IP Library Granted Patent US 12,626,529
Granted Patent B2
US 12,626,529 · App. 18/705,152 · Granted May 12, 2026

Character determination system and character determination method

Inventors: Tsuguna Inagaki (Tokyo, JP); Hiroyuki Takahashi (Tokyo, JP); Yoshie Ogoshi (Tokyo, JP)
Assignee: ANICOM HOLDINGS, INC.
G06V40/103A01K29/00G06V10/70G06V40/10
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,626,529
App. No.
18/705,152
Granted
May 12, 2026
Kind
B2
Abstract

An object of the present invention is to provide a character determination system and the like which determine a character of an animal with a simple method. The character determination system includes: receiving means which receives an input of an image of an animal; and character determining means which determines, using a trained model, a character of the animal from the image of the animal inputted to the receiving means, wherein the trained model is a trained model having learned a relationship between an image of an animal and a character of the animal.

Claims (41)

1 . A character determination system, comprising:

receiving means which receives an input of an image of an animal other than a human; and character determining means which determines, using a machine learning or a deep learning trained model, a character of the animal from the image of the animal inputted to the receiving means, and

a processor configured to:

determine behavioral characteristics of the animal; and

determine the character based on a strength of a tendency for each of the behavioral characteristics that are determined;

wherein

the character includes friendliness, and

wherein the trained model has been subjected to learning using, as training data, an image of the animal and a label related to the character of one or more specific breeds to learn a relationship between the image of the animal other than a human and the character of the animal.

2 . The character determination system according to claim 1 , wherein the character includes behavioral characteristics defined in C-barq (Canine Behavioral Assessment and Research Questionnaire).

3 . The character determination system according to claim 1 , wherein the trained model of the character determining means is the trained model which performs learning using the image of an animal other than the human and the label related to the character of the animal as training data and which receives the image of an animal as input and outputs the character determination of the animal.

4 . The character determination system according to claim 1 , wherein the image of the animal received by the receiving means is an image obtained by photographing a face of the animal from front.

5 . A character determination system, comprising:

receiving means which receives an input of an image of an animal other than a human; and character determining means which determines, using a machine learning or deep learning trained model, a character of the animal from the image of the animal inputted to the receiving means, and

a processor configured to:

determine behavioral characteristics of the animal; and

determine the character based on a strength of a tendency for each of the behavioral characteristics that are determined;

wherein

the trained model is a trained model having learned a relationship between an image of an animal other than a human and a character of the animal, and

the character determination system further comprises breed determining means which determines, using the machine learning or deep learning trained model for breed determination, a breed of an animal from an image of the animal inputted to the receiving means.

6 . The character determination system according to claim 5 , wherein the character determining means is provided with a plurality of trained models, and the character determining means determines, using one of the plurality of trained models corresponding to the determination result of the breed determining means, the character of the animal in the input image.

7 . The character determination system according to claim 6 , wherein the trained model is provided in plurality in accordance with a category into which a breed of an animal is categorized based an average weight of an adult of the breed.

8 . The character determination system according to claim 5 , wherein the trained model of the character determining means is a trained model which performs learning using an image of an animal other than a human and a label related to a character of the animal as training data and which receives an image of an animal as input and outputs a character determination of the animal.

9 . The character determination system according to claim 5 , wherein an image of an animal received by the receiving means is an image obtained by photographing a face of the animal from front.

10 . The character determination system according to claim 5 , wherein the character includes behavioral characteristics defined in C-barq (Canine Behavioral Assessment and Research Questionnaire).

11 . The character determination system according to claim 5 , wherein the trained model has been subjected to learning using, as training data, an image of an animal and a label related to a character of the one or more specific breeds.

12 . The character determination system according to claim 3 , wherein the trained model has been subjected to learning using, as training data, an image of an animal and a label related to a character of the one or more specific breeds.

13 . The character determination system according to claim 4 , wherein the trained model has been subjected to learning using, as training data, an image of an animal and a label related to a character of the one or more specific breeds.

14 . The character determination system according to claim 6 , wherein the trained model has been subjected to learning using, as training data, an image of an animal and a label related to a character of the one or more specific breeds.

15 . A generation method of a machine learning or deep learning trained model which determines a character of an animal other than a human from an image of the animal, the generation method including: inputting an image of an animal other than a human and a label related to a character of the animal to a computer as training data and causing an artificial intelligence to learn the training data, wherein

the character includes friendliness.

16 . A character determination method, comprising:

a step of preparing an image of an animal other than a human; and

a step of inputting the image to a machine learning or deep learning trained model and outputting a character determination of the animal from the inputted image of the animal by a computer using the machine learning or deep learning trained model, wherein

the machine learning or deep learning trained model is a trained model having learned a relationship between an image of an animal other than a human and a character of the animal, and

the character includes friendliness.

17 . The case determination method according to claim 16 , wherein the trained model is a trained model which performs learning using an image of an animal other than a human and a label related to a character of the animal as training data and which receives an image of an animal as input and outputs a character determination of the animal.

18 . A character determination method, comprising:

a step of preparing an image of an animal other than a human; and

a step of breed determining a breed of an animal from an image of the animal inputted to the receiving means by a computer using a machine learning or deep learning trained model for breed determination; and

a step of outputting a character determination of the animal from the inputted image of the animal by the computer using the machine learning or deep learning trained model, where

the machine learning or deep learning trained model is a trained model having learned a relationship between an image of an animal other than a human and a character of the animal.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 29, 2024
From: INAGAKI, TSUGUNA; TAKAHASHI, HIROYUKI; OGOSHI, YOSHIE
To: ANICOM HOLDINGS, INC.
Reel/Frame 067258/0201 →
Priority Claims (1)
JP 2021-177839 · Oct 29, 2021 · national
Continuity (1)
Related Publication 20240331437A1 · Oct 3, 2024
References Cited (25)
US 9104906B2 · McVey · 2015 [cited by examiner]
US 11574430B2 · Kwon · 2023 [cited by examiner]
US 20130259332A1 · Mcvey · 2013 [cited by examiner]
US 20140316216A1 · Kojima et al. · 2014 [cited by applicant]
US 20190286910A1 · Zimmerman · 2019 [cited by examiner]
US 20210049355A1 · Choi · 2021 [cited by examiner]
US 20220054532A1 · Smith · 2022 [cited by examiner]
US 20220087229A1 · Wernimont · 2022 [cited by examiner]
US 20220104464A1 · Wernimont · 2022 [cited by examiner]
US 20220391757A1 · Komori et al. · 2022 [cited by applicant]
US 20230073738A1 · Wernimont · 2023 [cited by examiner]
US 20230092647A1 · Mundell · 2023 [cited by examiner]
JP 2006185394A · 2006 [cited by applicant]
JP 2009165416A · 2009 [cited by applicant]
JP 2014223063A · 2014 [cited by applicant]
JP 2018195099A · 2018 [cited by applicant]
JP 2019195295A · 2019 [cited by applicant]
JP 202168382A · 2021 [cited by applicant]
JP 2021136868A · 2021 [cited by applicant]
Decision to Grant a Patent issued in Japanese Patent Application No. 2021-177839, dated Nov. 30, 2022. [cited by applicant]
Furusho et al., “Social Psychology of Zoological Gardens (2): Personality traits inference of animals bred in the zoo”, DWCLA human life and science, vol. 51, 2017, pp. 1-16. [cited by applicant]
International Search Report (PCT/ISA/210) issued in PCT/JP2022/039913, dated Jan. 10, 2023. [cited by applicant]
Notice of Reasons for Refusal issued in Japanese Patent Application No. 2021-177839, dated Sep. 27, 2022. [cited by applicant]
Written Opinion of the International Searching Authority (PCT/ISA/237) issued in PCT/JP2022/039913, dated Jan. 10, 2023. [cited by applicant]
Yamashita et al., “Predicting Demographics and Personalities from Profile Images of Social Network Users using Convolutional Deep Neural Network”, Proceedings of the 30th Annual Conference of JSAI, 2016, pp. 1-4. [cited by applicant]