Abstract: A method and system for content display. The method includes: capturing, by an imaging device, a plurality of images of an environment; generating, by a computing device, first user profiles based on appearance of users recognized in the images; detecting devices in the environment and associating the detected devices to the users to obtain associated devices that are held by the users; retrieving, by the computing device, second user profiles of the users based on identifications of the associated devices; selecting at least one content based on the first user profiles and the second user profiles; and displaying the at least one selected content.

Abstract: The present invention is generally directed to methods of disaggregating low resolution whole-house energy consumption data. In accordance with some embodiments of the present invention, methods may include steps of: receiving at a processor the low resolution whole house profile; selectively communicating with a first database including non-electrical information; selectively communicating with a second database including training data; and determining by the processor based on the low resolution whole house profile, the non-electrical information and the training data, individual appliance load profiles for one or more appliances.

Abstract: A method and system for content display. The method includes: capturing, by an imaging device, a plurality of images of an environment; generating, by a computing device, first user profiles based on appearance of users recognized in the images; detecting devices in the environment and associating the detected devices to the users to obtain associated devices that are hold by the users; retrieving, by the computing device, second user profiles of the users based on identifications of the associated devices; selecting at least one content based on the first user profiles and the second user profiles; and displaying the at least one selected content.

Abstract: A user can issue a query about a process that has a number of stages. A stage of the process is determined using the query and location data associated with the query, and a stage prediction model. A stage learning system can select a sample of query logs for a category from a database of millions or billions of users' queries. Queries can be parsed into keywords. A category can be determined from location information associated with each query and from query keywords. Queries are aligned based on location and, optionally, keywords. TF-IDF values are computed for queries and are used to determine a difference significance between aligned, adjacent queries. If aligned, adjacent queries have a substantially difference in keywords and TF-IDF, then a conversion stage is identified. Content can be presented to the user based on the category, keywords, location, and conversion stage.

Abstract: In one embodiment, a method or system generates a high resolution 3-D point cloud to operate an autonomous driving vehicle (ADV) from a low resolution 3-D point cloud and camera-captured image(s). The system receives a first image captured by a camera for a driving environment. The system receives a second image representing a first depth map of a first point cloud corresponding to the driving environment. The system determines a second depth map by applying a convolutional neural network model to the first image. The system generates a third depth map by applying a conditional random fields model to the first image, the second image and the second depth map, the third depth map having a higher resolution than the first depth map such that the third depth map represents a second point cloud perceiving the driving environment surrounding the ADV.

Abstract: In one embodiment, a method or system generates a high resolution 3-D point cloud to operate an autonomous driving vehicle (ADV) from a low resolution 3-D point cloud and camera-captured image(s). The system receives a first image captured by a camera for a driving environment. The system receives a second image representing a first depth map of a first point cloud corresponding to the driving environment. The system upsamples the second image by a predetermined scale factor to match an image scale of the first image. The system generates a second depth map by applying a convolutional neural network (CNN) model to the first image and the upsampled second image, the second depth map having a higher resolution than the first depth map such that the second depth map represents a second point cloud perceiving the driving environment surrounding the ADV.

Abstract: In one embodiment, a method or system generates a high resolution 3-D point cloud to operate an autonomous driving vehicle (ADV) from a low resolution 3-D point cloud and camera-captured image(s). The system receives a first image captured by a camera for a driving environment. The system receives a second image representing a first depth map of a first point cloud corresponding to the driving environment. The system downsamples the second image by a predetermined scale factor until a resolution of the second image reaches a predetermined threshold. The system generates a second depth map by applying a convolutional neural network (CNN) model to the first image and the downsampled second image, the second depth map having a higher resolution than the first depth map such that the second depth map represents a second point cloud perceiving the driving environment surrounding the ADV.

Abstract: The present invention is directed to systems and methods for performing energy disaggregation of a whole-house energy usage waveform, based at least in part on the whole-house energy usage profile, training data, and predetermined generic models, including: a module for pairing impulses identified in the whole-house energy usage waveform to indicate an appliance cycle, pairing impulses with at least one up transition with at least one down transition; a module for bundling impulses that are representative of an appliance cycle; a classification module, which upon determination of a type of appliance associated with bundles, is configured to classify the bundles of transitions in accordance with bundles exhibited by similar appliances with similar characteristics; and utilizing such pairing module and module for bundling to perform energy disaggregation.

Abstract: The present invention is directed to systems and methods of disaggregating and detecting energy usage associated with electric vehicle charging from a whole-house consumption signal. In general, methods of the present invention may include: identifying by an electronic processor potential interval candidates of electric vehicle charging, based at least in part upon long and decreasing patterns; determining by the electronic processor intervals associated with the charging of an electric vehicle, based at least in part on evaluating each potential interval candidate; determining by the electronic processor an initial point of charging for each interval associated with the charging of an electric vehicle; and accounting by the electronic processor for feedback of any incorrectly detected signals.

Abstract: In one embodiment, a method or system generates a high resolution 3-D point cloud to operate an autonomous driving vehicle (ADV) from a low resolution 3-D point cloud and camera-captured image(s). The system receives a first image captured by a camera for a driving environment. The system receives a second image representing a first depth map of a first point cloud corresponding to the driving environment. The system determines a second depth map by applying a convolutional neural network model to the first image. The system generates a third depth map by applying a conditional random fields model to the first image, the second image and the second depth map, the third depth map having a higher resolution than the first depth map such that the third depth map represents a second point cloud perceiving the driving environment surrounding the ADV.

Abstract: In one embodiment, a method or system generates a high resolution 3-D point cloud to operate an autonomous driving vehicle (ADV) from a low resolution 3-D point cloud and camera-captured image(s). The system receives a first image captured by a camera for a driving environment. The system receives a second image representing a first depth map of a first point cloud corresponding to the driving environment. The system upsamples the second image by a predetermined scale factor to match an image scale of the first image. The system generates a second depth map by applying a convolutional neural network (CNN) model to the first image and the upsampled second image, the second depth map having a higher resolution than the first depth map such that the second depth map represents a second point cloud perceiving the driving environment surrounding the ADV.

Abstract: In one embodiment, a method or system generates a high resolution 3-D point cloud to operate an autonomous driving vehicle (ADV) from a low resolution 3-D point cloud and camera-captured image(s). The system receives a first image captured by a camera for a driving environment. The system receives a second image representing a first depth map of a first point cloud corresponding to the driving environment. The system downsamples the second image by a predetermined scale factor until a resolution of the second image reaches a predetermined threshold. The system generates a second depth map by applying a convolutional neural network (CNN) model to the first image and the downsampled second image, the second depth map having a higher resolution than the first depth map such that the second depth map represents a second point cloud perceiving the driving environment surrounding the ADV.

Abstract: The present invention is generally directed to methods of disaggregating low resolution whole-house energy consumption data. In accordance with some embodiments of the present invention, methods may include steps of: receiving at a processor the low resolution whole house profile; selectively communicating with a first database including non-electrical information; selectively communicating with a second database including training data; and determining by the processor based on the low resolution whole house profile, the non-electrical information and the training data, individual appliance load profiles for one or more appliances.

Abstract: The present invention is generally directed to methods of disaggregating low resolution whole-house energy consumption data. In accordance with some embodiments of the present invention, methods may include steps of: receiving at a processor the low resolution whole house profile; selectively communicating with a first database including non-electrical information; selectively communicating with a second database including training data; and determining by the processor based on the low resolution whole house profile, the non-electrical information and the training data, individual appliance load profiles for one or more appliances.

Abstract: A user can issue a query about a process that has a number of stages. A stage of the process is determined using the query and location data associated with the query, and a stage prediction model. A stage learning system can select a sample of query logs for a category from a database of millions or billions of users' queries. Queries can be parsed into keywords. A category can be determined from location information associated with each query and from query keywords. Queries are aligned based on location and, optionally, keywords. TF-IDF values are computed for queries and are used to determine a difference significance between aligned, adjacent queries. If aligned, adjacent queries have a substantially difference in keywords and TF-IDF, then a conversion stage is identified. Content can be presented to the user based on the category, keywords, location, and conversion stage.

Abstract: Aspects in accordance with embodiments of the invention may include a method for remotely setting, controlling, or modifying settings on a programmable communicating thermostat (PCT) in order to customize settings to a specific house and user, including steps of: receiving at a remote processor information entered into the PCT by the user; receiving at the remote processor: non-electrical information associated with the specific house or user; and energy usage data of the specific house; performing by the remote processor energy disaggregation on the energy usage data; determining by the remote processor a custom schedule for the PCT based upon the information entered by the user, the non-electrical information associated with the specific house or user, and disaggregated energy usage data; revising by the remote processor, the custom schedule for the PCT based upon additional user input or seasonal changes; providing the custom schedule for the PCT to the PCT.

Abstract: Aspects in accordance with embodiments of the invention may include a method for remotely setting, controlling, or modifying settings on a programmable communicating thermostat (PCT) in order to customize settings to a specific house and user, including steps of: receiving at a remote processor information entered into the PCT by the user; receiving at the remote processor: non-electrical information associated with the specific house or user; and energy usage data of the specific house; performing by the remote processor energy disaggregation on the energy usage data; determining by the remote processor a custom schedule for the PCT based upon the information entered by the user, the non-electrical information associated with the specific house or user, and disaggregated energy usage data; revising by the remote processor, the custom schedule for the PCT based upon additional user input or seasonal changes; providing the custom schedule for the PCT to the PCT.

Abstract: The present invention is generally directed to methods of disaggregating low resolution whole-house energy consumption data. In accordance with some embodiments of the present invention, methods may include steps of: receiving at a processor the low resolution whole house profile; selectively communicating with a first database including non-electrical information; selectively communicating with a second database including training data; and determining by the processor based on the low resolution whole house profile, the non-electrical information and the training data, individual appliance load profiles for one or more appliances.