Amazon Technologies, Inc.

Twilio Inc.

Hanhan Wang

David Robert Cole

Haithem Albadawi

Narendra Gyanchandani

Bilyana Slavova

Maulik Majmudar

Jared Molton

William Scott Lamond

Njenga Kariuki

Daniel Kenneth Bone

Alexander Jonathan Pinkus

Samuel Elbert McGowan

Udit Mehta

Ranjith Govindan

Max Luster

Rakesh Nair

Thomas Pelletier

Brooks Taylor

Rowan Michael Wing

Inga Mgherbrishvili

Samuel Vaughn Tucker

Nicole Woytarowicz

A data warehouse connector service is described. The data warehouse connector service integrates with an identity resolution system of a customer data platform to systematically communicate identity-resolved customer profile data from the customer data platform to a cloud-based data warehouse. Operation of the data warehouse connector is managed through various configuration settings accessible to an end-user of the customer data platform via a user interface.

Techniques for generating customer profiles in a data warehouse

Methods, systems, and computer programs are presented for estimating if a user belongs to an audience category. One method includes an operation for accessing events generated at a website. Each event comprises a data structure describing an operation performed by a user, from a group of users, when accessing the website. Further, the method includes an operation for providing event information and information of a first user, for a predefined time window, as input to an audience machine-learning (ML) model. The audience ML model is trained with training data comprising values for features that include event features, user information features, and audience labels. The method further includes operations for generating, by the audience ML model, a score for the first user indicating a probability that the first user belongs to the audience, and for determining if the user belongs to the audience based on the score.

Generation of models for classifying user groups

Devices, systems, and methods are provided for performing volume and intensity-based activity evaluations. A method may include determining, by a device, a heart rate. The method may include determining, based on the heart rate, a motion threshold amount for a time period. The method may include determining motion data. The method may include comparing the motion data to the motion threshold for the time period. The method may include determining, based on the comparison of the motion data to the motion threshold for the time period, an activity intensity level associated with the heart rate and the motion data. The method may include determining, based on the activity intensity level, an activity score. The method may include sending data indicating the activity score for presentation at a second device.

Volume and intensity-based activity evaluations for devices

A microphone acquires audio data of a user's speech. The audio data is processed to determine sentiment data indicative of perceived emotional content of the speech. For example, the sentiment data may include values for one or more of valence that is based on a particular change in pitch over time or activation that is based on speech pace. A simplified user interface provides the user with a graphical representation based on the sentiment data and associated descriptors. For example, a visual indicator such as a dot may be shown at particular coordinates onscreen that correspond to the sentiment data. Text descriptors may also be presented near the dot. As the user continues speaking, and new audio data is processed, the interface is dynamically updated. The user may use this information to assess their state of mind, facilitate interactions with others, and so forth.

System for presentation of sentiment data

A device with a microphone acquires audio data of a user's speech. That speech comprises utterances, that together comprise a session. The audio data is processed to determine sentiment data indicative of perceived emotional content of the speech as conveyed by individual utterances of the user. That information is then used to determine the emotional content of the session. For example, the information may include several words describing the overall and outlying emotions of the session. Numeric metrics may also be determined, such as activation and valence. A user interface may present the words and metrics to the user. The user may use this information to assess their state of mind, facilitate interactions with others, and so forth.

System to characterize vocal presentation

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Seattle, WA, United States of America

Hanhan Wang