US 12,080,416 B2
Systems and methods for animal health monitoring
Mark Alan Donavon, Troy, IL (US); Natalie Langenfeld-McCoy, Bethalto, IL (US); Ragen Trudelle-Schwarz McGowan, Saint Joseph, MO (US); Helber Dussan, Saint Louis, MO (US); Mani Bharath Kamaraj, Coimbatore (IN); Vignesh Vijayarajan, Chennai (IN); Venkatakrishnan Govindarajan, Chennai (IN); Ajay Singh, Madhya Pradesh (IN); Sarath Malipeddi, Andhra Pradesh (IN); Abhishek Sai Nasanuru, Tirupati (IN); Ayushi Krishnan, Bihar (IN); Dwarakanath Raghavendra Ravi, Chennai (IN); Daniel James Sherwood, Cambridge (GB); Russell Stewart Maguire, Cambridge (GB); Jack William James Stone, Cambridge (GB); Georgina Elizabeth Mary Logan, Cambridge (GB); Tomoko Hatori, Cambridge (GB); Peter Michael Haubrick, Cambridge (GB); and Wendela Sophie Schim van der Loeff, Cambridge (GB)
Assigned to Société des Produits Nestlé S.A., Vevey (CH)
Filed by Société des Produits Nestlé S.A., Vevey (CH)
Filed on Aug. 26, 2022, as Appl. No. 17/896,399.
Claims priority of provisional application 63/237,664, filed on Aug. 27, 2021.
Prior Publication US 2023/0068528 A1, Mar. 2, 2023
Int. Cl. G16H 40/63 (2018.01)
CPC G16H 40/63 (2018.01) 46 Claims
OG exemplary drawing
 
1. A method of monitoring the health of an animal, comprising:
obtaining load data 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;
determining the load data is from an animal interaction with the animal monitoring device;
recognizing an animal behavior property associated with the animal determined based on load data that the interaction with the contained litter was due to the animal interaction with the contained litter;
employing normalization logic to analyze the load data for the purpose of classification; and
classifying the animal behavior property into an animal classified event using a machine learning classifier including analyzing the load data via a phase separation algorithm, wherein the animal classified events include animal eliminations, and wherein the phase separation algorithm is capable of classifying multiple discrete animal eliminations.