Pet food recommendation devices and methods

1. A method comprising:

receiving, by a web interface server, a pet image depicting a pet;

analyzing, by the web interface server using a pet image recognition model, the pet image to determine one or more pet characteristics of the pet, including:

training the pet image recognition model to identify an image characteristic based on a percentage of pixels of the pet image occupied by the pet; and

identifying the one or more pet characteristics of the pet using the trained pet image recognition model;

receiving, by the web interface server, one or more pet-related data inputs;

storing behavioral data of the pet in a historical database;

training, by the web interface server, a pet food recommendation model based on the behavioral data stored in the historical database; and

analyzing, by the web interface server using the pet food recommendation model, the pet characteristics and pet-related data inputs to generate a pet food recommendation and/or pet feeding recommendation for the pet.

2. The method of claim 1 , wherein the pet image is received from a user device.

3. The method of claim 1 , wherein analyzing the pet image further comprises:

analyzing, by the web interface server, the pet image to identify one or more image characteristics of the pet image; and

determining, by the web interface server, the one or more pet characteristics based on the image characteristics.

4. The method of claim 3 , wherein the pet characteristics comprise one or more characteristics selected from the group consisting of a breed, a breed size, a pet size, a body condition, a life stage, an activity level, a pet gender, a pet gender status, and a weight of the pet.

5. The method of claim 1 , further comprising:

filtering, by the web interface server, a list of pet food products based on at least one user preference to create a filtered list of pet food products.

6. The method of claim 5 , wherein the at least one user preference comprises at least one preference selected from the group consisting of a grain preference, protein preference, a food texture preference, a natural ingredient preference, and a shopping preference.

7. The method of claim 1 , further comprising:

calculating, by the web interface server, one or more variances selected from the group consisting of a breed size variance, a pet size variance, a body condition variance, a life stage variance, an activity level variance, and a pet weight variance.

8. The method of claim 1 , further comprising:

calculating, by the web interface server, a plurality of variances to generate a plurality of calculated variances; and

scoring, by the web interface server, a list of pet food products based on a sum of the calculated variances to identify one or more recommended pet food products.

9. The method of claim 1 , further comprising:

presenting, by the web interface server, the pet food recommendation to a user, wherein the pet food recommendation contains a plurality of pet food products;

receiving, by the web interface server, a selection from the user of a selected pet food product from the plurality of pet food products; and

generating, by the web interface server, a pet feeding recommendation based on the selected pet food product.

10. The method of claim 9 , wherein the pet feeding recommendation is generated based on a caloric density of the selected pet food product.

11. The method of claim 1 , further comprising:

training the pet image recognition model based on the behavioral data stored in the historical database.

12. A system comprising:

a processor; and

a memory storing instructions which, when executed by the processor, cause the processor to:

receive a pet image depicting a pet;

analyze, using a pet image recognition model, the pet image to determine one or more pet characteristics of the pet, including:

training the pet image recognition model to identify an image characteristic based on a percentage of pixels of the pet image occupied by the pet; and

identifying the one or more pet characteristics of the pet using the trained pet image recognition model;

receive one or more pet-related data inputs;

store behavioral data of the pet in a historical database;

train a pet food recommendation model based on the behavioral data stored in the historical database;

analyze, using the pet food recommendation model, the pet characteristics and pet related data inputs to generate a pet food recommendation and/or pet feeding recommendation for the pet.

13. The system of claim 12 , wherein the memory stores further instructions which, when executed by the processor, cause the processor to:

analyze the pet image to identify one or more image characteristics of the pet image; and

determine the one or more pet characteristics based on the image characteristics.

14. The system of claim 12 , wherein the memory stores further instructions which, when executed by the processor, cause the processor to:

filter a list of pet food products based on at least one user preference to create a filtered list of pet food products.

15. The system of claim 14 , wherein the pet characteristics comprise one or more characteristics selected from the group consisting of a breed, a breed size, a pet size, a body condition, a life stage, an activity level, a pet gender, a pet gender status, and a weight of the pet.

16. The system of claim 12 , wherein the memory stores further instructions which, when executed by the processor, cause the processor to:

calculate one or more variances selected from the group consisting of a breed size variance, a pet size variance, a body condition variance, a life stage variance, an activity level variance, and a pet weight variance.

17. The system of claim 12 , wherein the memory stores further instructions which, when executed by the processor, cause the processor to:

calculate a plurality of variances to generate a plurality of calculated variances; and

score a list of pet food products based on a sum of the calculated variances to identify one or more recommended pet food products.

18. The system of claim 12 , wherein the memory stores further instructions which, when executed by the processor, cause the processor to:

present the pet food recommendation to a user, wherein the pet food recommendation contains a plurality of pet food products;

receive a selection from the user of a selected pet food product from the plurality of pet food products; and

generate a pet feeding recommendation based on the selected pet food product.

19. The system of claim 12 , wherein the memory stores further instructions which, when executed by the processor, cause the processor to:

train the image recognition model based on the behavioral data stored in the historical database.

20. A non-transitory, computer-readable medium storing instructions which, when executed by a processor, cause the processor to:

receive a pet image depicting a pet;

analyze, using a pet image recognition model, the pet image to determine one or more pet characteristics of the pet, including:

training the pet image recognition model to identify an image characteristic based on a percentage of pixels of the pet image occupied by the pet; and

identifying the one or more pet characteristics of the pet using the trained pet image recognition model;

receive one or more pet-related data inputs;

store behavioral data of the pet in a historical database;

train a pet food recommendation model based on the behavioral data stored in the historical database; and

analyze, using the pet food recommendation model, the pet characteristics and pet-related data inputs to generate a pet food recommendation for the pet.