Systems and methods of providing content selection
Systems and methods of providing content selection are provided. For instance, one or more signals indicative of a user selection of an object displayed within a user interface can be received. Responsive to receiving the one or more signals, a content attribute associated with one or more objects displayed within the user interface can be identified. A content entity can be determined based at least in part on the content attribute and the user selection. One or more relevant actions can then be determined based at least in part on the determined content entity. Data indicative of the relevant actions can then be provided for display.
1. A computer-implemented method comprising:
receiving, by one or more computing devices, one or more signals indicative of user input selecting a particular object of a plurality of different objects displayed within a user interface; and
responsive to receiving the one or more signals:
utilizing, by the one or more computing devices, one or more machine-learning techniques to determine a content entity associated with the particular object and estimating one or more objects of the plurality of different objects intended to be selected via the user input, the content entity comprising the particular object and one or more additional objects of the plurality of different objects proximate the particular object and determined to form a cohesive entity with the particular object;
determining, by the one or more computing devices, one or more relevant actions to be performed based at least in part on the content entity;
providing, by the one or more computing devices and within the user interface, data indicative of the one or more relevant actions; and
responsive to selection of a particular relevant action of the one or more relevant actions, prompting, by the one or more computing devices, the one or more computing devices to perform the particular relevant action.
2. The computer-implemented method of claim 1 , wherein utilizing the one or more machine-learning techniques to determine the content entity comprises utilizing one or more neural networks to determine the content entity.
3. The computer-implemented method of claim 2 , wherein utilizing the one or more neural networks to determine the content entity comprises:
providing, to the one or more neural networks, data indicative of the particular object and the one or more additional objects; and
providing, by the one or more neural networks, data indicative that the particular object and the one or more additional objects form the content entity.
4. The computer-implemented method of claim 1 , wherein:
the particular object comprises an image; and
utilizing the one or more machine-learning techniques to determine the content entity comprises utilizing the one or more machine-learning techniques to determine the content entity comprises one or more items depicted in the image.
5. The computer-implemented method of claim 1 , wherein:
the particular object comprises a portion of a textual entity; and
utilizing the one or more machine-learning techniques to determine the content entity comprises utilizing the one or more machine-learning techniques to determine the content entity comprises one or more additional portions of the textual entity.
6. The computer-implemented method of claim 1 , wherein utilizing the one or more machine-learning techniques to determine the content entity comprises utilizing the one or more machine-learning techniques to determine the content entity based at least in part on one or more sizes or locations of the one or more additional objects.
7. The computer-implemented method of claim 1 , wherein utilizing the one or more machine-learning techniques to determine the content entity comprises utilizing the one or more machine-learning techniques to determine one or more of a category or classification associated with the content entity.
8. The computer-implemented method of claim 7 , wherein determining the one or more relevant actions comprises determining the one or more relevant actions based at least in part on the one or more of the category or the classification associated with the content entity.
9. The computer-implemented method of claim 1 , wherein determining the one or more relevant actions comprises determining the one or more relevant actions based at least in part on a context of the user interface.
10. The computer-implemented method of claim 9 , wherein the context is associated with one or more applications currently running on the one or more computing devices.
11. The computer-implemented method of claim 1 , comprising:
determining, by the one or more computing devices, a content attribute of the particular object; and
analyzing, by the one or more computing devices, the content attribute of the particular object in view of one or more content attributes of the one or more additional objects to determine that the particular object and the one or more additional objects form the cohesive entity.
12. The computer-implemented method of claim 1 , wherein determining the one or more relevant actions comprises utilizing one or more machine-learning techniques to determine the one or more relevant actions.
13. The computer-implemented method of claim 12 , wherein utilizing the one or more machine-learning techniques to determine the one or more relevant actions comprises utilizing one or more neural networks to determine the one or more relevant actions.
14. The computer-implemented method of claim 13 , wherein utilizing the one or more neural networks to determine the one or more relevant actions comprises:
providing, the one or more neural networks, with data indicative of the content entity and a context of the user interface; and
providing, by the one or more neural networks, data indicative of the one or more relevant actions.
15. A system comprising:
one or more processors; and
a memory storing instructions that when executed by the one or more processors cause the system to perform operations comprising:
receiving one or more signals indicative of user input selecting a particular object of a plurality of different objects displayed within a user interface; and
responsive to receiving the one or more signals:
utilizing one or more machine-learning techniques to determine a content entity associated with the particular object and estimating one or more objects of the plurality of different objects intended to be selected via the user input, the content entity comprising the particular object and one or more additional objects of the plurality of different objects proximate the particular object and determined to form a cohesive entity with the particular object;
determining one or more relevant actions to be performed based at least in part on the content entity;
providing, within the user interface, data indicative of the one or more relevant actions; and
responsive to selection of a particular relevant action of the one or more relevant actions, prompting the system to perform the particular relevant action.
16. The system of claim 15 , wherein utilizing the one or more machine-learning techniques to determine the content entity comprises utilizing one or more neural networks to determine the content entity.
17. The system of claim 16 , wherein utilizing the one or more neural networks to determine the content entity comprises:
providing, to the one or more neural networks, data indicative of the particular object and the one or more additional objects; and
providing, by the one or more neural networks, data indicative that the particular object and the one or more additional objects form the content entity.
18. One or more non-transitory computer-readable media comprising instructions that when executed by one or more computing devices cause the one or more computing devices to perform operations comprising:
receiving one or more signals indicative of user input selecting a particular object of a plurality of different objects displayed within a user interface; and
responsive to receiving the one or more signals:
utilizing one or more machine-learning techniques to determine a content entity associated with the particular object and estimating one or more objects of the plurality of different objects intended to be selected via the user input, the content entity comprising the particular object and one or more additional objects of the plurality of different objects proximate the particular object and determined to form a cohesive entity with the particular object;
determining one or more relevant actions to be performed based at least in part on the content entity;
providing, within the user interface, data indicative of the one or more relevant actions; and
responsive to selection of a particular relevant action of the one or more relevant actions, prompting the one or more computing devices to perform the particular relevant action.
19. The one or more non-transitory computer-readable media of claim 18 , wherein utilizing the one or more machine-learning techniques to determine the content entity comprises utilizing one or more neural networks to determine the content entity.
20. The one or more non-transitory computer-readable media of claim 19 , wherein utilizing the one or more neural networks to determine the content entity comprises:
providing, to the one or more neural networks, data indicative of the particular object and the one or more additional objects; and
providing, by the one or more neural networks, data indicative that the particular object and the one or more additional objects form the content entity.