Foodfx Inc

Juniper Networks, Inc.

Skoopmagic, Inc.

Cyphort Inc.

Fengmin Gong

Jun Du

Min Fan

Weijun Zhang

Frank Jas

Alexander Burt

Samir Virmani

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for tracking consumption rate of food in food containers. An example method includes capturing an image of a food station using an RGB camera, determining bounding boxes on the image that correspond to food containers in the food station, capturing an overall point cloud of the food station using a depth camera, cropping a point cloud for each food container from the overall point cloud based on the bounding boxes. Then for each food container, a volume of remaining food in the food container is determined based on the point cloud of the food container, and the food consumption information of the food containers is obtained by tracking volume changes of the remaining food in food containers.

System and method for food service consumption tracking

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for dietary tracking with instant dietary feedback using a portable multi-sensor device are described. An example method may include: obtaining a first machine learning model trained based on a plurality of food images and corresponding labels; receiving a series of images from one or more cameras capturing a user taking food out of a food-serving storage; determining, using the first machine learning model based on the series of images, an identification of the food being taken by the user; determining, using a second machine learning model based on the series of images and the identification of the food, an estimated weight of the food being taken by the user; determining and displaying portion-based food information of the food being taken by the user.

Multi-sensor device for dietary tracking

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for automated foodservice are described. An example method includes receiving one or more images from one or more cameras capturing a food container comprising a plurality of compartments; determining, using a first machine learning model based on the one or more images, a user action comprising a user taking food from a first compartment of the plurality of compartments; receiving a plurality of pieces of weight sensor data from a plurality of weight sensors coupled to a frame hosting the food container; determining, using a second machine learning model based on the plurality of pieces of weight sensor data, a second compartment of the plurality of compartments; in response to the first compartment being the same as the second compartment, displaying the weight and the food information of the food taken by the user.

Method and system for foodservice with instant feedback

A system configured to detect a threat activity on a network. The system including a digital device configured to detect a first order indicator of compromise on a network, detect a second order indicator of compromise on the network, generate a risk score based on correlating said first order indicator of compromise on the network with the second order indicator of compromise on said network, and generate at least one incident alert based on comparing the risk score to a threshold.

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LOS ALTOS HILLS, CA, United States of America

Fengmin Gong