Abstract: A conversational assistant for conversational engagement platform can contain various modules including a user-model augmentation module, a dialogue management module, and a user-state analysis input/output module. The dialogue management module receives metrics tied to a user from the other modules to understand a current topic and a user's emotions regarding the current topic from the user-state analysis input/output module and then adapts dialogue from the dialogue management module to the user based on dialogue rules factoring in these different metrics. The dialogue rules also factors in both i) a duration of a conversational engagement with the user and ii) an attempt to maintain a positive experience for the user with the conversational engagement. A flexible ontology relationship representation about the user is built and stores learned metrics about the user over time with each conversational engagement, and then in combination with the dialogue rules, drives the conversations with the user.

Abstract: A system for at least one of warning of a possible wildfire or preventing a wildfire ignition in a selected area of interest includes a processor configured to execute instructions stored on a non-transitory medium. The instructions include receiving input data related to conditions that are relevant to igniting a wildfire in the selected area of interest. The instructions also include creating a model of a possible wildfire ignition in the selected area of interest using an artificial intelligence algorithm that is trained with the input data, providing an output from the model related to the possible wildfire ignition, and transmitting an alert notification automatically in response to the output exceeding a selected threshold value. The system also includes at least one of a warning device or a wildfire ignition prevention device configured to be automatically activated in response to receiving the alert notification.

Abstract: A conversational assistant for conversational engagement platform can contain various modules including a user-model augmentation module, a dialogue management module, and a user-state analysis input/output module. The dialogue management module receives metrics tied to a user from the other modules to understand a current topic and a user's emotions regarding the current topic from the user-state analysis input/output module and then adapts dialogue from the dialogue management module to the user based on dialogue rules factoring in these different metrics. The dialogue rules also factors in both i) a duration of a conversational engagement with the user and ii) an attempt to maintain a positive experience for the user with the conversational engagement. A flexible ontology relationship representation about the user is built and stores learned metrics about the user over time with each conversational engagement, and then in combination with the dialogue rules, drives the conversations with the user.

Abstract: A computing system includes a vision-based user interface platform to, among other things, analyze multi-modal user interactions, semantically correlate stored knowledge with visual features of a scene depicted in a video, determine relationships between different features of the scene, and selectively display virtual elements on the video depiction of the scene. The analysis of user interactions can be used to filter the information retrieval and correlating of the visual features with the stored knowledge.

Abstract: Embodiments of the disclosed technologies include a method of capturing, using a mobile device, a best-focused image of a skin surface of a subject, the method including: setting a camera of the mobile device to a fixed focal length; capturing, using the camera, a current image of a plurality of images of the skin surface, the plurality of images having a sequence and including a first previous image captured, using the camera, previously to the current image and a second previous image captured, using the camera, previously to the first previous image; producing a modified image from the current image; transforming the modified image, using a Laplacian pyramid, to produce a plurality of first luminance values from the modified image and a plurality of second luminance values from the plurality of first luminance values; averaging a plurality of first squared values, each including a square of a corresponding first luminance value of the plurality of first luminance values, to produce a first energy value; av

Abstract: Device logic in a mobile device configures a processor to capture a series of images, such as a video, using a consumer-grade camera, and to analyze the images to determine the best-focused image, of the series of images, that captures a region of interest. The images may be of a textured surface, such as facial skin of a mobile device user. The processor sets a focal length of the camera to a fixed position for collecting the images. The processor may guide the user to position the mobile device for capturing the images, using audible cues. For each image, the processor crops the image to the region of interest, extracts luminance information, and determines one or more energy levels of the luminance via a Laplacian pyramid. The energy levels may be filtered, and then are compared to energy levels of the other images to determine the best-focused image.

Abstract: Device logic in a mobile device configures a processor to capture a series of images, such as a video, using a consumer-grade camera, and to analyze the images to determine the best-focused image, of the series of images, that captures a region of interest. The images may be of a textured surface, such as facial skin of a mobile device user. The processor sets a focal length of the camera to a fixed position for collecting the images. The processor may guide the user to position the mobile device for capturing the images, using audible cues. For each image, the processor crops the image to the region of interest, extracts luminance information, and determines one or more energy levels of the luminance via a Laplacian pyramid. The energy levels may be filtered, and then are compared to energy levels of the other images to determine the best-focused image.

Abstract: A computing system includes a vision-based user interface platform to, among other things, analyze multi-modal user interactions, semantically correlate stored knowledge with visual features of a scene depicted in a video, determine relationships between different features of the scene, and selectively display virtual elements on the video depiction of the scene. The analysis of user interactions can be used to filter the information retrieval and correlating of the visual features with the stored knowledge.