Societe Des Produits Nestle S.a.

Nestec S.a.

Mark A Donavon

Ragen Trudelle-Schwarz McGowan

Helber Dussan

Natalie Langenfeld-McCoy

Sarath Malipeddi

Mani Bharath Kamaraj

Dwarakanath Raghavendra Ravi

Wendela Sophie Schim Van Der Loeff

Ayushi Krishnan

Tomoko Hatori

Daniel James Sherwood

Russell Stewart Maguire

Peter Michael Haubrick

Vignesh Vijayarajan

Jack William James Stone

Venkatakrishnan Govindarajan

Ajay Singh

Georgina Elizabeth Mary Logan

Kristin Slater

Abhishek Sai Nasanuru

Gregg Robert Draudt

Narendra Kumar Murugan

Srikanth Vemparala

Julia Foti

LeAnn Snow

Alex Sutton

Nicholas Schoeneck

Rabinarayan Swain

Vignesh Chockalingam Arun

Roberto Vargas

Olivia Arndt

Sanjay Kalsangra

Mark A Roos

William Joseph Vespa

James Collins

Stuart Neubarth

Sarah Thomas

William Broun

Mark Harris

Julien Belahbib

Brian Lester

Shivam Awasthi

Mohammed Affan Khan

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.

The present disclosure is directed to systems and methods for preparing nutrition, health, and/or wellness recommendations for an animal. The systems and methods involve collecting data from the animal, analyzing the data, and providing the nutrition, health, and/or wellness recommendation based upon the analyzed data.

Systems and methods for providing animal health, nutrition, and/or wellness recommendations

Pet scale

Methods and systems for customized pet food are described. Generally, the systems and methods comprises obtaining a pet profile information from a user, which includes at least one of a species, an activity level, a medical history, a breed, a gender, a breeding status, a feeding method, an age, a spayed/neutered status, a snack schedule, a biological sample, a body condition, a dental health, a coat information, a digestive health information and a weight of the pet. The methods and systems may also comprise obtaining a second pet profile information from a user, related to a preference regarding an ingredient, a food form, a flavor, a protein source, a shape and a texture. The methods and systems also may include correlating the first pet profile information and second pet profile information to a stored nutritional information to determine a pre-made pet blend based.

Systems and methods for ordering and manufacturing custom pet food

Growing community of inventors

Troy, IL, United States of America

Mark A Donavon