MACHINE LEARNING

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Resultados 48 resultados LastUpdate Última actualización 13/10/2019 [18:05:00] pdf PDF xls XLS




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



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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]

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]

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 OF GENERATING DATASETS FROM HETEROGENEOUS SOURCES FOR MACHINE LEARNING

NºPublicación: US2019303719A1 03/10/2019

Solicitante:
NASDAQ INC [US]

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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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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TRAFFIC AND GEOGRAPHY BASED COGNITIVE DISASTER RECOVERY

NºPublicación: US2019303235A1 03/10/2019

Solicitante:
IBM [US]

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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Machine Learning to Integrate Knowledge and Natural Language Processing

NºPublicación: US2019303441A1 03/10/2019

Solicitante:
IBM [US]

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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SYSTEM AND METHOD FOR USING A KNOWLEDGE REPRESENTATION WITH A MACHINE LEARNING CLASSIFIER

NºPublicación: EP3545425A1 02/10/2019

Solicitante:
PRIMAL FUSION INC [CA]

Resumen de: WO2018094496A1

Systems and methods are provided for generating training data for a machine-learning classifier. A knowledge representation synthesized based on an object of interest is used to assign labels to content items. The labeled content items can be used as training data for training a machine learning classifier. The labeled content items can also be used as validation data for the classifier.



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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]

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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SYSTEMS AND METHODS FOR INTELLIGENTLY CURATING MACHINE LEARNING TRAINING DATA AND IMPROVING MACHINE LEARNING MODEL PERFORMANCE

NºPublicación: US2019294925A1 26/09/2019

Solicitante:
CLINC INC [US]

Resumen de: US2019294925A1

Systems and methods of intelligent formation and acquisition of machine learning training data for implementing an artificially intelligent dialogue system includes constructing a corpora of machine learning test corpus that comprise a plurality of historical queries and commands sampled from production logs of a deployed dialogue system; configuring training data sourcing parameters to source a corpora of raw machine learning training data from remote sources of machine learning training data; calculating efficacy metrics of the corpora of raw machine learning training data, wherein calculating the efficacy metrics includes calculating one or more of a coverage metric value and a diversity metric value of the corpora of raw machine learning training data; using the corpora of raw machine learning training data to train the at least one machine learning classifier if the calculated coverage metric value of the corpora of machine learning training data satisfies a minimum coverage metric threshold.



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METHODS, SYSTEMS AND DEVICES FOR MONITORING AND CONTROLLING MEDIA CONTENT USING MACHINE LEARNING

NºPublicación: US2019294997A1 26/09/2019

Solicitante:
AT & T IP I LP [US]

Resumen de: US2019294997A1

Aspects of the subject disclosure may include, for example, embodiments that comprise provisioning a target user profile and obtaining viewing history data. Further embodiments include generating a group of control rules according to the target user profile and training a machine learning application according to the viewing history data and the group of control rules. Additional embodiments include receiving a first indication that a first media content is to be presented to a target user. Also, embodiments include determining by the machine learning application, that the first media content does not conform to the group of control rules and providing a first notification that the first media content does not conform to the group of control rules. Other embodiments are disclosed.



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MACHINE LEARNING DETECTION OF UNUSED OPEN PORTS

NºPublicación: WO2019182775A1 26/09/2019

Solicitante:
MICROSOFT TECHNOLOGY LICENSING LLC [US]

Resumen de: WO2019182775A1

A security service utilizes a machine learning model to detect unused open ports. A security agent on client machines tracks the operating executables and the open ports on a machine. A machine learning model is trained for a specific port number using the more commonly-used executables that run on machines having the port opened from a large and diverse population of machines. The model is then used to determine the ports that an executable is likely to be associated with which is then used to determine if a particular machine has an unused open port.



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MACHINE LEARNING INFERENCE ROUTING

NºPublicación: US2019294983A1 26/09/2019

Solicitante:
FLYBITS INC [CA]

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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GAME PROGRAM INSPECTING SYSTEM, METHOD, PROGRAM, MACHINE LEARNING ASSIST DEVICE, AND DATA STRUCTURE

NºPublicación: WO2019181801A1 26/09/2019

Solicitante:
CYGAMES INC [JP]

Resumen de: WO2019181801A1

Provided is a system capable of inspecting a game program by deducing actions that are more likely to be executed by a user. The present invention is a system for inspecting a game program of a game which is proceeded by a user selecting a medium from an owned media group configured to include a plurality of media, and putting the medium in a game field. The system acquires a game log including media information about media included in a game condition configured to include a game field and an owned media group, creates tensor data by creating array data in which matrixes indicating information about the media included in the game condition are arrayed on a time axis on the basis of the acquired game log, generates a learning model by performing machine learning using the tensor data, deduces a medium to be selected by a user, by using the learning model, executes a game program while using the deduced medium as a medium selection made by the user, and thereby, inspects the game program.



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METHOD AND SYSTEM FOR TRAINING AND VALIDATING MACHINE LEARNING IN NETWORK ENVIRONMENTS

NºPublicación: US2019294995A1 26/09/2019

Solicitante:
TELEFONICA SA [ES]

Resumen de: US2019294995A1

A system and method for training and validating ML algorithms in real networks, including: generating synthetic traffic and receiving it along with real traffic; aggregating the received traffic into network flows by using metadata and transforming them to generate a first dataset readable by the ML algorithm, comprising features defined by the metadata; labelling the traffic and selecting a subset of the features from the labelled dataset used in an iterative training to generate a trained model; filtering out a part of real traffic to obtain a second labelled dataset; and selecting a subset of features from the second labelled dataset used for validating the trained model by comparing predicted results for the trained model and the labels; repeating the steps with a different subset of features to generate another trained model until results are positive in terms of precision or accuracy.



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EVOLVED MACHINE LEARNING MODELS

NºPublicación: US2019295000A1 26/09/2019

Solicitante:
H2O AI INC [US]

Resumen de: US2019295000A1

A plurality of initial machine learning models are determined based on a plurality of original features. The plurality of initial machine learning models are filtered by selecting a subset of the initial machine learning models as one or more surviving machine learning models. One or more evolved machine learning models are generated. At least one of the evolved machine learning models is based at least in part on one or more new features, which are based at least in part on a transformation of at least one of features of the one or more surviving machine learning models. Corresponding validation scores associated with the one or more evolved machine learning models and corresponding validation scores associated with the one or more surviving machine learning models are compared. At least one of the one or more evolved machine learning models or the one or more surviving machine learning models are selected as one or more new selected surviving machine learning models.



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HYBRID LEARNING FOR INTELLIGENT INSTRUMENTS

NºPublicación: US2019294992A1 26/09/2019

Solicitante:
ZHU YUDONG [US]

Resumen de: US2019294992A1

Numerous instruments make inferences by fitting sensor data to knowledge that characterizes the physical world, where the knowledge result from human learning. The present intelligent instruments integrate machine learning elements with human learning elements so that they work in a hybrid fashion to improve the performance of inference making. In some embodiments human learning elements, e.g., scientific models, participate directly in the construction of some of the machine learning elements. In some embodiments machine learning elements help assess and drive the refinement of the inferences. The integration comprises training data synthesis, loss and gradient calculations, and autonomous adaptation.



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

NºPublicación: US2019294999A1 26/09/2019

Solicitante:
GUTTMANN MOSHE [IL]

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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SYSTEMS AND METHODS FOR SCENARIO SIMULATION

NºPublicación: US2019294633A1 26/09/2019

Solicitante:
GOLDMAN SACHS & CO LLC [US]

Resumen de: US2019294633A1

Systems and methods for automatically generating scenarios and user interface elements representing valuations of instruments under the scenarios are described. The systems and methods use expert polling systems and machine learning rules to generate tree data storage structures representing different scenarios of macro factors for outcomes of events. Machine implemented interfaces for expert polling, presentment of scenarios, and interaction with scenarios are also provided.



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METHODS AND SYSTEMS FOR TRANSFORMING COMPUTING ANALYTICS FRAMEWORKS INTO CROSS-PLATFORM REAL-TIME DECISION-MAKING SYSTEMS THAT OPTIMIZE CONFIGURABLE GOAL METRICS

NºPublicación: US2019287002A1 19/09/2019

Solicitante:
SCALED INFERENCE INC [US]

Resumen de: US2019287002A1

Systems described herein provide structures and functionality for transforming passive analytics systems into systems that can actively modify software behavior based on analytic data to improve software performance relative to configurable goal metrics. An example method generally includes generating a decision-making policy representing logic learned by a machine-learning model from time-series data collected for sessions of a software application; deploying the decision-making policy; receiving a decision-making request originating from the software application including a consumer identifier and a decision-point type; retrieving time-series data in a session associated with the consumer identifier; selecting actions for the software application to perform by comparing the time-series data and the event type to the decision-making policy; sending an indication of the selected actions in response to the decision-making request; and updating data in the session associated with the consumer identifier to reflect the decision-point event and the one or more selected actions.



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MACHINE LEARNING MODEL TRAINING

NºPublicación: US2019286943A1 19/09/2019

Solicitante:
PINTEREST INC [US]

Resumen de: US2019286943A1

Systems and methods for efficiently training a machine learning model are presented. More particularly, using information regarding the relevant neighborhoods of target nodes within a body of training data, the training data can be organized such that the initial state of the training data is relatively easy for a machine learning model to differentiate. Once trained on the initial training data, the training data is then updated such that differentiating between a matching and a non-matching node is more difficult. Indeed, by iteratively updating the difficulty of the training data and then training the machine learning model on the updated training data, the speed that the machine learning model reaches a desired level of accuracy is significantly improved, resulting in reduced time and effort in training the machine learning model.



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PART SUPPLY AMOUNT ESTIMATING DEVICE AND MACHINE LEARNING DEVICE

NºPublicación: US2019287189A1 19/09/2019

Solicitante:
FANUC CORP [JP]

Resumen de: US2019287189A1

A machine learning device included in a part supply amount estimating device which can estimate an appropriate number of parts necessary to manufacture a manufacturing product includes: a state observing unit that observes manufacturing product data and manufacturing environment data as a state variable, the manufacturing product data indicating information related to the manufacturing product, the manufacturing environment data indicating information related to machining environment for manufacturing the manufacturing product, and the state variable indicating a current state of environment; a label data obtaining unit that obtains the part margin necessary to manufacture the manufacturing product as label data; and a learning unit that associates and learns the information related to the manufacturing product and the information related to the machining environment for manufacturing the manufacturing product, and the part margin necessary to manufacture the manufacturing product by using the state variable and the label data.



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SYSTEMS AND METHODS FOR PROVIDING AUTOMATED NATURAL LANGUAGE DIALOGUE WITH CUSTOMERS

NºPublicación: US2019287512A1 19/09/2019

Solicitante:
CAPITAL ONE SERVICES LLC [US]

Resumen de: US2019287512A1

A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of providing automated natural dialogue with a customer. The system may generate one or more events and commands temporarily stored in queues to be processed by one or more of a dialogue management device, an API server, and an NLP device. The dialogue management device may create adaptive responses to customer communications using a customer context, a rules-based platform, and a trained machine learning model.



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MACHINE LEARNING DEVICE, SERVO CONTROL DEVICE, SERVO CONTROL SYSTEM, AND MACHINE LEARNING METHOD

NºPublicación: US2019287007A1 19/09/2019

Solicitante:
FANUC CORP [JP]

Resumen de: US2019287007A1

A machine learning device performs machine learning with respect to a servo control device including at least two feedforward calculation units among a position feedforward calculation unit configured to calculate a position feedforward term on the basis of a position command, a velocity feedforward calculation unit configured to calculate a velocity feedforward term on the basis of a position command, and a current feedforward calculation unit configured to calculate a current feedforward term on the basis of a position command. Machine learning related to the coefficients of a transfer function of one feedforward calculation unit among the at least two feedforward calculation units is performed earlier than machine learning related to the coefficients of a transfer function of the other feedforward calculation unit.



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MACHINE LEARNING DETECTION OF UNUSED OPEN PORTS

NºPublicación: US2019286826A1 19/09/2019

Solicitante:
MICROSOFT TECHNOLOGY LICENSING LLC [US]

Resumen de: US2019286826A1

A security service utilizes a machine learning model to detect unused open ports. A security agent on client machines tracks the operating executables and the open ports on a machine. A machine learning model is trained for a specific port number using the more commonly-used executables that run on machines having the port opened from a large and diverse population of machines. The model is then used to determine the ports that an executable is likely to be associated with which is then used to determine if a particular machine has an unused open port.



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ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING PLATFORM FOR IDENTIFYING GENETIC AND GENOMIC TESTS

Nº publicación: US2019287681A1 19/09/2019

Solicitante:
GENOMESMART INC [US]

Resumen de: US2019287681A1

Improvements in genetic test identification are accomplished using a method and accompanying system that receives first input comprising recommendations for genetic tests given a plurality of different combinations of health-related variables and second input comprising information associated with available genetic tests. Based thereon, a set of rules comprising a plurality of mappings between the different combinations of health-related variables and the available genetic tests is generated. A classifier is trained using the set of rules as training data. Third input comprising a first combination of health-related variables is received, where the first combination of health-related variables is not included in the plurality of different combinations of health-related variables, provides the first combination of health-related variables as input to the classifier, and receives as output from the classifier, based on the input to the classifier, one or more recommended genetic tests from the available genetic tests.


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