MACHINE LEARNING

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Resultados 59 resultados LastUpdate Última actualización 22/11/2019 [14:10:00] pdf PDF

Solicitudes publicadas en los últimos 60 días / Applications published in the last 60 days

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ANOMALY DETECTION BASED ON COMMUNICATION BETWEEN ENTITIES OVER A NETWORK

NºPublicación: US2019327251A1 24/10/2019

Solicitante:

SPLUNK INC [US]

US_2019342311_A1

Resumen de: US2019327251A1

A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

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METHOD FOR CONVERTING NOMINAL TO ORDINAL OR CONTINUOUS VARIABLES USING TIME-SERIES DISTANCES

NºPublicación: US2019325339A1 24/10/2019

Solicitante:

TRENDALYZE INC [US]

Resumen de: US2019325339A1

A method and system for converting non-ordered categorical data stored within a column in a data set into an ordered or continuous data stored in a new column within the data set. Each distinct categorical value in the nominal data column is represented by a corresponding distinct numerical value in the new column. The new representative numerical values are derived by constructing separate time series for each distinct value in the nominal data column and by calculating the similarities between the shapes of the time series. The proximity of the time series is captured in a numeric distance score. Each distinct distance score corresponds to a distinct value in the nominal data column and is a valid representation of that value in machine learning, deep learning, and statistical analysis.

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EARLY WARNING AND COLLISION AVOIDANCE

NºPublicación: US2019325754A1 24/10/2019

Solicitante:

DERQ INC [VG]

US_2019287403_A1

Resumen de: US2019325754A1

Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

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Automatic Pre-detection of Potential Coding Issues and Recommendation for Resolution Actions

NºPublicación: US2019324886A1 24/10/2019

Solicitante:

IBM [US]

US_2018107583_A1

Resumen de: US2019324886A1

A tool for automatic pre-detection of potential software product impact according to a statement placed in a software development system, and for automatically recommending for resolutions which accesses a repository of information containing a history of changes and effects of the changes for a software project; using a received a statement in natural language to perform a natural language search of the repository; according to the findings of the search of the repository, using a machine learning model to compose an impact prediction regarding the received statement relative to the findings; and automatically placing an advisory notice regarding to the impact prediction into the software development system, wherein the advisory notice is associated with the received statement.

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METHOD AND SYSTEM FOR PREDICTING PATIENT OUTCOMES USING MULTI-MODAL INPUT WITH MISSING DATA MODALITIES

NºPublicación: US2019325995A1 24/10/2019

Solicitante:

NEC LABORATORIES EUROPE GMBH [DE]

Resumen de: US2019325995A1

A method for predicting a patient outcome from a caretaker episode includes receiving a current episode snapshot of the caretaker episode comprising multi-modal data of the patient from an electronic health records (EHR) system, the multi-modal data including one or more available data modalities and one or more missing data modalities. The multi-modal data is applied as input to an embedding model having a submodel for each of the data modalities. A first embedding is generated for each of the available data modalities. A second embedding is generated for each of the missing data modalities using corresponding embeddings of neighbors in an episode snapshot graph. The first and second embeddings are combined to obtain a complete embedding. The patient outcome is predicted based on the complete embedding for the current episode snapshot using a machine learning component which has been trained using patient outcomes of the historical episode snapshots.

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MACHINE LEARNING PREDICTIVE LABELING SYSTEM

NºPublicación: US2019325267A1 24/10/2019

Solicitante:

SAS INST INC [US]

Resumen de: US2019325267A1

A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix. A label is selected for each observation. A mean observation vector and a covariance matrix are computed from the unlabeled observations selected to have each respective label. A number of eigenvalues that have a smallest value is selected from each covariance matrix and used to define a null space for each respective label. A distance value is computed for a distance vector computed to the mean observation vector and projected into the null space associated with the label selected for each respective observation. A diversity rank is determined for each respective observation based on minimum computed distance values. A predefined number of observations having highest values for the diversity rank are included in labeled observations and removed from the unlabeled observations.

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GAME ENGINE AND ARTIFICIAL INTELLIGENCE ENGINE ON A CHIP

NºPublicación: EP3556444A1 23/10/2019

Solicitante:

TMRW ENTERTAINMENT EUROPE S A R L [LU]

CN_110275859_A

Resumen de: EP3556444A1

An electronic chip, a chip assembly, a computing device, and a method are described. The electronic chip comprises a plurality of processing cores and at least one hardware interface coupled to at least one of the one or more processing cores. At least one processing core implements a game engine and/or a simulation engine and at least one or more processing cores implements an artificial intelligence engine, whereby implementations are on-chip implementations in hardware by dedicated electronic circuitry. The at least one or more game and/or simulation engines performs tasks on sensory, generating data sets that are processed through machine learning algorithms by the hardwired artificial intelligence engine. The data sets processed by the hardwired artificial intelligence engine include at least contextual data and target data, wherein combining both data and processing by dedicated hardware results in enhanced machine learning processing.

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MACHINE LEARNING ARTIFICIAL INTELLIGENCE SYSTEM FOR PREDICTING POPULAR HOURS

NºPublicación: CA3040646A1 20/10/2019

Solicitante:

CAPITAL ONE SERVICES LLC [US]

CA_3002232_A1

Resumen de: CA3040646A1

A system for generating a graphical user interface in a client device. The system may include a processor in communication with the client device and a database. The processor may execute: receiving a request for occupancy information of a specified merchant; obtaining a plurality of credit card authorizations associated with the merchant; generating a posted transaction array based on the credit card authorizations; removing outlier members of the posted transaction array by applying a threshold filter; generating a transaction frequency array based on the posted transaction array, the transaction frequency array comprising weekdays and aggregated transactions associated with the weekdays; modifying the transaction frequency array by applying a transformation to the aggregated transactions; generating a smoothed array by applying a kernel density estimate to the transaction frequency array; and generating a graphical user interface displaying information in the smoothed array.

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METHOD AND APPARATUS FOR AUTOMATED DECISION MAKING

NºPublicación: US2019318254A1 17/10/2019

Solicitante:

SAMSUNG ELECTRONICS CO LTD [KR]

KR_20190087635_A

Resumen de: US2019318254A1

A method for a first electronic device comprises generating a decision-making data structure using a machine learning data structure; transmitting, to a second electronic device, the decision-making data structure; receiving, from the electronic device, result data regarding a result of performing a selected action selected from the decision-making data structure; and updating the machine learning data structure using the result data.

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LEARNING ETL RULES BY EXAMPLE

NºPublicación: US2019318272A1 17/10/2019

Solicitante:

ORACLE INT CORP [US]

WO_2019204106_A1

Resumen de: US2019318272A1

Embodiments provide systems and methods for learning extract, transform, and load mappings by example. A plurality of features can be extracted from a source schema and a target schema. Example ETL mappings can be provided to a machine learning algorithm that comprise definitions for extracting data from source tables and loading the extracted data into target tables. Using the machine learning algorithm and based on the source schema, target schema, and extracted features, one or more ETL rules can be predicted that define logic for extracting data from the source schema and loading the extracted data into the target schema. Additional ETL mappings can be generated based on the predicted ETL rules, the additional ETL mappings providing additional definitions for extracting data from the source schema and loading the extracted data into the target schema.

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MACHINE LEARNING IMPLEMENTATION FOR MULTI-ANALYTE ASSAY OF BIOLOGICAL SAMPLES

NºPublicación: WO2019200410A1 17/10/2019

Solicitante:

FREENOME HOLDINGS INC [US]

Resumen de: WO2019200410A1

Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values. The system inputs the feature vector into the machine learning model and obtains an output classification of whether the sample has a specified property.

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CLAUSE DISCOVERY FOR VALIDATION OF DOCUMENTS

NºPublicación: WO2019200014A1 17/10/2019

Solicitante:

ICERTIS INC [US]

US_10409805_B1

Resumen de: WO2019200014A1

Embodiments are directed to managing documents where clauses in a document may be identified. Evaluations of the clauses may be provided based on evaluators and machine learning (ML) models that assign each of the clauses to a category and a confidence score. Actions associated with the clauses may be monitored including updates to content of the clauses. Inconsistent evaluations associated with the clauses be identified. The ML models may be retrained based on the content of the clauses associated with the inconsistent evaluations.

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Self-Service Classification System

NºPublicación: AU2019236756A1 17/10/2019

Solicitante:

FINANCIAL & RISK ORGANISATION LTD

AU_2019236757_A1

Resumen de: AU2019236756A1

Abstract Systems, technologies and techniques for generating a customized classification model are disclosed. The system and technologies, such as 5 THOMSON REUTERS SELF-SERVICE CLASSIFICATION T M , employ part machine learning and part an user interactive approach to generate a customized classification model. The system combines a novel approach for text classification using a smaller initial set of data to initiate training, with a unique workflow and user interaction for customization.

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Self-Service Classification System

NºPublicación: AU2019236757A1 17/10/2019

Solicitante:

FINANCIAL & RISK ORGANISATION LTD

AU_2019236756_A1

Resumen de: AU2019236757A1

Abstract Systems, technologies and techniques for generating a customized classification model are disclosed. The system and technologies, such as 5 THOMSON REUTERS SELF-SERVICE CLASSIFICATION T M , employ part machine learning and part an user interactive approach to generate a customized classification model. The system combines a novel approach for text classification using a smaller initial set of data to initiate training, with a unique workflow and user interaction for customization.

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METHOD FOR MACHINE LEARNING, NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM FOR STORING PROGRAM, APPARATUS FOR MACHINE LEARNING

NºPublicación: US2019311288A1 10/10/2019

Solicitante:

FUJITSU LTD [JP]

JP_2019185244_A

Resumen de: US2019311288A1

A method for machine learning performed by a computer includes: (i) executing a first process that includes executing machine learning on weight values corresponding to multiple functions to be used to calculate similarities between items forming pairs and included in first and second data included in a teacher data item for each of the pairs of items based on the teacher data item stored in a memory; and (ii) executing a second process that includes identifying evaluation functions to be used to calculate the similarities between the items forming the pairs based on the multiple functions and the weight values corresponding to the multiple functions.

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SYSTEM AND METHOD FOR IN-SITU CLASSIFIER RETRAINING FOR MALWARE IDENTIFICATION AND MODEL HETEROGENEITY

NºPublicación: US2019311285A1 10/10/2019

Solicitante:

BLUVECTOR INC [US]

JP_2018526732_A

Resumen de: US2019311285A1

A system and method for batched, supervised, in-situ machine learning classifier retraining for malware identification and model heterogeneity. The method produces a parent classifier model in one location and providing it to one or more in-situ retraining system or systems in a different location or locations, adjudicates the class determination of the parent classifier over the plurality of the samples evaluated by the in-situ retraining system or systems, determines a minimum number of adjudicated samples required to initiate the in-situ retraining process, creates a new training and test set using samples from one or more in-situ systems, blends a feature vector representation of the in-situ training and test sets with a feature vector representation of the parent training and test sets, conducts machine learning over the blended training set, evaluates the new and parent models using the blended test set and additional unlabeled samples, and elects whether to replace the parent classifier with the retrained version.

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SYSTEMS AND METHODS FOR IMPLEMENTING AN INTELLIGENT APPLICATION PROGRAM INTERFACE FOR AN INTELLIGENT OPTIMIZATION PLATFORM

NºPublicación: US2019310898A1 10/10/2019

Solicitante:

SIGOPT INC [US]

US_2019213056_A1

Resumen de: US2019310898A1

Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the proposed hyperparameter values.

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ATTENTION FILTERING FOR MULTIPLE INSTANCE LEARNING

NºPublicación: WO2019186198A1 03/10/2019

Solicitante:

BENEVOLENTAI TECH LIMITED [GB]

Resumen de: WO2019186198A1

Method(s), apparatus, and system(s) are provided for filtering a set of data, the set of data comprising multiple data instances by: receiving a set of scores for the set of data; determining attention filtering information based on prior knowledge of one or more relationships between the data instances in said set of data and calculating attention relevancy weights corresponding to the data instances and the set of scores; and providing the attention filtering information to a machine learning, ML, technique or ML model.

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SYSTEMS AND METHODS OF GENERATING DATASETS FROM HETEROGENEOUS SOURCES FOR MACHINE LEARNING

NºPublicación: US2019303719A1 03/10/2019

Solicitante:

NASDAQ INC [US]

WO_2019191542_A1

Resumen de: US2019303719A1

A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

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Machine Learning to Integrate Knowledge and Natural Language Processing

NºPublicación: US2019303441A1 03/10/2019

Solicitante:

IBM [US]

WO_2019138289_A1

Resumen de: US2019303441A1

A system, computer program product, and method are provided to automate a framework for knowledge graph based persistence of data, and to resolve temporal changes and uncertainties in the knowledge graph. Natural language understanding, together with one or more machine learning models (MLMs), is used to extract data and a data relationship from structured and/or unstructured data, create an entry in the KG and selectively store the extracted data and data relationship in the KG, assign a veracity value to the stored data, create an asset value entry in a corresponding BC ledger, and store a BC identifier with the KG entry.

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DEEP FUSION REASONING ENGINE (DFRE) FOR PRIORITIZING NETWORK MONITORING ALERTS

NºPublicación: US2019306011A1 03/10/2019

Solicitante:

CISCO TECH INC [US]

Resumen de: US2019306011A1

In one embodiment, a service that monitors a network detects a plurality of anomalies in the network. The service uses data regarding the detected anomalies as input to one or more machine learning models. The service maps, using a conceptual space, outputs of the one or more machine learning models to symbols. The service applies a symbolic reasoning engine to the symbols, to rank the anomalies. The service sends an alert for a particular one of the detected anomalies to a user interface, based on its corresponding rank.

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TRAFFIC AND GEOGRAPHY BASED COGNITIVE DISASTER RECOVERY

NºPublicación: US2019303235A1 03/10/2019

Solicitante:

IBM [US]

US_2019220342_PA

Resumen de: US2019303235A1

In a system having at least two data storage and processing sites, each capable of alternatively serving as a primary site and a backup or target site, disaster recovery migration is optimized by cognitively analyzing at least one system parameter. Using machine learning, at least one pattern of that system related parameter is predicted, and planned or unplanned migration procedures are performed based on the predicted parameter patterns. The analyzed parameter may be data traffic at the sites, and the predicted data traffic pattern is used to assign primary and backup site status to those sites. The analyzed parameter may be the occurrence of events or transactions at the sites, and the predicted event or transaction patterns may be used to determine times of disaster recovery procedure processing so as to not interrupt a critical event or transaction.

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METHOD AND APPARATUS FOR DETERMINING AN IDENTITY OF AN UNKNOWN INTERNET-OF-THINGS (IOT) DEVICE IN A COMMUNICATION NETWORK

NºPublicación: SG11201907943WA 27/09/2019

Solicitante:

UNIV SINGAPORE TECHNOLOGY & DESIGN [SG]
B G NEGEV TECHNOLOGIES AND APPLICATIONS LTD AT BEN GURION UNIVERSITY [IL]

WO_2018160136_PA

Resumen de: SG11201907943WA

Receive network traffic 711 generated by an unknown loT device 150a. 720 730 740 Extract device network behaviour 721 from the generated network traffic 711 of the unknown loT device 150a. W O 20 18/ 160 136 Al Apply a selected machine learning based classifier 731a from a set of machine learning based classifiers 731 to analyse the device network behaviour 721. Determine the iden ity of the unknown loT device 150a from a list of known loT devices 103 Figure 7 (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (43) International Publication Date 07 September 2018 (07.09.2018) WIPO I PCT omit VIII °nolo omioollm mom oimIE (10) International Publication Number WO 2018/160136 Al (51) International Patent Classification: HO4L 12/24 (2006.01) G06N 99/00 (2010.01) GOON 3/00 (2006.01) HO4L 29/00 (2006.01) GO6F 19/24 (2011.01) (21) International Application Number: PCT/SG2018/050089 (22) International Filing Date: 27 February 2018 (27.02.2018) (25) Filing Language: English (26) Publication Language: English (30) Priority Data: 10201701692Y 02 March 2017 (02.03.2017) SG (71) Applicants: SINGAPORE UNIVERSITY OF TECH- NOLOGY AND DESIGN [SG/SG]; 8 Somapah Road, Singapore 487372 (SG). B. G. NEGEV TECH- NOLOGIES AND APPLICATIONS LTD., AT BEN- GURION UNIVERSITY [IL/IL]; P.O. Box 653, Beer- Sheva 8410501 (IL). (72) Inventors: OCHOA, Martin; c/o Singapore University of Technology and Design, 8

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MACHINE LEARNING INFERENCE ROUTING

NºPublicación: US2019294983A1 26/09/2019

Solicitante:

FLYBITS INC [CA]

WO_2019178665_A1

Resumen de: US2019294983A1

According to embodiments described in the specification, an exemplary method and a system including a server is provided for performing a session handshake with an electronic device, receiving an intervention request and contextual data parameters from the electronic device, activating a subset of data sets and at least one Machine Learning (ML) container from a graph data structure maintained by the server, adjusting weight data parameters of the activated data sets, routing the activated data sets to the activated ML container or containers to generate a ML inference or inferences, and providing a notification of the result of the intervention request based on the generated ML inference or inferences.

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SELECTING HYPER PARAMETERS FOR MACHINE LEARNING ALGORITHMS BASED ON PAST TRAINING RESULTS

Nº publicación: US2019294999A1 26/09/2019

Solicitante:

GUTTMANN MOSHE [IL]

US_2019294982_A1

Resumen de: US2019294999A1

Systems and methods for selecting hyper parameters for machine learning algorithms based on past training results are provided. For example, groups of values of hyper parameters may be obtained. Further, in some examples, results of training the machine learning algorithm using different pluralities of training examples and/or the different group of values of hyper parameters may be obtained. Further, in some examples, the results and the groups of values of hyper parameters may be used to select at least one value of a hyper parameter for a prospective training of the machine learning algorithm.

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