniques for executing the AI architecture to detect whether or not characters in a digital document have been manipulated. The AI architecture can be configured to classify each character in a digital document as manipulated or not manipulated by constructing a graph for each character, generating features for each node of the graph, and inputting a vector representation of the graph into a trained machine-learning model to generate the character classification.

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EVALUATING ON-DEVICE MACHINE LEARNING MODEL(S) BASED ON PERFORMANCE MEASURES OF CLIENT DEVICE(S) AND/OR THE ON-DEVICE MACHINE LEARNING MODEL(S)

NºPublicación: US2022309389A1 29/09/2022

Solicitante:

GOOGLE LLC [US]

Resumen de: US2022309389A1

Implementations disclosed herein are directed to systems and methods for evaluating on-device machine learning (ML) model(s) based on performance measure(s) of client device(s) and/or the on-device ML model(s). The client device(s) can include on-device memory that stores the on-device ML model(s) and a plurality of testing instances for the on-device ML model(s). When certain condition(s) are satisfied, the client device(s) can process, using the on-device ML model(s), the plurality of testing instances to generate the performance measure(s). The performance measure(s) can include, for example, latency measure(s), memory consumption measure(s), CPU usage measure(s), ML model measure(s) (e.g., precision and/or recall), and/or other measures. In some implementations, the on-device ML model(s) can be activated (or kept active) for use locally at the client device(s) based on the performance measure(s). In other implementations, the on-device ML model(s) can be sparsified based on the performance measure(s).

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ADVERSE FEATURES NEUTRALIZATION IN MACHINE LEARNING

NºPublicación: US2022309359A1 29/09/2022

Solicitante:

PAYPAL INC [US]

Resumen de: US2022309359A1

Methods and systems are presented for identifying and neutralizing adverse input features that negatively impact accuracy of a machine learning model. A machine learning model is configured to produce an output based on parameter values corresponding to input features. Each input feature is evaluated with respect to its impact on producing a correct output by the machine learning model. One or more adverse input features that have a negative impact on accuracy of the machine learning model are determined. When a request to assess a data is received, input values associated with the data and corresponding to the set of input features are obtained. One or more input values corresponding to the adverse input features are identified. The one or more input values are altered, and the altered input values along with other unaltered input values are used to generate a more accurate output by the machine learning model.

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AUTOMATED PROGRAM ACCESS CONFIGURATION BASED ON INTERACTION AND TIMING DATA

NºPublicación: US2022309361A1 29/09/2022

Solicitante:

CAPITAL ONE SERVICES LLC [US]

Resumen de: US2022309361A1

In certain embodiments, access to a plurality of sites associated with a plurality of users may be obtained. The plurality of sites may comprise categories of the plurality of users and interactions among the plurality of users. Based on the plurality of sites, a plurality of interaction datasets and a plurality of timing datasets may be generated. The plurality of interaction datasets and the plurality of timing datasets may be provided as inputs to a machine learning model and the machine learning model may be configured based on the inputs. Subsequent to the configuration, an interaction dataset and a timing dataset associated with a user may be provided to the machine learning model. A predicted length of time for the user may be obtained via the machine learning model and one or more settings of a program may be configured to the user based on the predicted length of time.

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AUTOMATED NATURAL LANGUAGE GENERATION AND SELECTION

NºPublicación: US2022309245A1 29/09/2022

Solicitante:

IBM [US]

Resumen de: US2022309245A1

A method, system, and computer program product for implementing machine learning natural language digital package generation and selection is provided. The method includes receiving from hardware and software sources, factual data associated with an event. In response, natural language digital templates comprising natural language phrase variants for each portion of the factual data is generated. Factual data phrases are generated and packaged into digital packages including at least one natural language phrase variant with respect to each portion of factual data. An initial package is selected by minimizing a number of repetitions of the factual data phrases across the digital packages and digital summaries are extracted. Alignment attributes associated with the digital summaries are determined with respect to the initial package and a final package is selected. A hardware device is enabled for presenting a video stream including the final package with respect to the event.

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BOOSTING AND MATRIX FACTORIZATION

NºPublicación: WO2022203678A1 29/09/2022

Solicitante:

GOOGLE LLC [US]

Resumen de: WO2022203678A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for presenting a new machine learning model architecture. In some aspects, the methods include obtaining a training dataset with a plurality of training samples that includes feature variables and output variables. A first matrix is generated using the training dataset which is a sparse representation of the training dataset. Generating the first matrix can include generating a categorical representation of numeric features and an encoded representation of the categorical features. The methods further include generating a second, third and a fourth matrix. Each feature of the first matrix is then represented using a vector that includes a multiple adjustable parameters. The machine learning model can learn by adjusting values of the adjustable parameters using a combination of a loss function the fourth matrix, and the first matrix.

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DISTRIBUTED MACHINE LEARNING DECENTRALIZED APPLICATION PLATFORM

NºPublicación: US2022309520A1 29/09/2022

Solicitante:

SAP SE [DE]

US_2020193451_PA

Resumen de: US2022309520A1

A request for an inference from a customer is received at a machine learning (ML) decentralized application (DAPP) platform, where the request includes a data record associated with a user that is associated with the customer. The data record is distributed by the ML DAPP platform among a number of service providers. An inference is received at the ML DAPP platform from each service provider. The received inferences are returned to the customer by the ML DAPP platform.

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METHOD, DEVICE, AND SYSTEM FOR CONFIGURING A COATING MACHINE

NºPublicación: US2022308535A1 29/09/2022

Solicitante:

SAHU SUBRAT [IN]

EP_4063083_PA

Resumen de: US2022308535A1

A method, device, and system for configuring a coating machine for coating a surface of a product using a coating substance are provided. The method includes determining a value associated with one or more parameters from a plurality of parameters associated with the coating operation. The method also includes predicting a value associated with at least one attribute associable with the coating substance based on the determined value associated with the one or more parameters using a trained machine learning model. The method includes configuring the coating machine for coating the surface using the coating substance based on the predicted value associated with the at least one attribute associable with the coating substance. The method also includes initiating a coating operation at the configured coating machine for coating the surface of the product using the coating substance.

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SERVICE REQUEST REMEDIATION WITH MACHINE LEARNING BASED IDENTIFICATION OF CRITICAL AREAS OF LOG SEGMENTS

NºPublicación: US2022308952A1 29/09/2022

Solicitante:

DELL PRODUCTS LP [US]

CN_115129679_PA

Resumen de: US2022308952A1

An apparatus comprises a processing device configured to receive a service request associated with a given asset, to obtain a log file associated with the given asset, to split the log file into log segments, to generate sets of log pattern identifiers for the log segments, and to determine risk scores for the log segments utilizing a machine learning model that takes as input the sets of log pattern identifiers and provides as output information characterizing risk of the log segments. The processing device is also configured to identify critical areas of the log file based at least in part on the determined risk scores, a given critical area comprising a sequence of log segments having determined risk scores above a designated risk score threshold. The processing device is further configured to analyze the identified critical areas to determine remedial actions to be applied for resolving the service request.

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METHODS OF GENERATING MACHINE LEARNING OUTPUTS

Nº publicación: US2022308992A1 29/09/2022

Solicitante:

KORTICAL LTD [GB]

CN_114026538_PA

Resumen de: US2022308992A1

A computer-implemented method for generating one or more outputs is disclosed. The method comprises providing code in a high-level language, the code comprising one or more statements defining one or more properties of a desired output; determining that one or more properties of the desired output are undefined in the code; defining at least one of the one or more undefined properties using a machine learning algorithm; generating an output based on the one or more properties defined in the code and the at least one property defined using the machine learning algorithm; and redefining at least one property of the output defined using the machine learning algorithm to generate a redefined output. At least one property defined using the machine learning algorithm is redefined automatically, and at least one property defined using the machine learning algorithm is redefined automatically based on an associated level of performance of one or more previous outputs.

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