Verily Life Sciences LLC

Other

Google Inc.

Carnegie Mellon University

Mark Desnoyer

Tiantian Zha

Dilip Krishnan

Jianyi Liu

Nigel Snoad

Deborah Jeanne Johnson

Jacob Crawford

Yu Han

Wei Xiang

Kyran M Staunton

Michael Jay Tarr

Josh Livni

Yaniv Ovadia

Sophie Lebrecht

Peter Massaro

Sunil Mallya Kasaragod

Victor Criswell

Padraig Michael Furlong

Scott Ritchie

Yi Han

Scott A Ritchie

Sunil Mallya

Nicholas Paul Dufour

Daniel Newburger

David Henry Lea

A system includes an acoustic lure and a sensor package disposed within an interior volume of an insect trapping container and a computing device in communication with the acoustic lure and the sensor package configured to instruct the acoustic lure to output an acoustic tone. The computer device is further configured to receive sensor data from the sensor package, the sensor data representative of insects within the interior volume. Responsive to receiving the sensor data, the computing device is configured to instruct an imaging device to capture image data representative of the insects within the interior volume or determine insect count data based on the sensor data. The computing device is configured to then transmit output data to a remote computing system, the output data including at least one of a first portion of the image data or a second portion of the insect count data.

Lures and sensors for insect trapping

Insects can be classified into a category (e.g., sex category, species category, size category, etc.) using a variety of different classification approaches including, for example, an industrial vision classifier and/or a machine learning classifier. At least some classification approaches may be used in real-time to make real-time decisions and others can be used to validate earlier-made real-time decisions.

Predictive classification of insects

An insect trapping system includes an enclosed container having an entrance hole formed in a vertical wall and an acoustic lure device located within the container. The enclosed container may be formed from a transparent material. The system may also include a support stand, formed from a dark-colored material, and used to support the container.

Insect trapping systems

Insects can be localized and classified using a predictive model. To begin, image data is obtained that corresponds to the insects. Using a predictive model, samples of the image data are evaluated to determine whether the image portions include an insect and, if so, into what category the insect should be classified (e.g., male/female, species A/species B, etc.).

Predictive models for visually classifying insects

In one embodiment, a plurality of images is received. The plurality of images are frames of a video file. A user requests for a thumbnail picture representative of the plurality of images. The plurality of images are filtered to obtain a set of images. The filtering can be based on a blurriness of the image, whether an image is near a scene transition, an amount of text depicted in the image, or a color level of the image. Valence scores may be determined for one or more of the images in the set of images. Valence scores are based on determining values of characteristics of an image that can predict user responses to the image. A first image from the set of images is selected based at least in part on the valence score of the first image. The first image is sent for display.

Selecting a high valence representative image

Access is provided to optimal thumbnails that are extracted from a stream of video. Using a processing device configured with a model that incorporates preferences generated by the brain and behavior from the perception of visual images, the optimal thumbnail(s) for a given video is/are selected, stored and/or displayed.

Automated thumbnail selection for online video

Growing community of inventors

San Francisco, CA, United States of America

Mark Desnoyer