MACHINE LEARNING

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Resultados 104 resultados LastUpdate Última actualización 17/06/2019 [18:23:00] pdf PDF

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

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SYSTEMS AND METHODS FOR GENERATING AND UPDATING MACHINE HYBRID DEEP LEARNING MODELS

NºPublicación: US2019180196A1 13/06/2019

Solicitante:

CONVERSICA INC [US]

Resumen de: US2019180196A1

Systems and methods for improvements in AI model learning and updating are provided. The model updating may reuse existing business conversations as the training data set. Features within the dataset may be defined and extracted. Models may be selected and parameters for the models defined. Within a distributed computing setting the parameters may be optimized, and the models deployed. The training data may be augmented over time to improve the models. Deep learning models may be employed to improve system accuracy, as can active learning techniques. The models developed and updated may be employed by a response system generally, or may function to enable specific types of AI systems. One such a system may be an AI assistant that is designed to take use cases and objectives, and execute tasks until the objectives are met. Another system capable of leveraging the models includes an automated question answering system utilizing approved answers. Yet another system for utilizing these various classification models is an intent based classification system for action determination. Lastly, it should be noted that any of the above systems may be further enhanced by enabling multiple language analysis.

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GUIDING MACHINE LEARNING MODELS AND RELATED COMPONENTS

NºPublicación: US2019180199A1 13/06/2019

Solicitante:

IBM [US]

Resumen de: US2019180199A1

Techniques facilitating guiding machine learning models and related components are provided. In one example, a computer-implemented method comprises identifying, by a device operatively coupled to a processor, a set of models, wherein the set of models includes respective model components; determining, by the device, one or more model relations among the respective model components, wherein the one or more model relations respectively comprise a vector of component relations between respective pairwise ones of the model components; and suggesting, by the device, a subset of the set of models based on a mapping of the component relations.

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OPTIMIZING EMERGENCY DEPARTMENT RESOURCE MANAGEMENT

NºPublicación: US2019180868A1 13/06/2019

Solicitante:

UBQ INC [US]

Resumen de: US2019180868A1

The disclosed embodiments disclose techniques for optimizing emergency department resource management. During operation, a system receives a set of parameters that is associated with a patient entering an emergency department. The system analyzes the set of parameters in a machine learning module to determine (1) a calculated acuity score that indicates an estimated severity of illness for the patient and (2) a set of workload predictions that predict a set of resources that will be needed to treat the patient in the emergency department. The system then uses the acuity score and the workload predictions to assign a set of predicted tasks that are associated with treating the patient into the work queues of the emergency department.

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DATA TRANSFORMATION OF PERFORMANCE STATISTICS AND TICKET INFORMATION FOR NETWORK DEVICES FOR USE IN MACHINE LEARNING MODELS

NºPublicación: US2019182120A1 13/06/2019

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

Resumen de: US2019182120A1

A device may receive one or more data models that have been trained using a first set of values that are in a format capable of being processed by the one or more data models. The first set of values may be associated with a set of historical network performance indicators relating to a set of network devices. The device may receive network data that includes network ticket information and performance statistics for the one or more network devices. The device may determine a set of network performance indicators relating to the one or more network devices. The device may convert the set of network performance indicators into a second set of values that are in the format capable of being processed by the one or more data models. The device may use the second set of values to generate one or more recommendations associated with improving network performance.

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AUTO THROTTLING OF INPUT DATA AND DATA EXECUTION USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

NºPublicación: US2019179647A1 13/06/2019

Solicitante:

GEN ELECTRIC [US]

Resumen de: US2019179647A1

A method is for setting processing parameters for execution of an application program that runs on a multi-processor data processing system. Reinforcement learning is applied to sequentially propose configuration states for executing the application program. Each of the configuration states consists of: (a) a number of instances of the application program; (b) a number of dedicated processors; and (c) a quantity of dedicated memory. The reinforcement learning is allowed to reach an optimal configuration state. The application program is executed in the data processing system in accordance with the optimal configuration state.

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GENERATING AND/OR UTILIZING A MACHINE LEARNING MODEL IN RESPONSE TO A SEARCH REQUEST

NºPublicación: US2019179940A1 13/06/2019

Solicitante:

GOOGLE LLC [US]

Resumen de: US2019179940A1

Implementations relate to providing, in response to a query, machine learning model output that is based on output from a trained machine learning model. The machine learning model output can include a predicted answer to the query, that is predicted based on the trained machine learning model. The machine learning model output can additionally or alternatively include an interactive interface for the trained machine learning model. Some implementations relate to generating a trained machine learning model “on the fly” based on a search query. Some implementations additionally or alternatively relate to storing, in a search index, an association of a machine learning model with a plurality of content items from resource(s) on which the machine learning model was trained.

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WAYPOINT DETECTION FOR A CONTACT CENTER ANALYSIS SYSTEM

NºPublicación: US2019180175A1 13/06/2019

Solicitante:

RAYTHEON BBN TECHNOLOGIES CORP [US]

Resumen de: US2019180175A1

A contact center analysis system can receive various types of communications from customers, such as audio from telephone calls, voicemails, or video conferences; text from speech-to-text translations, emails, live chat transcripts, text messages, and the like; and other media or multimedia. The system can segment the communication data using temporal, lexical, semantic, syntactic, prosodic, user, and/or other features of the segments. The system can cluster the segments according to one or more similarity measures of the segments. The system can use the clusters to train a machine learning classifier to identify one or more of the clusters as waypoints (e.g., portions of the communications of particular relevance to a user training the classifier). The system can automatically classify new communications using the classifier and facilitate various analyses of the communications using the waypoints.

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DATA TRANSFORMATION OF PERFORMANCE STATISTICS AND TICKET INFORMATION FOR NETWORK DEVICES FOR USE IN MACHINE LEARNING MODELS

NºPublicación: EP3496015A1 12/06/2019

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

Resumen de: US2019182120A1

A device may receive one or more data models that have been trained using a first set of values that are in a format capable of being processed by the one or more data models. The first set of values may be associated with a set of historical network performance indicators relating to a set of network devices. The device may receive network data that includes network ticket information and performance statistics for the one or more network devices. The device may determine a set of network performance indicators relating to the one or more network devices. The device may convert the set of network performance indicators into a second set of values that are in the format capable of being processed by the one or more data models. The device may use the second set of values to generate one or more recommendations associated with improving network performance.

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DISTRIBUTED MACHINE LEARNING METHOD AND SYSTEM

NºPublicación: US2019171952A1 06/06/2019

Solicitante:

TENCENT TECH SHENZHEN CO LTD [CN]

CN_108009642_A

Resumen de: US2019171952A1

A method and system for distributed machine learning and model training are disclosed. In particular, a finite asynchronous parallel training scheme is described. The finite asynchronous parallel training takes advantage of the benefits of both asynchronous parallel training and synchronous parallel training. The computation delays in various distributed computation nodes are further considered when training parameter are updated during each round of iterative training. The disclosed method and system facilities increase of model training speed and efficiency.

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Computing Architecture Deployment Configuration Recommendation Using Machine Learning

NºPublicación: US2019171948A1 06/06/2019

Solicitante:

SAP SE [DE]

Resumen de: US2019171948A1

Data is received that characterizes a software system. Thereafter, using at least one machine learning model trained using historical testing data from a plurality of training software systems, a recommended computing architecture is generated for the software system. Data can then be provided that characterizes the software system. Related apparatus, systems, techniques and articles are also described.

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SYSTEMS AND METHODS FOR ASSESSING DRUG EFFICACY

NºPublicación: WO2019109089A1 06/06/2019

Solicitante:

ILLUMINA INC [US]

Resumen de: WO2019109089A1

Provided is a computer-implemented method, including inputting to a trained machine learning classifier genomic information of a non-training subject that includes features from a tumor sample, wherein the trained machine learning classifier trained on features of tumor samples obtained from training subjects and their a responsiveness to checkpoint inhibition treatment and the machine-learning classifier is trained to predict responsiveness to the treatment, and generating a checkpoint inhibition responsiveness classification predictive of the subject's responding to the checkpoint inhibition with the trained machine-learning classifier, and reporting the checkpoint inhibition responsiveness classification using a graphical user interface. Also provided are a computer system for performing the method and a machine learning classifier trained by the method.

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INTEGRITY EVALUATION OF UNSTRUCTURED PROCESSES USING ARTIFICIAL INTELLIGENCE (AI) TECHNIQUES

NºPublicación: US2019171944A1 06/06/2019

Solicitante:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

Resumen de: US2019171944A1

A process integrity evaluation system ensures integrity of unstructured processes. The process integrity evaluation system handles structured, semi-structured, and unstructured data at massive and large scale. The system provides scalability, secure storage, indexing, knowledge storage, and visualizations of processes by information retrieval, natural language processing, cloud computing, large scale machine learning, knowledge discovery, and other artificial intelligence techniques. Self-provided data, systematically gathered data, and potentially related data from additional sources are incorporated in the process integrity evaluation system which provides the core capabilities of data integrity checking, entity extraction, entity resolution, entity categorization, entity relationship extraction, processes extraction and reconstruction based on knowledge storage, such as knowledge graphs, inference functions, and evaluation computations. After extracting and reconstructing unstructured processes successfully, machine learning functions compute an integrity assurance score, e.g., a similarity, between extracted documents and the internal records in addition to an evaluation result, which can ensure the integrity of the unstructured processes.

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ARTIFICIAL INTELLIGENCE RADIO TRANSCEIVER

NºPublicación: US2019171965A1 06/06/2019

Solicitante:

DEEPWAVE DIGITAL INC [US]

Resumen de: US2019171965A1

A software-defined radio system may include a radio frequency front end connected to a high performance computing processor comprised of a central processing unit (CPU), a graphics processing unit (GPU), and a shared memory between the CPU and GPU. The software-defined radio system may incorporate a signal processing unit between the radio frequency front end and the high performance computing processor. Additionally, the software-defined radio system may be configured to create a ring buffer in a shared memory between the CPU and GPU and directly store digital signal data in the ring buffer. The software-defined radio system may be used to implement and train machine learning algorithms and transmit digital signals.

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Data Processing System and Method For Rules-Based Inventory Qualification

NºPublicación: US2019172001A1 06/06/2019

Solicitante:

FAIR IP LLC [US]

WO_2018136536_A1

Resumen de: US2019172001A1

One embodiment comprises a rules-based data processing system comprising a data store storing a set of machine learning model-based depreciation models, each depreciation model having an associated vehicle year/make/model/trim, and payment rules to determine payment schedules for vehicles using the set of depreciation curves. The rules-based data processing system can further comprise a processor and a memory coupled to the processor storing a set of computer executable instructions. The set of computer executable instructions executable to receive a first set of inventory feed records, each inventory feed record in the first set of inventory feed records comprising a vehicle identification number (VIN), dealer price and mileage and apply a first set of filter rules to the first set of inventory feed records to identify a second set of inventory feed records corresponding to vehicles having year/make/model/trim for which a depreciation curve is stored in the data store.

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METHOD AND SYSTEM FOR GENERATING AN ENTITIES VIEW WITH RISK-LEVEL SCORING FOR PERFORMING COMPUTER SECURITY MONITORING

NºPublicación: US2019173893A1 06/06/2019

Solicitante:

SPLUNK INC [US]

US_2019158517_A1

Resumen de: US2019173893A1

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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SYSTEM AND METHOD FOR AUTOMATICALLY IMPROVING GATHERING OF DATA USING A DATA GATHERING DEVICE

NºPublicación: US2019171897A1 06/06/2019

Solicitante:

MERAI MEHDI [CA]
ELAWAD ALI [CA]

Resumen de: US2019171897A1

The generation of the at least one action, such as changing the acquisition settings, can be based on an acquisition settings adjustment model and the modification of the action can include updating the model by machine learning (ex: reinforcement learning). The updated model can be applied to subsequent iterations of data gathering.

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AUTOMATIC GENERATION OF HUMAN-UNDERSTANDABLE GEOSPATIAL DESCRIPTORS

NºPublicación: US2019171943A1 06/06/2019

Solicitante:

LYFT INC [US]

Resumen de: US2019171943A1

A disclosed method may include receiving geographic coordinates of a location at which two parties are to rendezvous, generating a human-understandable geospatial descriptor for the request location, and sending the descriptor to respective devices of the two parties for presentation to the two parties. Generating the human-understandable geospatial descriptor may include identifying a human-visible feature in the vicinity of the request location that is labeled within available map data, selecting, based on a descriptor generation model, a reference expression relative to the identified feature, and applying a grammar-based constructor to the label and the selected reference expression to form the human-understandable geospatial descriptor. The model may be tuned using machine learning. The two parties may include a ride requestor and a ride provider in a ridesharing service. The identified feature may be a point of interest, landmark, street name, intersection, marker, or structure.

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DECOMPOSING TASKS THROUGH ARTIFICIAL INTELLIGENCE CHAINING

NºPublicación: WO2019108348A1 06/06/2019

Solicitante:

B YOND INC [US]

US_2019164087_A1

Resumen de: WO2019108348A1

Embodiments relate to intelligent entities for providing information service over a network in a telecommunication system. An intelligent element framework manages intelligent entities, which are modular and trained using artificial intelligence or machine learning algorithms to perform prediction or inference for different types of applications. The intelligent entities may communicate with each other via the intelligent element framework. For example, an intelligent entity may generate an output and provide the output for use by one or more other intelligent entities. Thus, the intelligent element framework may distribute portions of tasks for information service across multiple intelligent entities chained together, for example, in a directed graph configuration.

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SYSTEM AND METHOD FOR AUTOMATICALLY IMPROVING GATHERING OF DATA USING A DATA GATHERING DEVICE

NºPublicación: CA3026684A1 06/06/2019

Solicitante:

MERAI MEHDI [CA]
ELAWAD ALI [CA]

US_2019171897_A1

Resumen de: CA3026684A1

A computer-implemented system and method for automatically improving gathering of data includes processing first input data acquired by a data gathering device to generate a first prediction and a corresponding confidence score. If the confidence score is below a threshold, then generating and applying a set of at least one action to the data gathering device, such as changing acquisition settings of the device. Second input data acquired using the data gathering device having the action applied thereto is further received and processed to generate a second prediction and corresponding confidence score. The set of action to-be-applied to the device is further modified based on the difference. The generation of the at least one action, such as changing the acquisition settings, can be based on an acquisition settings adjustment model and the modification of the action can include updating the model by machine learning (ex: reinforcement learning). The updated model can be applied to subsequent iterations of data gathering.

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SCALABLE INTEGRATED INFORMATION STRUCTURE SYSTEM

NºPublicación: US2019171951A1 06/06/2019

Solicitante:

AT & T IP I LP [US]

Resumen de: US2019171951A1

A scalable integrated information system in a network environment, the system comprising: an agent instantiated as a virtual machine or virtual network function, the agent configured to communicate with the network environment, the network environment comprising a meta data inventory; a data store comprising a central metadata repository, the central metadata repository configured to communicate with the network environment and selectively retrieve the meta data inventory, wherein the central metadata repository stores an integrated context representation comprising at least one of a real-time temporal context, a historical context, and a meta context associated with the meta data inventory; a reasoning module instantiated as a virtual machine or virtual network function and including an input configured to receive a reasoning concept; a machine learning module, instantiated as a virtual machine or virtual network function and configured to communicate with the central metadata repository to selectively retrieve the integrated context representation, wherein the machine learning module communicates with the reasoning module to develop a reasoning model configured to associate the reasoning concept with the integrated context representation; and wherein the agent communicates with the data store to retrieve the integrated context representation and communicates with the reasoning module to retrieve the reasoning model to develop an action and wherein the agent implements the a

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ON-DEVICE MACHINE LEARNING PLATFORM

NºPublicación: EP3491588A1 05/06/2019

Solicitante:

GOOGLE LLC [US]

US_2019050749_A1

Resumen de: WO2019032157A1

The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

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ON-DEVICE MACHINE LEARNING PLATFORM

NºPublicación: EP3491587A1 05/06/2019

Solicitante:

GOOGLE LLC [US]

US_2019050746_A1

Resumen de: WO2019032156A1

The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

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MACHINE LEARNING RISK DETERMINATION SYSTEM FOR TREE BASED MODELS

NºPublicación: WO2019103979A1 31/05/2019

Solicitante:

EXPERIAN INF SOLUTIONS INC [US]

US_2019156227_A1

Resumen de: WO2019103979A1

The present disclosure describes systems and methods for determining correlation codes for tree-based decisioning models. In one embodiment, a method for determining correlation codes in a tree-based decision model includes: assigning each decision node in a tree-based decision model to a correlation code; initializing a risk sum for each correlation code; calculating, for all decision nodes in the tree-based decision model, a difference in risk between child nodes and respective parent nodes; updating the risk sum for each correlation code associated with the decision node used in the decision for the node; determining the feature with the highest risk sum; and determining the correlation code associated with the determined decision node.

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MACHINE DIAGNOSIS USING MOBILE DEVICES AND CLOUD COMPUTERS

NºPublicación: WO2019103767A1 31/05/2019

Solicitante:

SIEMENS AG [DE]
SIEMENS CORP [US]

Resumen de: WO2019103767A1

A method, performed by a mobile device (101), for determining an operating state of a machine (103) includes measuring (S101) a signal of the machine (103), applying (S103) the measured signal to a machine-learned classifier or machine learning model learned on machine signals and associated operating states, generating (S105) the operating state of the machine (103) based on the application of the measured signal to the machine-learned classifier or machine learning model, and outputting the operating state of the machine (103).

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AUTOMATICALLY GENERATING VOLUME FORECASTS FOR DIFFERENT HIERARCHICAL LEVELS VIA MACHINE LEARNING MODELS

Nº publicación: WO2019104126A1 31/05/2019

Solicitante:

UNITED PARCEL SERVICE AMERICA INC [US]

US_2019156253_A1

Resumen de: WO2019104126A1

Embodiments are disclosed for autonomously generating volume forecasts. An example method includes accessing volume information units from a volume forecast data management tool. The example method further includes extracting features from volume information units, wherein the features are representative of one or more of a package received time, or package information. The features can be categorized by different hierarchical level information. The example method further includes generating, using a volume forecast learning model and the features, an output comprising a volume forecast for a particular hierarchical level. Corresponding apparatuses and non-transitory computer readable storage media are also provided.

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