Google Inc.

A9.com, Inc.

Chetan Pitambar Bhole

Tanmay Sanjay Khirwadkar

Deepak Ramamurthi Sivaramapuram Chandrasekaran

Sanjay Mangla

Arnab Sanat Kumar Dhua

Erick Cantu-Paz

Eran Borenstein

Anish Ramdas Nair

Sourabh Prakash Bansod

Dongeek Shin

Shwetak N Patel

Todd Whitehurst

Himanshu Arora

Rizwan Ahmed Chaudhry

Anupam Pathak

Vaibhav Darbari

Methods, systems, and media for timing presentation of content are provided. In some embodiments, the method comprises: causing a first video content item to be presented on a user device of a user; wherein the video content item is provided by a video sharing service; identifying a promotion for the video sharing service that is to be presented to the user during a time window; determining a probability that the user will select the promotion if the promotion is presented at a first time point within the time window in connection with the first video content item; determining whether to present the promotion at the first time point based on at least on the probability; in response to determining that the promotion is to be presented at the first time point, causing the promotion to be presented on the user device at the first time point in connection with the first video content item; and, in response to determining that the promotion is not to be presented at the first time point, causing the promotion to be presented on the user device at a second time point in connection with a second video content item, wherein the second time point is within the time window and wherein the second time point is after the first time point.

Methods, systems, and media for timing presentation of content

A simulation test is run on a first machine learning model trained using first training data historically collected over a time period. The first training data includes a set of training inputs and a set of target outputs. In response to a determination that a result of the simulation test run on the first machine learning model satisfies one or more criteria, a size of the set of target outputs of the first training data is determined. Second training data for training a second machine learning model is obtained. A size of a set of target outputs of the second training data meets or exceeds the size of the target outputs of the first training data. The second machine learning model is trained using the second training data.

System and methods for machine learning training data selection

Various arrangements for performing fall detection are presented. A smart-home device (), comprising a monolithic radar integrated circuit (), may transmit radar waves. Based on reflected radar waves, raw waveform data may be created. The raw waveform data may be processed to determine that a fall by a person () has occurred. Speech may then be output announcing that the fall has been detected via the speaker () of the smart home device ().

Privacy-preserving radar-based fall monitoring

Methods, systems, and media for timing presentation of content are provided. In some embodiments, the method comprises: causing a first video content item to be presented on a user device of a user, wherein the video content item is provided by a video sharing service; identifying a promotion for the video sharing service that is to be presented to the user during a time window; determining a probability that the user will select the promotion if the promotion is presented at a first time point within the time window in connection with the first video content item; determining whether to present the promotion at the first time point based on at least on the probability; in response to determining that the promotion is to be presented at the first time point, causing the promotion to be presented on the user device at the first time point in connection with the first video content item; and, in response to determining that the promotion is not to be presented at the first time point, causing the promotion to be presented on the user device at a second time point in connection with a second video content item, wherein the second time point is within the time window and wherein the second time point is after the first time point.

A system and method are disclosed for running a plurality of simulation tests on a first machine learning model to obtain a plurality of results that are each produced during a respective simulation test, the first machine learning model gradually trained using first training data historically collected over a period of time, the first training data comprising a plurality of first training data sets each including a subset of first training inputs and first target outputs associated with one of a plurality of points in time during the period of time, determining a simulation test of the plurality of simulation tests at which corresponding results of the first machine learning model satisfy a threshold condition, wherein the threshold condition is based on historical data at a first point in time of the plurality of points in time, identifying a first training data set of the plurality of first training data sets on which the first machine learning model used during the determined simulation test was trained, wherein the first training data set on which the first machine learning model used during the determined simulation test was trained is associated with one or more second points in time that precede the first point in time, and determining a subset of target outputs from the identified first training data set on which the first machine learning model used during the determined simulation test was trained, the determined subset of first target outputs to define an amount of second training data to be sufficient to train a second machine learning model.

Images can be used to determine the size of an article of clothing matching the physical characteristics of a user. An image can include an article of clothing known to fit well or a representation of a body part, which can comprise the body part itself. Images can also include a reference item. Various techniques are used to determine one or more perspective distortions in the image based on analysis of the reference item and information known about the reference item. The one or more perspective distortions can be accounted for to determine physical characteristics of the article of clothing or the body part associated with the article of clothing. The physical characteristics can be used to determine the size of an article of clothing matching, or corresponding to, the physical characteristics.

Accurate size selection

The selection of content to present to a user can be based at least in part upon probabilities of the user selecting to view more information and/or entering into a transaction with respect to instances of the content. For example, user behavior with respect to various queries provided through a content provider can be determined in order to calculate a probability that a user was searching for a particular image. The user behavior can include historical action data, such as information that indicates images associated with an action (e.g., selected, purchased, etc.) in response to a particular image search. The historical action data can be analyzed to generate an index that indicates a likelihood that the search was intended for a particular image. Once an image query is received, items of interest can be determined using the index, and those images and associated content can be presented to the user.

Determine image relevance using historical action data

Techniques to determine the three-dimensional structure of wearable items are disclosed. A scanning device can project waves at a wearable item and receive reflections of the waves. In embodiments, the scanning device projects light pulses and detects reflections of the light pulses. Depth measurements from the scanning device to the wearable item can be used to determine points corresponding to locations on the wearable item. A point cloud can be generated of the points, which can convey the three-dimensional structure of the wearable item. In embodiments, another wearable item can be determined to have a corresponding three-dimensional structure based on point cloud comparisons. The second wearable item can be suggested to a user as a wearable item having an appropriate fit for the user.

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San Mateo, CA, United States of America

Chetan Pitambar Bhole