Societe Des Produits Nestle S.a.

Dwarakanath Raghavendra Ravi

Mark A Donavon

Ragen Trudelle-Schwarz McGowan

Natalie Langenfeld-McCoy

Helber Dussan

Mani Bharath Kamaraj

Sarath Malipeddi

Peter Michael Haubrick

Ayushi Krishnan

Venkatakrishnan Govindarajan

Vignesh Vijayarajan

Wendela Sophie Schim Van Der Loeff

Tomoko Hatori

Georgina Elizabeth Mary Logan

Jack William James Stone

Russell Stewart Maguire

Daniel James Sherwood

Abhishek Sai Nasanuru

Ajay Singh

Kristin Slater

Mark Harris

Stuart Neubarth

Olivia Arndt

Sarah Thomas

Rabinarayan Swain

Nicholas Schoeneck

LeAnn Snow

Sanjay Kalsangra

Julia Foti

Srikanth Vemparala

Narendra Kumar Murugan

Vignesh Chockalingam Arun

Mohammed Affan Khan

Shivam Awasthi

Julien Belahbib

Mark Alan Donavon

Ragen Trudelle-Schwarz Mcgowan

The present disclosure relates to the monitoring feeding behavior(s) of a pet(s), under the control of at least one processor. An example method includes obtaining load data from a load sensor of a pet bowl while the pet is interacting with contents of the pet bowl. The load sensor can have a sensitivity of +/−50 grams or less and the load data is obtained can occur at a sample rate from 10 samples to 150 samples per second. The example method can also include sequentially grouping the load data in 0.01 second to 5 second time increments, wherein individual time increments include multiple samples, and identifying a feeding behavior occurring within one or more of the time increments based on the load data resulting from the pet interacting with the pet bowl or the contents of the pet bowl.

Methods of monitoring feeding behaviors of pets

The present disclosure provides systems and methods for animal health monitoring. Load data can be obtained from a plurality of load sensors associated with a platform carrying contained litter thereabove, wherein individual load sensors of the plurality of load sensors are separated from one another and receive pressure input from the platform independent of one another. If the load data is determined or not to be from an animal interaction with the contained litter, an animal behavior property associated with an animal is recognized if a determination is made based on load data that the interaction with the contained litter was due to the animal interaction. The animal behavior property is classified into an animal classified event using a machine learning classifier. A change in the animal classified event is identified as compared to a previously recorded event associated with the animal.

Systems and methods for animal health monitoring

The present disclosure provides systems and methods for animal health monitoring. Load data can be obtained from an animal monitoring device including three or more load sensors associated with a platform carrying contained litter thereabove, wherein individual load sensors of the three or more load sensors are separated from one another and receive pressure input from the platform independent of one another, wherein the three or more load sensors individually sample loads at from 2.5 Hz to 110 Hz. An animal behavior property associated with the animal can be recognized if it is determined based on load data that the interaction with the contained litter was due to the animal interaction with the contained litter. The animal behavior property can be classified into an animal classified event using a machine learning classifier.

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Chennai, India

Dwarakanath Raghavendra Ravi