Sri International

Michael Wessel

Girish Acharya

Edgar Kalns

Min Yin

Hyong-Gyun Kim

Ajay Divakaran

William Scott Mark

Kenneth C Nitz

Elizabeth E Shriberg

Amir Tamrakar

Michael Frandsen

Jihua Huang

Cory Albright

Andreas Tsiartas

James F Carpenter

Brent Mombourquette

Eric Law

Greg Ho

James R Carpenter

John James Byrnes

Dayne B Freitag

Yi Yao

Dimitra Vergyri

Andreas Kathol

Anirban Roy

Aaron Spaulding

David A Salter

Louise Yarnall

Leonor Almada

Roland Heusser

Zachary Weiler

David Salter

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

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 machine learning techniques for capturing human knowledge for performing a task. In one example, a video device obtains video data of a first user performing the task and one or more sensors generate sensor data during performance of the task. An audio device obtains audio data describing performance of the task. A computation engine applies a machine learning system to correlate the video data to the audio data and sensor data to identify portions of the video, sensor, and audio data that depict a same step of a plurality of steps for performing the task. The machine learning system further processes the correlated data to update a domain model defining performance of the task. A training unit applies the domain model to generate training information for performing the task. An output device outputs the training information for use in training a second user to perform the task.

Machine learning system for technical knowledge capture

Growing community of inventors

Palo Alto, CA, United States of America

Michael Wessel