Electronic device and method for controlling same
An example method for controlling an electronic device includes detecting at least one user and acquiring user information of the detected at least one user; determining a user mode based on the acquired user information; determining a service to be provided to the detected at least one user, by inputting the user information and the determined user mode as input data to a model learned by an artificial intelligence algorithm; and providing the determined service corresponding to the user mode. A method for providing the service by the electronic device may at least partially use an artificial intelligence model learned according to at least one of machine learning, neural network, and deep learning algorithms.
1. A method for controlling an electronic device, the method comprising:
obtaining, by the electronic device, an image regarding at least one person around the electronic device;
analyzing, by the electronic device, the image to determine a number of persons in the image;
based on determining the number of persons in the image to be one person, determining, by the electronic device, a first service to be provided to the one person based on a context of the one person, and providing, by the electronic device, the first service; and
based on determining the number of persons in the image to be a plurality of persons, determining, by the electronic device, a second service to be provided to the plurality of persons based on a relationship between the plurality of persons, and providing, by the electronic device, the second service.
2. The method of claim 1 , further comprising:
obtaining the context using a first neural network model, and
obtaining the relationship between the plurality of persons using a second neural network model.
3. The method of claim 2 ,
wherein the first service is determined by inputting, as input data to the first neural network model, at least one of information of the one person, information of objects around the one person, or information of a situation around the one person.
4. The method of claim 3 , further comprising:
based on determining the number of persons in the image to be at least one user being one person, predicting next behavior of the one person based on at least one of information of the one person, information of objects around the one person, or information of a situation around the one person,
wherein the first service is determined based on the predicted next behavior of the one person.
5. The method of claim 2 , further comprising:
updating the first neural network model using, as learning data, information regarding the first service; and
updating the second neural network model using, as learning data, information regarding the second service.
6. The method of claim 1 ,
wherein the relationship between the plurality of persons comprises a level of closeness between the plurality of persons, and
wherein the second service is determined based on the level of closeness.
7. The method of claim 1 ,
further comprising:
determining a reaction to the first service or the second service;
based on the reaction being positive, providing the first service or the second service; and
based on the reaction being negative, determining another service to be provided.
8. An electronic device comprising:
a camera;
a display;
memory; and
at least one processor,
wherein the memory stores instructions which, when executed by the at least one processor individually and/or collectively, cause the electronic device to perform operations comprising:
obtaining, through the camera, an image regarding at least one person around the electronic device,
analyzing the image to determine a number of persons in the image,
based on determining the number of persons in the image to be one person, determining a first service to be provided to the one person based on a context of the one person, and providing the first service, and
based on determining the number of persons in the image to be a plurality of persons, determining a second service to be provided to the plurality of persons based on a relationship between the plurality of persons, and providing the second service.
9. The electronic device of claim 8 ,
wherein the instructions, when executed by the at least one processor individually and/or collectively, cause the electronic device to perform operations comprising:
obtaining the context using a first neural network model, and
obtaining the relationship between the plurality of persons using a second neural network model.
10. The electronic device of claim 9 ,
wherein the instructions, when executed by the at least one processor individually and/or collectively, cause the electronic device to perform operations comprising:
determining the first service by inputting, as input data to the first neural network model, at least one of information of the one person, information of objects around the one person, or information of a situation around the one person.
11. The electronic device of claim 10 ,
wherein the instructions, when executed by the at least one processor individually and/or collectively, cause the electronic device to perform operations comprising:
based on determining the number of persons in the image to be one person, predicting next behavior of the one person based on at least one of information of the one person, information of objects around the one person, or information of a situation around the one person, and
determining the first service based on the predicted next behavior of the one person.
12. The electronic device of claim 9 ,
wherein the instructions, when executed by the at least one processor individually and/or collectively, cause the electronic device to perform operations comprising:
updating the first neural network model using, as learning data, information regarding the first service, and
updating the second neural network model using, as learning data, information regarding the second service.
13. The electronic device of claim 8 ,
wherein the relationship between the plurality of persons comprises a level of closeness between the plurality of persons, and
wherein the instructions, when executed by the at least one processor individually and/or collectively, cause the electronic device to perform operations comprising determining the second service based on the level of closeness.
14. The electronic device of claim 8 ,
wherein the instructions, when executed by the at least one processor individually and/or collectively, cause the electronic device to perform operations comprising:
determining a reaction to the first service or the second service,
based on the reaction being positive, providing the first service or the second service, and
based on the reaction being negative, determining another service to be provided.
15. A non-transitory computer-readable storage medium storing instructions which, when executed by at least one processor of an electronic device, configure the at least one processor to perform operations comprising:
analyzing, by the electronic device, an image regarding at least one person around the electronic device;
based on determining a number of persons in the image to be one person, determining, by the electronic device, a first service to be provided to the one person based on a context of the one person, and providing the first service; and
based on determining the number of persons in the image to be a plurality of persons, determining a second service to be provided to the plurality of persons based on a relationship between the plurality of persons, and providing the second service.
16. The non-transitory computer-readable storage medium of claim 15 , wherein the operations further comprise:
obtaining the context using a first neural network model, and
obtaining the relationship between the plurality of persons using a second neural network model.
17. The non-transitory computer-readable storage medium of claim 16 ,
wherein the first service is determined by inputting, as input data to the first neural network model, at least one of information of the one person, information of objects around the one person, or information of a situation around the one person.
18. The non-transitory computer-readable storage medium of claim 17 , wherein the operations further comprise:
based on determining the number of persons in the image to be one person, predicting next behavior of the one person based on at least one of information of the one person, information of objects around the one person, or information of a situation around the one person,
wherein the first service is determined based on the predicted next behavior of the one person.
19. The non-transitory computer-readable storage medium of claim 15 ,
wherein the relationship between the plurality of persons comprises a level of closeness between the plurality of persons, and
wherein the second service is determined based on the level of closeness.
20. The non-transitory computer-readable storage medium of claim 15 ,
wherein the operations further comprise:
determining a reaction to the first service or the second service;
based on the reaction being positive, providing the first service or the second service; and
based on the reaction being negative, determining another service to be provided.