Abstract: Described herein are embodiments for machine-generating and naming ontologies for for-sale items. A neural network may be used to train information describing for-sale items into feature vectors that describe the for-sale items. These feature vectors may be sorted into clusters based on their relative proximity using clustering algorithms. The resulting clusters may be sub-divided into smaller clusters depending on the precision used in the clustering algorithm. The set of clusters may form a hierarchical data structure where each level has clusters determined at a certain precision and each lower level sub-divides those clusters. The clusters may be named based on the most salient facets that describe the for-sale items in the clusters.

Abstract: Disclosed herein are system, computer-readable storage medium, and method embodiments of automatic ontology generation by embedding representations. A system including at least one processor may be configured to receive a vectorized feature set derived from an embedding and including first and second features, and provide the vectorized feature set to a fuser set including first and second fusers. The system may be configured to generate a representation from the fuser set based on the first and second features, and derive tasks based on the representation, assigning to the tasks respective qualifier sets including a weight value, a loss function, and a feedforward function. The system may be configured to compute respective weighted losses for the tasks, based on the respective qualifier sets, and output a data model based on backpropagating the respective weighted losses through the fuser set, the vectorized feature set, the embedding, or a combination thereof.

Abstract: A method, an electronic device, and computer readable medium is provided. The method includes identifying a set of observable features associated with one or more users. The method also includes generating latent features from the set of observable features. The method additionally includes sorting the latent features into one or more clusters. Each of the one or more clusters represents verbal utterances of a group of users that share a portion of the latent features. The method further includes generating a language model that corresponds to a specific cluster of the one or more clusters. The language model represents a probability ranking of the verbal utterances that are associated with the group of users of the specific cluster.

Abstract: A method, an electronic device, and computer readable medium is provided. The method includes identifying a set of observable features associated with one or more users. The method also includes generating latent features from the set of observable features. The method additionally includes sorting the latent features into one or more clusters. Each of the one or more clusters represents verbal utterances of a group of users that share a portion of the latent features. The method further includes generating a language model that corresponds to a specific cluster of the one or more clusters. The language model represents a probability ranking of the verbal utterances that are associated with the group of users of the specific cluster.

Abstract: A method includes receiving a driver model for a device. The driver model includes a list of variables associated with the device and one or more characteristics of the variables. The method includes determining whether the driver model is format-compliant and validating syntax of the driver model based at least partially on a driver template that is accessible to a third party. In response to the driver model being format-compliant and the syntax being valid, the method includes generating a verified file that is representative of the driver model. The verified file is formatted to dynamically load into a device application module during operation and to dynamically support the device. The method includes communicating the verified file to a user apparatus and adding an integrity check value thereto. In response to the driver model being format-noncompliant or the syntax being invalid, the method includes communicating an error message.

Abstract: A method of electricity load disaggregation may include identifying a first monitored appliance in an electrical network based on ON/OFF status information. The method may further include identifying a period of time during which the first monitored appliance is on. The method may also include determining whether a second monitored appliance is on during the period of time. The method may include allocating a first load of an aggregate load associated with the period of time to the first monitored appliance in response to determining that the second monitored appliance is not on during the period of time, or in response to determining that the second monitored appliance is on during the period of time, allocating a second load of the aggregate load to the first monitored appliance and allocating a third load of the aggregate load to the second monitored appliance.

Abstract: A method includes receiving a driver model for a device. The driver model includes a list of variables associated with the device and one or more characteristics of the variables. The method includes determining whether the driver model is format-compliant and validating syntax of the driver model based at least partially on a driver template that is accessible to a third party. In response to the driver model being format-compliant and the syntax being valid, the method includes generating a verified file that is representative of the driver model. The verified file is formatted to dynamically load into a device application module during operation and to dynamically support the device. The method includes communicating the verified file to a user apparatus and adding an integrity check value thereto. In response to the driver model being format-noncompliant or the syntax being invalid, the method includes communicating an error message.

Abstract: A method of constructing Internet of Things (JOT) device configurations includes receiving a solution template selection for a device configuration for a particular automated interaction between two or more IOT devices. The method includes determining whether a complete solution template for the device configuration is selected. If so, the method includes deploying the device configuration. If not, the method includes receiving IOT device selections, accessing device capabilities from an IOT database of the selected IOT devices, configuring a network connection between the selected IOT devices, simulating a device configuration using the device capabilities accessed from the IOT database, and determining whether the device configuration is operational based on the simulation. When the device configuration is not operational, the method includes reconfiguring the device configuration to include a replacement IOT device.