Sri International

Toyota Motor Corporation

Amir Tamrakar

Ajay Divakaran

Harpreet Singh Sawhney

Qian Yu

Hui Ting Cheng

Omar Javed

Jingen Liu

Jihua Huang

Girish Acharya

David A Salter

William Scott Mark

Kenneth C Nitz

Elizabeth E Shriberg

Jayakrishnan Kumar Eledath

Mohamed R Amer

Edgar Kalns

Michael Frandsen

Min Yin

Cory Albright

Michael Wessel

Jiejie Zhu

Saad Ali

Hyong-Gyun Kim

Andreas Tsiartas

Eric Law

Brent Mombourquette

Greg Ho

James R Carpenter

John James Byrnes

Timothy J Shields

Aswin Nadamuni Raghavan

Timothy J Meo

James F Carpenter

Timur Almaev

Alex C Tozzo

Sujeong Kim

Nonye M Alozie

Elizabeth McBride

Anirudh Som

Bladimir Lopez-Prado

David Salter

Max Ehrlich

Svati Dhamija

Makoto Okabe

Kyung-Yoon Kim

Gregory Ho

In some examples, a computer-implemented collaboration assessment model identifies actions of each of two or more individuals depicted in video data, identify, based at least on the identified actions of each of the two or more individuals depicted in the video data, first behaviors at a first collaboration assessment level, identify, based at least on the identified actions of each of the two or more individuals depicted in the video data, second behaviors at a second collaboration assessment level different from the first collaboration assessment level, and generate and output, based at least on the first behaviors at the first collaboration assessment level and the second behaviors at the second collaboration assessment level, an indication of at least one of an assessment of a collaboration effort of the two or more individuals or respective assessments of individual contributions of the two or more individuals to the collaboration effort.

Automated collaboration skills assessment

Methods, computing devices, and computer-program products are provided for implementing a virtual personal assistant. In various implementations, a virtual personal assistant can be configured to receive sensory input, including at least two different types of information. The virtual personal assistant can further be configured to determine semantic information from the sensory input, and to identify a context-specific framework. The virtual personal assistant can further be configured to determine a current intent. Determining the current intent can include using the semantic information and the context-specific framework. The virtual personal assistant can further be configured to determine a current input state. Determining the current input state can include using the semantic information and one or more behavioral models. The behavioral models can include one or more interpretations of previously-provided semantic information. The virtual personal assistant can further be configured to determine an action using the current intent and the current input state.

VPA with integrated object recognition and facial expression recognition

This disclosure describes techniques for improving accuracy of machine learning systems in facial recognition. The techniques include generating, from a training image comprising a plurality of pixels and labeled with a plurality of facial landmarks, one or more facial contour heatmaps, wherein each of the one or more facial contour heatmaps depicts an estimate of a location of one or more facial contours within the training image. Techniques further include training a machine learning model to process the one or more facial contour heatmaps to predict the location of the one or more facial contours within the training image, wherein training the machine learning model comprises applying a loss function to minimize a distance between the predicted location of the one or more facial contours within the training image and corresponding contour data generated from facial landmarks of the plurality of facial landmarks with which the training image is labeled.

Fine-level face alignment through anchor and contour estimations

Technologies for analyzing multi-task multimodal data to detect multi-task multimodal events using a deep multi-task representation learning, are disclosed. A combined model with both generative and discriminative aspects is used to share information during both generative and discriminative processes. The technologies can be used to classify data and also to generate data from classification events. The data can then be used to morph data into a desired classification event.

Deep multi-task representation learning

An evaluation engine has two or more modules to assist a driver of a vehicle. A driver drowsiness module analyzes monitored features of the driver to recognize two or more levels of drowsiness of the driver of the vehicle. The driver drowsiness module evaluates drowsiness of the driver based on observed body language and facial analysis of the driver. The driver drowsiness module is configured to analyze live multi-modal sensor inputs from sensors against at least one of i) a trained artificial intelligence model and ii) a rules based model while the driver is driving the vehicle to produce an output comprising a driver drowsiness-level estimation. A driver assistance module provides one or more positive assistance mechanisms to the driver to return the driver to be at or above the designated level of drowsiness.

Driver monitoring and response system

This disclosure describes techniques that include generating, based on a description of a scene, a movie or animation that represents at least one possible version of a story corresponding to the description of the scene. This disclosure also describes techniques for training a machine learning model to generate predefined data structures from textual information, visual information, and/or other information about a story, an event, a scene, or a sequence of events or scenes within a story. This disclosure also describes techniques for using GANs to generate, from input, an animation of motion (e.g., an animation or a video clip). This disclosure also describes techniques for implementing an explainable artificial intelligence system that may provide end users with information (e.g., through a user interface) that enables an understanding of at least some of the decisions made by the AI system.

Artificial intelligence in interactive storytelling

A method and a system are provided for monitoring driving conditions. The method includes receiving video data comprising video frames from one or more sensors where the video frames may represent an interior or exterior of a vehicle, detecting and recognizing one or more features from the video data where each feature is associated with at least one driving condition, extracting the one or more features from the video data, developing intermediate features by associating and aggregating the extracted features among the extracted features, and developing a semantic meaning for the at least one driving condition by utilizing the intermediate features and the extracted one or more features.

Method and system for monitoring driving behaviors

A system for object detection and tracking includes technologies to, among other things, detect and track moving objects, such as pedestrians and/or vehicles, in a real-world environment, handle static and dynamic occlusions, and continue tracking moving objects across the fields of view of multiple different cameras.

Real-time detection, tracking and occlusion reasoning

A complex video event classification, search and retrieval system can generate a semantic representation of a video or of segments within the video, based on one or more complex events that are depicted in the video, without the need for manual tagging. The system can use the semantic representations to, among other things, provide enhanced video search and retrieval capabilities.

Classification, search and retrieval of complex video events

Real-time object detection, tracking and occlusion reasoning

Classification, search, and retrieval of complex video events

A computer-implemented method for estimating a volume of at least one food item on a food plate is disclosed. A first and second plurality of images are received from different positions above a food plate, wherein angular spacing between the positions of the first plurality of images is greater than angular spacing between the positions of the second plurality of images. A first set of poses of each of the first plurality of images is estimated. A second set of poses of each of the second plurality of images is estimated based on at least the first set of poses. A pair of images taken from each of the first and second plurality of images is rectified based on at least the first and second set of poses. A 3D point cloud is reconstructed based on at least the rectified pair of images. At least one surface of the at least one food item above the food plate is estimated based on at least the reconstructed 3D point cloud. The volume of the at least one food item is estimated based on the at least one surface.

Method for computing food volume in a method for analyzing food

Growing community of inventors

Philadelphia, PA, United States of America

Amir Tamrakar