Accenture (ACN) USPTO patents

This disclosure is directed to a computer system and method to assist in identifying data feature intersection or overlap between private datasets without revealing any specific data items or data features in the datasets.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed.

Aspects of the present disclosure provide an environmental probe configured to be at least partially inserted into the ground at a location and to provide defensible space monitoring and maintenance for a home or other structure.

In some examples, migration context and flow graph based migration control may include ascertaining an application that is to be migrated from a physical environment to a cloud environment, and determining a migration issue associated with the migration of the application.

In some implementations, a self-cleaning seat includes a cover layer with a photocatalyst; a light-emitting fiber layer including one or more light sources; a light-diffusing spacer configured to diffuse light emitted from the light-emitting fiber layer and positioned between the cover layer and the light-emitting fiber layer; and a triggering mechanism that activates a cleaning cycle by activating the one or more light sources.

The present disclosure describes a method of controlling a manufacturing system using multivariate time series, the method comprising: recording data from one or more devices in the manufacturing system; storing the recorded data in a data storage as a plurality of time series, wherein each time series has a first recorded value corresponding to a first time and a final recorded value corresponding to an end of the time series; interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, wherein the missing values fall between the first and end time of the respective time series; storing the interpolated values as prediction data in a prediction storage, wherein the interpolated values include the uncertainty of each interpolated value; loading the recorded data that fall within a second time window from the data storage; loading prediction data from the prediction storage that fall within the second time window and for which no recorded data are available; optimizing the parameters of the Bayesian model using the loaded recorded data and the prediction data; predicting, using the Bayesian model, values for each of the time series for which loaded recorded and prediction data are not available; storing the predicted values as prediction data in the prediction storage, wherein the prediction values include the uncertainty of each prediction value; and adjusting one or more of the devices that generate the recorded data based on the prediction data within the second time window.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are described that enable identifying an insurance information associated with a property, retrieving one or more compliance rules related to the insurance information, automatically determining, based on the one or more compliance rules, whether the insurance information has a compliant status or a non-compliant status, in response to determining that the insurance information has the non-compliant status, processing the insurance information, based on one or more exception rules, to generate an exception waiver including a plurality of waiver parameters, applying the plurality of waiver parameters to a waiver model to determine a waiver confidence score, and based on the waiver confidence score selectively generate an insurance certificate for the property.

Systems and methods for facilitating candidate acquisition are disclosed.

Methods, systems, and apparatus for implementing a hexadecimal to quantum computation translation.

A material failure forecasting system accesses historical failure data to forecasts future failures.

Examples of artificial intelligence-based reasoning explanation are described.

A device may receive workflow data identifying an automation request, and may request jobs for the workflow data.

Implementations are directed to design advisor platform uses machine learning (ML) models (e.g., deep learning models, such as convolutional neural networks (CNNs)) to compare an input design with a set of reference designs and provide evaluation and recommendation of the input design on-the-fly (i.e., in real-time).

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters.

Example computer-implemented methods, media, and systems for configuring an energy consuming system are described.

A method and system for cloud based service provider performance evaluation are disclosed.

A computing architecture providing for rapid analysis and control of an environment via edge computing nodes is disclosed.

A method and system for cloud distributed hybrid data storage and normalization are disclosed.

Implementations include providing a robotic model representative of a robotic system, determining baseline data by executing a simulation using the robotic model and a simulation world that represents the environment, generating, by a perturbation system, perturbations to be included in a set of test cases, each test case including at least one perturbation to at least one object relative to a baseline of the at least one object determined from the baseline data, determining test result data for the set of test cases by modifying the at least one object in view of the at least one perturbation to provide a modified object and executing the simulation with the modified object, and displaying a graph representative of the at least one object based on the baseline data and test result data for a test case, the graph depicting variance of the object during the simulation using the test case.

Systems and methods for establishing data augmentation and enrichment within a data repository are disclosed, including a plug-in including an information relation charting engine, an analytical metric study engine, a derived record construction engine, and a configurational layer.