The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 11, 2022

Filed:

Jun. 28, 2018
Applicant:

Observe Technologies Limited, Richmond, GB;

Inventors:

Hemang Ravi Rishi, Richmond, GB;

Pieter Jan Fabry, London, GB;

Ivan Makeev, London, GB;

Charchris Sloan, London, GB;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A01K 61/80 (2017.01); A01K 61/13 (2017.01); G06K 9/62 (2022.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06T 7/20 (2017.01); A01K 61/60 (2017.01); A01K 61/10 (2017.01); G06N 20/00 (2019.01); G06V 40/20 (2022.01);
U.S. Cl.
CPC ...
A01K 61/80 (2017.01); A01K 61/10 (2017.01); A01K 61/13 (2017.01); A01K 61/60 (2017.01); G06K 9/6262 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06T 7/20 (2013.01); G06V 40/20 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

The present invention relates to a method and apparatus for providing a dynamic decision-making process in relation to feeding animals in water. More particularly, the present invention relates to a method and apparatus for improving feeding and/or farming strategies used in a fish farm. According to a first aspect, there is provided a computer-implemented method for feeding one or more aquatic animals, the method comprising the steps of: receiving pre-processed sensor data in relation to the one or more aquatic animals; inputting the pre-processed sensor data into one or more learned decision-making models, wherein the one or more learned decision-making models has been trained to substantially optimise the rate and amount of food provided to the aquatic animals; determining, by the one or more learned decision-making models using the received pre-processed sensor data, feeding instructions for the one or more aquatic animals; and outputting the feeding instructions from the one or more learned decision-making models.


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