Sri International

Aaron Spaulding

Girish Acharya

Harpreet Singh Sawhney

Rakesh Kumar

Supun Samarasekera

Michael Anthony Isnardi

Mayank Bansal

Jayakrishnan Kumar Eledath

Sek Meng Chai

Michael John Wolverton

Douglas A Bercow

Edgar Kalns

John Benjamin Southall

David Chao Zhang

Dimitra Vergyri

Min Yin

Michael Raymond Piacentino

Andreas Kathol

Michael Wessel

Hyong-Gyun Kim

James F Carpenter

David Berends

Ron Krakower

Leonor Almada

Roland Heusser

Nikoletta Basiou

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.

Conversational assistant for conversational engagement

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.

Real-time human-machine collaboration using big data driven augmented reality technologies

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.

Selecting optimal image from mobile device captures

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Menlo Park, CA, United States of America

Aaron Spaulding