Affectiva, Inc.

Timothy Peacock

Rana El Kaliouby

Taniya Mishra

Panu James Turcot

Seyedmohammad Mavadati

Gregory Poulin

Evan Kodra

Techniques for machine-trained analysis for multimodal machine learning vehicle manipulation are described. A computing device captures a plurality of information channels, wherein the plurality of information channels includes contemporaneous audio information and video information from an individual. A multilayered convolutional computing system learns trained weights using the audio information and the video information from the plurality of information channels. The trained weights cover both the audio information and the video information and are trained simultaneously. The learning facilitates cognitive state analysis of the audio information and the video information. A computing device within a vehicle captures further information and analyzes the further information using trained weights. The further information that is analyzed enables vehicle manipulation. The further information can include only video data or only audio data. The further information can include a cognitive state metric.

Multimodal machine learning for vehicle manipulation

Techniques for multidevice, multimodal emotion services monitoring are disclosed. An expression to be detected is determined. The expression relates to a cognitive state of an individual. Input on the cognitive state of the individual is obtained using a device local to the individual. Monitoring for the expression is performed. The monitoring uses a background process on a device remote from the individual. An occurrence of the expression is identified. The identification is performed by the background process. Notification that the expression was identified is provided. The notification is provided from the background process to a device distinct from the device running the background process. The expression is defined as a multimodal expression. The multimodal expression includes image data and audio data from the individual. The notification enables emotion services to be provided. The emotion services augment messaging, social media, and automated help applications.

Multidevice multimodal emotion services monitoring

Techniques are described for machine-trained analysis for multimodal machine learning. A computing device captures a plurality of information channels, wherein the plurality of information channels includes contemporaneous audio information and video information from an individual. A multilayered convolutional computing system learns trained weights using the audio information and the video information from the plurality of information channels, wherein the trained weights cover both the audio information and the video information and are trained simultaneously, and wherein the learning facilitates emotional analysis of the audio information and the video information. A second computing device captures further information and analyzes the further information using trained weights to provide an emotion metric based on the further information. Additional information is collected with the plurality of information channels from a second individual and learning the trained weights factors in the additional information. The further information can include only video data or audio data.

Multimodal machine learning for emotion metrics

Mental state data is gathered from a plurality of people and analyzed in order to determine mental state information. Metrics are generated based on the mental state information gathered as the people view media presentations. Norms, defined as the quantitative measures of the mental states of a plurality of people as they view the media presentation, are determined based on the mental state information metrics. The norms can be determined based on various viewer criteria including country of residence, demographic group, or device type on which the media presentation is viewed. Responses to new media are then compared against norms to determine the effectiveness of the new media presentations.

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Concord, MA, United States of America

Timothy Peacock