Amazon Technologies, Inc.

Yelin Kim

Saurabh Gupta

Dilek Zeynep Hakkani-Tur

Yang Liu

Cheng-Hao Kuo

Anna Chen Santos

Amin Hani Atrash

Raumi Sidki

Che-Chun Su

Vikas Deshpande

Thomas Irvine Nelson

Joshua Seth Levy

Zhuo Deng

A system configured to reduce false positives when performing human presence detection is provided. In addition to calculating a Human Detection (HD) confidence score during human presence detection, the system may use human keypoint detection (HKD) techniques to calculate a true positive (TP) confidence score and detect false positives based on a combination of the two confidence scores. For example, the device system may generate keypoint data, which indicates a location and maximum confidence value for individual keypoints associated with a human body. The system may input the keypoint data to a model configured to generate the TP confidence score, such as a logistic regression model that is configured to receive numerical values as inputs (e.g., HD confidence score and 17 keypoint confidence values) and generate the TP confidence score. The system then detects false positives using the TP confidence score and may remove corresponding bounding boxes.

False positive suppression using keypoints

Described herein is a system for improving sentiment detection and/or recognition using multiple inputs. For example, an autonomously motile device is configured to generate audio data and/or image data and perform sentiment detection processing. The device may process the audio data and the image data using a multimodal temporal attention model to generate sentiment data that estimates a sentiment score and/or a sentiment category. In some examples, the device may also process language data (e.g., lexical information) using the multimodal temporal attention model. The device can adjust its operations based on the sentiment data. For example, the device may improve an interaction with the user by estimating the user's current emotional state, or can change a position of the device and/or sensor(s) of the device relative to the user to improve an accuracy of the sentiment data.

Multimodal sentiment detection

A simulated emotional state of a device is generated and maintained based on previous emotional state and contextual information. A trigger is associated with an effect value. For example, successful completion of a task may have an effect value of +5 while task failure may have an effect value of &#x2212;6. Upon occurrence of a trigger, an associated effect value of the trigger may be combined with a previously determined effect value to determine a function input value (FIV). The FIV is used as input to a function that provides an emotion value as output. The emotion value may then be used as input to determine a particular action. For example, if the emotion value corresponds to 'happy', the resulting action may be presenting an animation of a smile on a display device. As triggers occur, the function input value is updated, resulting in updates to the emotion value.

Simulation of emotional state of an interactive device

Growing community of inventors

Sunnyvale, CA, United States of America

Yelin Kim