Embodyvr, Inc.

X Development LLC

Shruti Badhwar

Kapil Jain

Nikhil Ratnesh Javeri

Faiyadh Shahid

Allen Richard Zhao

Gearoid Murphy

Alexander Holiday

Marielle Venita Jakobsons

Saarish Kareer

Nidhi Sikarwar

Diosdado Banatao

Methods and systems for using multiple hyperspectral cameras sensitive to different wavelengths to predict characteristics of objects for further processing, including recycling, are described. The multiple hyperspectral images can be used to predict higher resolution spectra by using a trained machine learning model. The higher resolution spectra may be more easily analyzed to sort plastics into a recyclability category. The hyperspectral images may also be used to identify and analyze dark or black plastics, which are challenging for SWIR, MWIR, and other wavelengths. The machine learning model may also predict the base polymers and contaminants of plastic objects for recycling. The hyperspectral images may be used to predict recyclability and other characteristics using a trained machine learning model.

Sensor fusion approach for plastics identification

An application such as a machine learning workflow manager executes a machine learning workflow utilizing varying sets of parameters and tracks the utilized parameters and performance metrics for each execution. The manager generates a unique experiment identifier (ID) for each set of parameters used for executing a workflow and stores the experiment ID and the set of parameters along with results of the execution, e.g., performance metrics, output data, program code, etc. In some implementations, the manager can algorithmically generate sets of parameters for experiments of the workflow. Once experimentation for the workflow is complete, the manager utilizes the stored performance metrics to identify an experiment which exhibited the best performance and can retrieve the set of parameters, output data, or generated program code for deployment of the workflow using the associated experiment ID.

Automated versioning and evaluation of machine learning workflows

Spatial audio is received from an audio server over a first communication link. The spatial audio is converted by a cloud spatial audio processing system into binaural audio. The binauralized audio is streamed from the cloud spatial audio processing system to a mobile station over a second communication link to cause the mobile station to play the binaural audio on the personal audio delivery device.

Streaming binaural audio from a cloud spatial audio processing system to a mobile station for playback on a personal audio delivery device

A video is received from a video capture device. The video capture device has a front facing camera and a display screen which displays the video captured by the video capture device in real time to a user. One or more images of a pinna and head of the user in the video are used to automatically determine one or more features associated with the user. The one or more features include an anatomy of the user, a demographic of the user, a latent feature of the user, and an indication of an accessory worn by the user. Based on the one or more features and one or more HRTF models, a head related transfer function (HRTF) is determined which is personalized to the user.

Personalized head related transfer function (HRTF) based on video capture

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San Mateo, CA, United States of America

Shruti Badhwar