MACHINE LEARNING

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Resultados 106 resultados LastUpdate Última actualización 20/01/2022 [12:30:00] pdf PDF xls XLS

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



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SYSTEMS AND METHODS FOR WEIGHT MEASUREMENT FROM USER PHOTOS USING DEEP LEARNING NETWORKS

NºPublicación: EP3938956A1 19/01/2022

Solicitante:

BODYGRAM INC [US]

CN_113711235_A

Resumen de: US2020319015A1

Disclosed are systems and methods for body weight prediction from one or more images. The method includes the steps of receiving one or more subject parameters; receiving one or more images containing a subject; identifying one or more annotation key points for one or more body features underneath a clothing of the subject from the one or more images utilizing one or more annotation deep-learning networks; calculating one or more geometric features of the subject based on the one or more annotation key points; and generating a prediction of the body weight of the subject utilizing a weight machine-learning module based on the one or more geometric features of the subject and the one or more subject parameters.

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SYSTEMS AND METHODS FOR AIDING HIGHER EDUCATION ADMINISTRATION USING MACHINE LEARNING MODELS

NºPublicación: EP3938967A1 19/01/2022

Solicitante:

ELLUCIAN COMPANY L P [US]

WO_2020186009_A1

Resumen de: US2020294167A1

Systems and methods applicable, for instance, to using machine learning models to aid higher education administration. Various machine learning model-based tools can be provided. Further provided can be various infrastructure software modules.

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Training a machine learning system for transaction data processing

NºPublicación: GB2597130A 19/01/2022

Solicitante:

FEATURESPACE LTD [GB]

US_2022012742_A1

Resumen de: GB2597130A

A machine learning system 900 for detecting anomalies within transaction data comprises an observable feature generator 910 to generate data representing a first set of features from transaction data 912, 914 for a current transaction, and a context feature generator 920 to generate data representing a second set of features from ancillary data 922, 924. The ancillary data is derived from historical data for a uniquely identifiable entity associated with the transaction data, such as a merchant or customer. A trained binary classifier 40 receives the data representing the first and second sets of features and maps to a scalar value 950 indicative of the presence of an anomaly. The binary classifier is trained based on a training set comprising historical data samples and synthetic data samples. The synthetic data samples are generated by combining features from the first and second sets of features from the historical data samples that respectively relate to two different ones of the set of uniquely identifiable entities. The synthetic data samples are assigned a label indicating presence of an anomaly. Unlabelled data samples within the historical data are assigned a label indicating absence of an anomaly.

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MACHINE LEARNING SYSTEM, METHOD, AND COMPUTER PROGRAM FOR INFERRING USER PRESENCE IN A RESIDENTIAL SPACE

NºPublicación: US2022012630A1 13/01/2022

Solicitante:

AMDOCS DEVELOPMENT LTD [CY]

Resumen de: US2022012630A1

As described herein, a machine learning system, method, and computer program are provided for inferring user presence in a residential space. In use, network usage data is collected from a residential network router operating in a residential space. Additionally, the network usage data is processed by a machine learning algorithm to infer whether a user is present in the residential space. Further, the inference is output for performing one or more related actions.

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PRIORITIZING ALERTS IN INFORMATION TECHNOLOGY SERVICE MANAGEMENT SYSTEMS

NºPublicación: US2022012608A1 13/01/2022

Solicitante:

SERVICENOW INC [US]

Resumen de: US2022012608A1

A plurality of correlations is determined including by applying a machine learning model to a first plurality of features extracted from a plurality of information technology and operations management alerts and information technology service management reporting data. Each correlation of the plurality of correlations is between a corresponding one of the plurality of information technology and operations management alerts and at least one corresponding portion of the information technology service management reporting data. The information technology service management reporting data includes at least one urgency indicator. A prioritized list of information technology and operations management alerts is generated based at least in part on the determined plurality of correlations and the at least one urgency indicator. The prioritized list of information technology and operations management alerts is organized based at least in part on relative priorities of the alerts.

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CELL ACCESSIBILITY PREDICTION AND ACTUATION

NºPublicación: US2022014424A1 13/01/2022

Solicitante:

ERICSSON TELEFON AB L M [SE]

Resumen de: US2022014424A1

A method for predicting cell accessibility issues for a mobile network. The method includes receiving a set of metrics from the mobile network, processing a set of key performance indicators (KPIs) derived from the set of metrics in an ensemble machine learning model, the ensemble machine learning model including an RRC model, an RACH model, an ERAB model, and an S1 signaling model to generate at least one cell accessibility degradation prediction and a confidence score, and applying a root cause mapping to the at least one cell accessibility degradation prediction and the confidence score to identify at least one recommended action to correct a correlated cell accessibility issue.

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CONTEXTUAL DIAGRAM-TEXT ALIGNMENT THROUGH MACHINE LEARNING

NºPublicación: US2022012434A1 13/01/2022

Solicitante:

IBM [US]

Resumen de: US2022012434A1

Embodiments relate to a system, program product, and method for leveraging cognitive systems to facilitate the bilateral contextual alignment of textual material and associated diagrammatic matter. The system, computer program product, and method disclosed herein facilitate leveraging a trained cognitive system to understand textual and diagrammatic patterns established by a user, and to learn to draft and edit such textual material and associated diagrammatic matter based on user behavior and preferences. The cognitive system is trained to bilaterally translate between textual material and diagrammatic matter, where the cognitive system includes natural language processing (NLP) features, diagram analytics features, and one or more user profiles. Machine-drafting of a document is achieved through the trained cognitive system such that first textual material and first diagrammatic matter, including first pictorial objects and first diagrammatic textual matter, are bilaterally translated into second textual material and second diagrammatic matter, which are combined and contextually aligned.

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METHOD FOR IDENTIFYING AN UNKNOWN BIOLOGICAL SAMPLE FROM MULTIPLE ATTRIBUTES

NºPublicación: US2022013197A1 13/01/2022

Solicitante:

AGENCY SCIENCE TECH & RES [SG]

CN_113383236_A

Resumen de: US2022013197A1

A method for identifying an unknown biological sample (e.g. a glycan, an antibody, a metabolite) is disclosed. The method comprises: receiving more than two sample measurements for the unknown biological sample, calculating a sample point in a two-dimensional plot from the more than two sample measurements for the unknown biological sample and identifying the unknown biological sample by comparing the sample point against the plurality of reference points in the two-dimensional plot. The two-dimensional plot includes a plurality of stored reference points corresponding to respective known biological compounds. Each reference point is calculated from a plurality of reference measurements for more than two attributes of the corresponding known biological compound (e.g. by performing principal component analysis on the plurality of reference measurements), with each attribute being different from another attribute. Each reference measurement may be obtained experimentally (e.g. by liquid chromatography, mass spectrometry, tandem mass spectrometry, ion mobility spectrometry) or by a machine learning algorithm.

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QUANTIZING TRAINING DATA SETS USING ML MODEL METADATA

NºPublicación: US2022012639A1 13/01/2022

Solicitante:

VMWARE INC [US]

Resumen de: US2022012639A1

Techniques for quantizing training data sets using machine learning (ML) model metadata are provided. In one set of embodiments, a computer system can receive a training data set comprising a plurality of features and a plurality of data instances, where each data instance includes a feature value for each of the plurality of features. The computer system can further train a machine learning (ML) model using the training data set, where the training results in a trained version of the ML model, and can extract metadata from the trained version of the ML model pertaining to the plurality of features. The computer system can then quantize the plurality of data instances based on the extracted metadata, the quantizing resulting in a quantized version of the training data set.

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METHOD AND MACHINE LEARNING MANAGER FOR HANDLING PREDICTION OF SERVICE CHARACTERISTICS

NºPublicación: US2022012611A1 13/01/2022

Solicitante:

ERICSSON TELEFON AB L M [SE]

WO_2020104072_A1

Resumen de: US2022012611A1

A method and a machine learning manager (100) for handling prediction of service characteristics using machine learning applied in a target domain (102B). A source model MS used for machine learning pre-trained in a source domain (102A) is obtained, and a transfer configuration that divides the source model into a fixed first part and a non-fixed second part is selected. A target model is created by applying the selected transfer configuration on the source model so that the target model is divided into said first and second parts. The second part is then trained using observations collected in the target domain, and the target model MT with the first part and the trained second part is provided for prediction of service characteristics in the target domain.

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SELECTING FORECASTING MODELS BY MACHINE LEARNING BASED ON ANALYSIS OF MODEL ROBUSTNESS

NºPublicación: US2022012609A1 13/01/2022

Solicitante:

IBM [US]

Resumen de: US2022012609A1

A computer-implemented method, a computer program product, and a computer system for selecting predictions by models. A computer receives a request for a forecast of a dependent variable in a time domain, where the time domain includes first time periods that have normal labels due to normal predictor variable data and second time periods that have anomalous labels due to anomalous predictor variable data. The computer retrieves accuracy scores and robustness scores of models, where the accuracy scores indicate forecasting accuracy in the first time periods and the robustness scores indicate forecasting accuracy in the second time periods. For predictions in the first time period, the computer selects dependent variable values predicted by a first model that has highest values of the accuracy scores. For predictions in the second time periods, the computer selects dependent variable values predicted by a second model that has highest values of the robustness scores.

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METHOD AND DEVICE FOR CREATING A SYSTEM FOR THE AUTOMATED CREATION OF MACHINE LEARNING SYSTEMS

NºPublicación: US2022012636A1 13/01/2022

Solicitante:

BOSCH GMBH ROBERT [DE]

DE_102020208671_PA

Resumen de: US2022012636A1

Computer-implemented method for creating a system, which is suitable for creating in an automated manner a machine learning system for computer vision. The method includes: providing predefined hyperparameters; determining an optimal parameterization of the hyperparameters using BOHB (Bayesian optimization (BO) and Hyperband (HB)) for a plurality of different training data sets; assessing all optimal parameterizations on all training data sets of the plurality of different training data sets with the aid of a normalized metric; creating a matrix, the matrix including the evaluated normalized metric for each parameterization and for each training data set; determining meta-features for each of the training data sets; optimizing a decision tree, which outputs as a function of the meta-features and of the matrix which of the optimal parameterization using BOHB is a suitable parameterization for the given meta-features.

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SYSTEMS AND METHODS FOR EVALUATING A BEHAVIOR OF A MACHINE-LEARNING ALGORITHM

NºPublicación: US2022012624A1 13/01/2022

Solicitante:

LADURINI AARON R [US]
KOM ANDREW W [US]
HOWARD REBECCA A [US]
NORTHROP GRUMMAN SYSTEMS CORP [US]

Resumen de: US2022012624A1

A computer implemented method is described herein for post-execution evaluation of a machine-learning (ML) algorithm. The method can include receiving a post-execution version of the ML algorithm having a plurality of behavioral states. The method can include generating behavior identification data identifying a given behavioral state from the plurality of behavioral states of the ML algorithm. The given behavioral state can correspond to a decision-making state of the ML algorithm that the ML algorithm learned during an execution of the ML algorithm. A graphical user interface (GUI) can be generated based on the behavior identification data that includes a behavior object characterizing the given behavioral state of the ML algorithm. Behavior evaluation data can be generated based on a user's interaction with the behavior object. A learning process of the ML algorithm can be altered for future execution of the ML algorithm based on the behavior evaluation data.

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MACHINE LEARNING FOR MISFIRE DETECTION IN A DYNAMIC FIRING LEVEL MODULATION CONTROLLED ENGINE OF A VEHICLE

NºPublicación: US2022010744A1 13/01/2022

Solicitante:

TULA TECHNOLOGY INC [US]

US_11125175_B2

Resumen de: US2022010744A1

Using machine learning for cylinder misfire detection in a dynamic firing level modulation controlled internal combustion engine is described. In a classification embodiment, cylinder misfires are differentiated from intentional skips based on a measured exhaust manifold pressure. In a regressive model embodiment, the measured exhaust manifold pressure is compared to a predicted exhaust manifold pressure generated by neural network in response to one or more inputs indicative of the operation of the vehicle. Based on the comparison, a prediction is made if a misfire has occurred or not. In yet other alternative embodiment, angular crank acceleration is used as well for misfire detection.

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ARTIFICIAL INTELLIGENCE AND/OR MACHINE LEARNING MODELS TRAINED TO PREDICT USER ACTIONS BASED ON AN EMBEDDING OF NETWORK LOCATIONS

NºPublicación: US2022012614A1 13/01/2022

Solicitante:

DSTILLERY INC [US]

US_11068935_B1

Resumen de: US2022012614A1

A computer-implemented method can facilitate delivery of targeted content to user devices in situations in which historic tracking data (e.g., cookie data) is generally unavailable and/or unreliable. A p-dimensional embedding of websites can be generated based on a group of user devices for whom tracking data is available. Conversion event data that indicates indicating whether that audience member performed a conversion action can be received. A machine learning model can be trained using the conversion event data and the positions of websites appearing in the conversion event data within the p-dimensional embedding to predict a likelihood of conversion and/or a type of content to provide given a position in the p-dimensional embedding. When an indication that a user device is accessing a website is received, a position of that website in the p-dimensional embedding can be determined and targeted content can be delivered to the user device.

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AUTO-TUNING OF RULE WEIGHTS IN PROFILES

NºPublicación: US2022012352A1 13/01/2022

Solicitante:

VISA INT SERVICE ASS [US]

Resumen de: US2022012352A1

Disclosed is a system to optimize rule weights for classifying access requests so as to manage rates of false positives and false negative classifications. A rules suggestion engine may suggest a profile of classification rules to a merchant for access requests. The system can optimize weights for the profile of rules using a cost function based on a training set of historical access requests, for example using stepwise regression or machine learning (ML). The system can compute a profile score based on the optimized weights, for example by summing the weights. The system statistically analyzes the profile score using classification thresholds and the historical access requests. The system can perform receiver operating characteristic (ROC) analysis for various threshold values, enabling a user to select a suitable threshold. The system can further optimize by adding or removing rules from the profile of rules.

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APPARATUS AND METHOD FOR HYPERPARAMETER OPTIMIZATION OF A MACHINE LEARNING MODEL IN A FEDERATED LEARNING SYSTEM

NºPublicación: US2022012601A1 13/01/2022

Solicitante:

HUAWEI TECH CO LTD [CN]

CN_113614750_A

Resumen de: US2022012601A1

A Federated learning server and a method are provided. The Federated learning server is configured to aggregate a plurality of received model updates to update a master machine learning model. Once a pre-defined threshold or interval for received model updates is reached, a set of current hyper-parameter values and corresponding validation set performance metrics obtained from the updated master machine learning model are sent to a hyper-parameter optimization model. The optimization model infers the next set of optimal hyper-parameters using pairwise history of hyper-parameter values and the corresponding performance metrics. The inferred hyper-parameter values are sent to the Federated Learning server which updates the master machine learning model with the updated set of hyper-parameter values and redistributes the updated master machine learning model with the updated set of hyper-parameter values. According to the application, hyper-parameter optimization in a Federated learning mode can be realized to provide accurate personalized recommendations.

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SYSTEM AND METHOD FOR EVALUATING MACHINE LEARNING MODEL BEHAVIOR OVER DATA SEGMENTS

NºPublicación: US2022012613A1 13/01/2022

Solicitante:

TRUERA INC [US]

Resumen de: US2022012613A1

A computing machine receives a representation of a machine learning model, a representation of a first data segment, and a representation of a second data segment. The computing machine computes an output difference between an output of the machine learning model applied to the first data segment and an output of the machine learning model applied to the second data segment. The computing machine determines a set of reasons for the computed output difference based on a set of metrics defining distance between feature importance distributions, the set of reasons identifying a set of features from a feature vector of the machine learning model along with a relative contribution of each feature to the computed output difference. The computing machine provides an output representing the set of reasons.

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VIRTUAL METROLOGY APPARATUS, VIRTUAL METROLOGY METHOD, AND VIRTUAL METROLOGY PROGRAM

NºPublicación: US2022011747A1 13/01/2022

Solicitante:

TOKYO ELECTRON LTD [JP]

JP_WO2020111258_A1

Resumen de: US2022011747A1

A virtual metrology apparatus, a virtual metrology method, and a virtual metrology program that allow a highly accurate virtual metrology process to be performed is provided. A virtual metrology apparatus includes an acquisition unit configured to acquire a time series data group measured in association with processing of a target object in a predetermined processing unit of a manufacturing process, and a training unit configured to train a plurality of network sections by machine learning such that a result of consolidating output data produced by the plurality of network sections processing the acquired time series data group approaches inspection data of a resultant object obtained upon processing the target object in the predetermined processing unit of the manufacturing process.

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METHOD FOR GENERATING DENTAL MODELS BASED ON AN OBJECTIVE FUNCTION

NºPublicación: US2022008175A1 13/01/2022

Solicitante:

3SHAPE AS [DK]

CN_113728363_A

Resumen de: US2022008175A1

A computer-implemented method of generating a dental model based on an objective function output, including creating an objective function including at least one quality estimation function which trains at least one machine learning method that generates quality estimation output, and an objective function output is the output of the objective function providing a model as an input data to the objective function and generating model-related objective function output; and modifying the model based on the model-related objective function output to transform the model to a generated model, wherein the generated model is the dental model.

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ACCOUNT PREDICTION USING MACHINE LEARNING

NºPublicación: US2022012643A1 13/01/2022

Solicitante:

INTUIT INC [US]

Resumen de: US2022012643A1

Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include receiving a historical support record comprising time-stamped actions, a support initiation time, and an account indication. Embodiments include determining features of the historical support record based at least on differences between times of the time-stamped actions and the support initiation time. Embodiments include determining a label for the features based on the account indication. Embodiments include training an ensemble model, using training data comprising the features and the label, to determine an indication of an account in response to input features, wherein the ensemble model comprises a plurality of tree-based models and a ranking model.

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MACHINE LEARNING SYSTEM, METHOD, AND COMPUTER PROGRAM FOR MANAGING GUEST NETWORK ACCESS IN A RESIDENTIAL SPACE

NºPublicación: US2022012631A1 13/01/2022

Solicitante:

AMDOCS DEVELOPMENT LTD [CY]

Resumen de: US2022012631A1

As described herein, a machine learning system, method, and computer program are provided for managing guest network access in a residential space. In use, network usage data is collected from a residential network router operating in a residential space. Additionally, a machine learning algorithm processes the network usage data to classify a user device connected to the residential network router as being operated by a guest of the residential space. Further, the classification is output for performing one or more related actions.

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GENERALIZED METRIC FOR MACHINE LEARNING MODEL EVALUATION FOR UNSUPERVISED CLASSIFICATION

NºPublicación: US2022012632A1 13/01/2022

Solicitante:

INTUIT INC [US]

Resumen de: US2022012632A1

Certain aspects of the present disclosure provide techniques for generalized metric for machine learning model evaluation for unsupervised classification including: for each unsupervised machine learning model of one or more unsupervised machine learning models: generating a first set of synthetic inputs for the model of the one or more unsupervised machine learning models; providing the first set of synthetic inputs to the model trained to output a prediction for each input of the first set of synthetic inputs, wherein the prediction indicates whether the input is of a first class; identifying, based on an output of the model, a second set of synthetic inputs predicted to be of the first class; determining, based on a set of expected normal inputs for the model and the second set of synthetic inputs, an accuracy score for the unsupervised machine learning model; and providing the accuracy score for display.

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MACHINE LEARNING FOR DETERMINING SUITABILITY OF APPLICATION MIGRATION FROM LOCAL TO REMOTE PROVIDERS

NºPublicación: US2022012627A1 13/01/2022

Solicitante:

IBM [US]

Resumen de: US2022012627A1

A computer implemented method is provided that includes using historic migration data to label key performance indicators (KPIs) in a migration model including a scale that indicates a level of successful migration to a remote provider. Employing the migration model to predict successful migration of a local application having one or more of said one or more of local key performance indicators for the local application. Migrating the local application to a remote provider when the model to predict successful migration indicates a greater than threshold value for successful migration.

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ADAPTIVE QUANTUM SIGNAL PROCESSOR

Nº publicación: US2022012618A1 13/01/2022

Solicitante:

COLDQUANTA INC [US]

Resumen de: US2022012618A1

An adaptive quantum signal processor (AQSP) includes a signal combiner, a physics station, a measurement system, a machine-learning engine and an output generator. The signal combiner combines incoming signals with control functions to yield recipe functions. For example, the recipe functions can be “shaking” functions used to change the wavefunctions of atoms entrained in an optical lattice. The recipe functions are applied to wavefunctions in initial wavefunction states causing the wavefunctions to transition to signal-impacted states. The measurement system measures the wavefunctions in their signal-impacted quantum states to yield wavefunction characterizations. The machine-learning engine updates control functions based on the wavefunction characterizations. The output generator outputs results based on the wavefunction characterizations and/or control function characterizations. In a matched-filter application, the outputs characterize (e.g., identify, classify, rate) the incoming signals.

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