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:
Nov. 15, 2022

Filed:

Jan. 15, 2020
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Jagadesh Ramaswamy Hulugundi, Bangalore, IN;

Abhishek Solanki, Bangalore, IN;

Sudhanshu Shekhar, Bangalore, IN;

Raghuveer Prasad Nagar, Kota, IN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 7/00 (2006.01); G06N 5/04 (2006.01); G06F 16/27 (2019.01); A01K 29/00 (2006.01); G06N 20/00 (2019.01); G06F 16/23 (2019.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); A01K 29/00 (2013.01); G06F 16/2379 (2019.01); G06F 16/27 (2019.01); G06N 20/00 (2019.01);
Abstract

In predictive natural disaster response, a server determines an affected location predicted to be affected by a natural disaster and determines whether the type of natural disaster requires relocation of an animal population at the affected location. When relocation is required, using machine learning models, the server generates a rehabilitation needs prediction for the relocation, determines an optimal location for the relocation from candidate locations, and generates a relocation plan to relocate the animal population from the affected location to the optimal location. When relocation is not required, using the machine learning models, the server generates a demands prediction for products and services at the affected location, generates a supply prediction for the products and the services at the affect location, and generates a supply plan using the demands and supply predictions.


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