MACHINE LEARNING

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Resultados 76 resultados LastUpdate Última actualización 17/08/2019 [18:58:00] pdf PDF

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

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Dynamic search and retrieval of questions

NºPublicación: AU2017386654A1 15/08/2019

Solicitante:

UNIVERSAL RESEARCH SOLUTIONS LLC

CA_3049088_A1

Resumen de: AU2017386654A1

A method includes actions of accessing a database storing multiple forms of a particular type that are each associated with a score. The actions include obtaining data corresponding to one or more forms from the database storing forms that includes at least (i) one or more questions, (ii) one or more answers to the one or more questions, and (iii) a score, training a machine learning model hosted by a server, wherein training the machine learning model includes: processing the data corresponding to the one or more forms from the database storing forms into a plurality of clusters, and for each cluster, identifying a subset of questions, from the predetermined number of questions, that are uniquely associated with each cluster, and generating a dynamic question identification model based on the identified subset of questions for each cluster.

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SYSTEM AND METHOD FOR CREATING BIOLOGICALLY BASED ENTERPRISE DATA GENOME TO PREDICT AND RECOMMEND ENTERPRISE PERFORMANCE

NºPublicación: US2019251475A1 15/08/2019

Solicitante:

QUANTIPLY CORP [US]

US_2016371591_A1

Resumen de: US2019251475A1

Briefly described, embodiments of the present invention pertains to a key performance indicator (KPI)-driven digital genome system or framework as well as various systems and methods of use and interaction therewith. Unlike conventional stand-alone KPI applications or pure-play centralized KPI solutions, embodiments of the present invention provide an automated way to codify the organizational objectives, goals, behavior, and motivations by continuously measuring, correlating, and discovering hidden relationships among various metrics, attributes, causal relationships, and networks display genomic findings via business applications without a priori knowledge of machine learning or statistical techniques.

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ARCHITECTURE FOR DENSE OPERATIONS IN MACHINE LEARNING INFERENCE ENGINE

NºPublicación: US2019244130A1 08/08/2019

Solicitante:

CAVIUM LLC [US]

US_2019243800_A1

Resumen de: US2019244130A1

A processing unit of an inference engine for machine learning (ML) includes a first, a second, and a third register, and a matrix multiplication block. The first register receives a first stream of data associated with a first matrix data that is read only once. The second register receives a second stream of data associated with a second matrix data that is read only once. The matrix multiplication block performs a multiplication operation based on data from the first register and the second register resulting in an output matrix. A row associated with the first matrix is maintained while rows associated with the second matrix is fed to the matrix multiplication block to perform a multiplication operation. The process is repeated for each row of the first matrix. The third register receives the output matrix from the matrix multiplication block and stores the output matrix.

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USING META-LEARNING FOR AUTOMATIC GRADIENT-BASED HYPERPARAMETER OPTIMIZATION FOR MACHINE LEARNING AND DEEP LEARNING MODELS

NºPublicación: US2019244139A1 08/08/2019

Solicitante:

ORACLE INT CORP [US]

Resumen de: US2019244139A1

Techniques are provided herein for optimal initialization of value ranges of machine learning algorithm hyperparameters and other predictions based on dataset meta-features. In an embodiment for each particular hyperparameter of a machine learning algorithm, a computer invokes, based on an inference dataset, a distinct trained metamodel for the particular hyperparameter to detect an improved subrange of possible values for the particular hyperparameter. The machine learning algorithm is configured based on the improved subranges of possible values for the hyperparameters. The machine learning algorithm is invoked to obtain a result. In an embodiment, a gradient-based search space reduction (GSSR) finds an optimal value within the improved subrange of values for the particular hyperparameter. In an embodiment, the metamodel is trained based on performance data from exploratory sampling of configuration hyperspace, such as by GSSR. In various embodiments, other values are optimized or intelligently predicted based on additional trainable metamodels.

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ARCHITECTURE FOR IRREGULAR OPERATIONS IN MACHINE LEARNING INFFERENCE ENGINE

NºPublicación: US2019243871A1 08/08/2019

Solicitante:

CAVIUM LLC [US]

US_2019243800_A1

Resumen de: US2019243871A1

A processing unit of an inference engine for machine learning (ML) includes a first data load steamer, a second data load streamer, an operator component, and a store streamer. The first data load streamer streams a first data stream from an on-chip memory (OCM) to the operator component. The second data load streamer streams a second data stream from the OCM to the operator component. The operator component performs a matrix operation on the first data stream and the second data stream. The store streamer receives a data output stream from the operator component and to store the data output stream in a buffer.

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METHOD AND SYSTEM FOR APPLYING MACHINE LEARNING APPROACH TO ROUTING WEBPAGE TRAFFIC BASED ON VISITOR ATTRIBUTES

NºPublicación: US2019244131A1 08/08/2019

Solicitante:

UNBOUNCE MARKETING SOLUTIONS INCORPORATED [CA]

Resumen de: US2019244131A1

The present invention is a cloud-based machine learning method and system that utilizes the attributes and past performance statistics of visitors to a set of webpage variants to predict performance statistics for incoming website visitors with respect to the webpage variants, and uses such predicted performance statistics to direct such incoming website visitors; and which learns from the performance of each directed website visitor by refining the past performance statistics to take into account such performance and the attributes of each directed website visitor, all in order to optimize future performance statistics for the set of webpage variants.

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ARRAY-BASED INFERENCE ENGINE FOR MACHINE LEARNING

NºPublicación: US2019243800A1 08/08/2019

Solicitante:

CAVIUM LLC [US]

US_2019244141_A1

Resumen de: US2019243800A1

An array-based inference engine includes a plurality of processing tiles arranged in a two-dimensional array of a plurality of rows and a plurality of columns. Each processing tile comprises at least one or more of an on-chip memory (OCM) configured to load and maintain data from the input data stream for local access by components in the processing tile and further configured to maintain and output result of the ML operation performed by the processing tile as an output data stream. The array includes a first processing unit (POD) configured to perform a dense and/or regular computation task of the ML operation on the data in the OCM. The array also includes a second processing unit/element (PE) configured to perform a sparse and/or irregular computation task of the ML operation on the data in the OCM and/or from the POD.

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SYSTEMS AND METHODS FOR PROGRAMMABLE HARDWARE ARCHITECTURE FOR MACHINE LEARNING

NºPublicación: US2019244141A1 08/08/2019

Solicitante:

CAVIUM LLC [US]

US_2019243800_A1

Resumen de: US2019244141A1

A programmable hardware architecture for machine learning (ML) is proposed, which includes at least a host, a memory, a core, a data streaming engine, a instruction-streaming engine, and an interference engine. The core interprets a plurality of ML commands for a ML operation and/or data received from the host and coordinate activities of the engines based on the data in the received ML commands. The instruction-streaming engine translates the ML commands received from the core and provide a set of programming instructions to the data streaming engine and the inference engines based on the translated parameters. The data steaming engine sends one or more data streams to the inference engine in response to the received programming instructions. The inference engine then processes the data streams received from the data stream engine according to the programming instructions received from the instruction-streaming engine.

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ARCHITECTURE OF CROSSBAR OF INFERENCE ENGINE

NºPublicación: US2019244118A1 08/08/2019

Solicitante:

CAVIUM LLC [US]

US_2019243800_A1

Resumen de: US2019244118A1

A programmable hardware system for machine learning (ML) includes a core and an inference engine. The core receives commands from a host. The commands are in a first instruction set architecture (ISA) format. The core divides the commands into a first set for performance-critical operations, in the first ISA format, and a second set of performance non-critical operations, in the first ISA format. The core executes the second set to perform the performance non-critical operations of the ML operations and streams the first set to inference engine. The inference engine generates a stream of the first set of commands in a second ISA format based on the first set of commands in the first ISA format. The first set of commands in the second ISA format programs components within the inference engine to execute the ML operations to infer data.

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SINGLE INSTRUCTION SET ARCHITECTURE (ISA) FORMAT FOR MULTIPLE ISAS IN MACHINE LEARNING INFERENCE ENGINE

NºPublicación: US2019243653A1 08/08/2019

Solicitante:

CAVIUM LLC [US]

US_2019243800_A1

Resumen de: US2019243653A1

A programmable hardware system for machine learning (ML) includes a core and an inference engine. The core receives commands from a host. The commands are in a first instruction set architecture (ISA) format. The core divides the commands into a first set for performance-critical operations, in the first ISA format, and a second set of performance non-critical operations, in the first ISA format. The core executes the second set to perform the performance non-critical operations of the ML operations and streams the first set to inference engine. The inference engine generates a stream of the first set of commands in a second ISA format based on the first set of commands in the first ISA format. The first set of commands in the second ISA format programs components within the inference engine to execute the ML operations to infer data.

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STREAMING ENGINE FOR MACHINE LEARNING ARCHITECTURE

NºPublicación: US2019244117A1 08/08/2019

Solicitante:

CAVIUM LLC [US]

US_2019243800_A1

Resumen de: US2019244117A1

A programmable hardware system for machine learning (ML) includes a core and a streaming engine. The core receives a plurality of commands and a plurality of data from a host to be analyzed and inferred via machine learning. The core transmits a first subset of commands of the plurality of commands that is performance-critical operations and associated data thereof of the plurality of data for efficient processing thereof. The first subset of commands and the associated data are passed through via a function call. The streaming engine is coupled to the core and receives the first subset of commands and the associated data from the core. The streaming engine streams a second subset of commands of the first subset of commands and its associated data to an inference engine by executing a single instruction.

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PERSONALIZED AUTO-TRIAGE OF COMMUNICATIONS

NºPublicación: US2019236486A1 01/08/2019

Solicitante:

IBM [US]

Resumen de: US2019236486A1

One embodiment provides a method comprising extracting natural language content from a piece of communication for a user, generating a representation of the piece of communication based on the natural language content extracted, and utilizing a global deep learning model and a personalized learning model for the user to assign a priority label to the piece of communication based on the representation and user behavioral information associated with recent conversations of the user. Another embodiment provides a method comprising, for each piece of communication of a set of multiple pieces of communication for multiple users, extracting natural language content from the piece communication and generating a representation of the piece of communication based on the natural language extracted, and training a deep learning neural network to predict a degree of priority of a subsequent piece of communication based on each representation generated.

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CLASSIFICATION OF PIPING AND INSTRUMENTAL DIAGRAM INFORMATION USING MACHINE-LEARNING

NºPublicación: US2019236352A1 01/08/2019

Solicitante:

CHEVRON USA INC [US]

WO_2019055849_A1

Resumen de: US2019236352A1

Systems and methods for identifying patterns of symbols in standardized system diagrams are disclosed. Disclosed implementations obtain or synthetically generate a symbol recognition training data set including multiple training images, generate a symbol recognition model based on the symbol recognition training data set, obtain an image comprising a pattern of symbols, group symbols into process loops based on the logical relationships captured by process loop identification algorithm, apply a character classification model to image contours to identify the characters and group characters into tags via hierarchical clustering, and store the identified tags, symbols and identified process loops in a relational database.

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METHODS AND APPARATUS FOR DETECTION OF MALICIOUS DOCUMENTS USING MACHINE LEARNING

NºPublicación: WO2019145912A1 01/08/2019

Solicitante:

SOPHOS LTD [GB]

US_2019236273_A1

Resumen de: WO2019145912A1

An apparatus for detecting malicious files includes a memory and a processor communicatively coupled to the memory. The processor receives multiple potentially malicious files. A first potentially malicious file has a first file format, and a second potentially- malicious file has a second file format different than the first file format. The processor extracts a first set of strings from the first potentially malicious file, and extracts a second set of strings from the second potentially malicious file. First and second feature vectors are defined based on lengths of each string from the associated set of strings. The processor provides the first feature vector as an input to a machine learning model to produce a maliciousness classification of the first potentially malicious file, and provides the second feature vector as an input to the machine learning model to produce a maliciousness classification of the second potentially malicious file.

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METHODS AND APPARATUS FOR DETECTION OF MALICIOUS DOCUMENTS USING MACHINE LEARNING

NºPublicación: US2019236273A1 01/08/2019

Solicitante:

SOPHOS LTD [GB]

WO_2019145912_A1

Resumen de: US2019236273A1

An apparatus for detecting malicious files includes a memory and a processor communicatively coupled to the memory. The processor receives multiple potentially malicious files. A first potentially malicious file has a first file format, and a second potentially malicious file has a second file format different than the first file format. The processor extracts a first set of strings from the first potentially malicious file, and extracts a second set of strings from the second potentially malicious file. First and second feature vectors are defined based on lengths of each string from the associated set of strings. The processor provides the first feature vector as an input to a machine learning model to produce a maliciousness classification of the first potentially malicious file, and provides the second feature vector as an input to the machine learning model to produce a maliciousness classification of the second potentially malicious file.

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Activation of Ancillary Sensor Systems Based on Triggers from a Wearable Gesture Sensing Device

NºPublicación: US2019236465A1 01/08/2019

Solicitante:

KLUE INC [US]

Resumen de: US2019236465A1

An event detection system includes sensors to detect movement and other physical inputs related to a user, which the event detection system can process to identify gestures of the user, and possibly also determine, using historical data, machine learning, rule sets, or other techniques for processing data to derive an inferred event related to the user sensed by the sensors. An inferred event might be an eating event, a smoking event, a personal hygiene event, a medication related event, or some other event the user is inferred to be engaging in. When an event is inferred to have started, to be ongoing, and/or to have concluded, the event detection system can take actions related to that event, such as obtaining other information to be stored in memory in association with the data representing the event, interacting with the user to provide information or reminders or to prompt for user input, sending a message to a remote computer system, sending a message to another person, such as a friend, health care provider, first responder, or other action(s).

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OFFICE BUILDING SECURITY SYSTEM USING FIBER SENSING

NºPublicación: US2019236920A1 01/08/2019

Solicitante:

NEC LAB AMERICA INC [US]

Resumen de: US2019236920A1

A security system is provided for a building. The security system includes a fiber optic cable arranged in various locations in the building for Distributed Vibration Sensing (DVS) and Distributed Acoustic Sensing (DAS) at the various locations. The security system further includes a machine-learning-based analyzer for selectively providing any of an early warning and a prevention of various detected conditions responsive to a machine-learning-based analysis of results from the DVS and the DAS.

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DEEP LEARNING ARCHITECTURE FOR MAINTENANCE PREDICTIONS WITH MULTIPLE MODES

NºPublicación: US2019235484A1 01/08/2019

Solicitante:

HITACHI LTD [JP]

Resumen de: US2019235484A1

Example implementations described herein involve a system for maintenance predictions generated using a single deep learning architecture. The example implementations can involve managing a single deep learning architecture for three modes including a failure prediction mode, a remaining useful life (RUL) mode, and a unified mode. Each mode is associated with an objective function and a transformation function. The single deep learning architecture is applied to learn parameters for an objective function through execution of a transformation function associated with a selected mode using historical data. The learned parameters of the single deep learning architecture can be applied with streaming data from with the equipment to generate a maintenance prediction for the equipment.

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AUTOMATED DISTRIBUTION OF MODELS FOR EXECUTION ON A NON-EDGE DEVICE AND AN EDGE DEVICE

NºPublicación: WO2019143715A1 25/07/2019

Solicitante:

AMAZON TECH INC [US]

US_2019220783_A1

Resumen de: WO2019143715A1

Techniques for generating and executing an execution plan for a machine learning (ML) model using one of an edge device and a non-edge device are described. In some examples, a request for the generation of the execution plan includes at least one objective for the execution of the ML model and the execution plan is generated based at least in part on comparative execution information and network latency information.

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CODE SUGGESTION BASED ON MACHINE LEARNING

NºPublicación: WO2019143541A1 25/07/2019

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

US_2019227774_A1

Resumen de: WO2019143541A1

A code completion tool uses machine learning models to more precisely predict the likelihood of a method invocation completing a code fragment that follows one or more method invocations of a same class in a same document during program development. In one aspect, the machine learning model is a n-order Markov chain model that is trained on features that represent characteristics of the context of method invocations of a class in commonly-used programs from a sampled population.

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DYNAMIC SELF-LEARNING SYSTEM FOR AUTOMATICALLY CREATING NEW RULES FOR DETECTING ORGANIZATIONAL FRAUD

NºPublicación: US2019228419A1 25/07/2019

Solicitante:

SURVEILLENS INC [US]

CA_3026250_A1

Resumen de: US2019228419A1

A fraud detection system that applies scoring models to process transactions by scoring them and sidelines potential fraudulent transactions is provided. Those transactions which are flagged by this first process are then further processed to reduce false positives by scoring them via a second model. Those meeting a predetermined threshold score are then sidelined for further review. This iterative process recalibrates the parameters underlying the scores over time. These parameters are fed into an algorithmic model. Those transactions sidelined after undergoing the aforementioned models are then autonomously processed by a similarity matching algorithm. In such cases, where a transaction has been manually cleared as a false positive previously, similar transactions are given the benefit of the prior clearance. Less benefit is accorded to similar transactions with the passage of time. The fraud detection system predicts the probability of high risk fraudulent transactions. Models are created using supervised machine learning.

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ELECTRONIC APPARATUS, METHOD AND SYSTEM FOR PROVIDING CONTENT INFORMATION, AND COMPUTER READABLE MEDIUM

NºPublicación: US2019228339A1 25/07/2019

Solicitante:

SAMSUNG ELECTRONICS CO LTD [KR]

Resumen de: US2019228339A1

The disclosure relates to an application of an artificial intelligence (AI) system utilizing a machine learning algorithm. An electronic apparatus is provided. The electronic apparatus includes: a display; a storage storing a content and metadata for the content therein; and a processor configured to control the display to display information indicating a degree to which an artificial intelligence model contributes to generation of the content based on the metadata.

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DEEP LEARNING DATA MANIPULATION FOR MULTI-VARIABLE DATA PROVIDERS

NºPublicación: US2019228326A1 25/07/2019

Solicitante:

INTEL CORP [US]

Resumen de: US2019228326A1

The disclosure is generally directed to systems in which numerous devices arranged to provide data are deployed. The system includes a source processing device arranged to received data from the data provider devices. The source processing data is arranged to process and/or store all or a part of the data based on whether the part of the data can be used to infer the rest of the data. The received data can be identified as either prediction data or response data. A data processing model can be used to generate inferred response data from the prediction data. Where the inferred response data is within an error threshold of the response data, then the prediction data can be stored. As such, the response data can be reproduced using the data processing model.

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FLUID ANALYSIS AND MONITORING USING OPTICAL SPECTROSCOPY

NºPublicación: US2019226947A1 25/07/2019

Solicitante:

VIRTUAL FLUID MONITORING SERVICES LLC [US]

US_2019101473_A1

Resumen de: US2019226947A1

Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

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REAL-TIME ADAPTIVE CONTROL OF ADDITIVE MANUFACTURING PROCESSES USING MACHINE LEARNING

Nº publicación: US2019227525A1 25/07/2019

Solicitante:

RELATIVITY SPACE INC [US]

US_2018341248_A1

Resumen de: US2019227525A1

Disclosed herein are machine learning-based methods and systems for automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.

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