One or more embodiments of a content naming system provide machine-learned name suggestions to a user for naming content items. Specifically, an online content management system can train a machine-learning model to identify a naming pattern from previously stored content items corresponding to a user account of the user. The online content management system uses the machine-learning model to determine a plurality of name suggestions for naming a content item associated with the user account. One or more embodiments provide graphical elements corresponding to the name suggestions within a graphical user interface. The user can select one or more graphical elements to add the corresponding name suggestion(s) to the name of the content item.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: training, based on characteristics of a plurality of previously named content items corresponding to a user account, a machine-learning model to identify a naming pattern associated with the plurality of previously named content items; determining, by at least one processor and using the machine-learning model, a plurality of name suggestions for a content item associated with the user account; providing, for display in a graphical user interface, a plurality of graphical elements corresponding to the plurality of name suggestions for naming the content item; identifying a selected graphical element from the plurality of graphical elements; and adding a name suggestion corresponding to the selected graphical element to the name of the content item. 2. The method as recited in claim 1, wherein determining the plurality of name suggestions for the content item comprises: determining one or more characteristics of the content item; and using the machine-learning model to determine the plurality of name suggestions based on the one or more characteristics of the content item. 3. The method as recited in claim 1, further comprising: preselecting one or more graphical elements from the plurality of graphical elements; and auto-populating the name of the content item with one or more corresponding name suggestions. 4. The method as recited in claim 1, further comprising enabling, for display in the graphical user interface, a modification of the name of the content item by adding, removing, or rearranging one or more graphical elements corresponding to one or more name suggestions. 5. The method as recited in claim 1, wherein the plurality of name suggestions comprise a plurality of name segments. 6. The method as recited in claim 1, further comprising: identifying a plurality of selected graphical elements from the plurality of graphical elements; and adding a plurality of name suggestions corresponding to the plurality of selected graphical elements to the name of the content item. 7. The method as recited in claim 6, wherein adding the plurality of name suggestions corresponding to the plurality of selected graphical elements to the name of the content item comprises adding the plurality of name suggestions in an order corresponding to an order in which the plurality of selected graphical elements are selected. 8. The method as recited in claim 1, wherein: determining the plurality of name suggestions for the content item associated with the user account comprises generating, using the machine-learning model, a plurality of scores for the plurality of name suggestions; and providing the plurality of graphical elements corresponding to the plurality of name suggestions for naming the content item comprises selecting, for display in the graphical user interface and based on the plurality of scores for the plurality of name suggestions, the plurality of graphical elements. 9. The method as recited in claim 8, wherein selecting the plurality of graphical elements comprises selecting a predetermined number of graphical elements. 10. The method as recited in claim 8, wherein selecting the plurality of graphical elements comprises selecting graphical elements corresponding to name suggestions having scores that meet a predetermined threshold. 11. The method as recited in claim 1, further comprising training the machine-learning model to identify a naming pattern associated with a plurality of user accounts associated with a related group of users. 12. The method as recited in claim 1, further comprising: detecting a text input comprising one or more manually entered characters; and determining the plurality of name suggestions based on the text input and the naming pattern. 13. A system comprising: at least one processor; and a non-transitory computer readable storage medium comprising instructions that, when executed by the at least one processor, cause the system to: identify characteristics of a plurality of previously named content items corresponding to a user account; train, based on characteristics of the plurality of previously named content items corresponding to the user account, a machine-learning model to identify a naming pattern associated with the plurality of previously named content items; determine, by the at least one processor and using the machine-learning model, a name segment for a new content item associated with the user account; provide, for display in a graphical user interface, a graphical element corresponding to the name segment for naming the new content item; and add, in response to a selection of the graphical element, the name segment to the name of the new content item. 14. The system as recited in claim 13, further comprising instructions that, when executed by the at least one processor, cause the system to determine the name segment for the new content item by: determining a characteristic of the new content item; and determining, using the identified naming pattern, the name segment according to the determined characteristic of the new content item. 15. The system as recited in claim 14, further comprising instructions that, when executed by the at least one processor, cause the system to determine the name segment according to the determined characteristic of the new content item by: comparing the determined characteristic of the new content item to one or more characteristics of the plurality of previously named content items; and selecting a name segment from a plurality of possible name segments based on a similarity of the characteristic of the new content item to the one or more characteristics of the plurality of previously named content items. 16. The system as recited in claim 13, further comprising instructions that, when executed by the at least one processor, cause the system to auto-populate, using the machine-learning model, the name of the new content item with one or more name segments based on one or more characteristics of the new content item. 17. The system as recited in claim 16, further comprising instructions that, when executed by the at least one processor, cause the system to: provide, for display in the graphical user interface, an option to remove the one or more name segments auto-populating the name of the new content item; and modify the name of the new content item in response to a selection of the option to remove the one or more name segments from the name of the new content item. 18. A non-transitory computer readable storage medium comprising instructions that, when executed by at least one processor, cause a computer system to: train, based on a plurality of content items previously stored in connection with a user account, a machine-learning model to identify a naming pattern associated with the plurality of content items; determine, by the at least one processor and using the machine-learning model, a plurality of name suggestions for a content item associated with the user account; provide, for display in a graphical user interface, a plurality of graphical elements corresponding to the plurality of name suggestions for naming the content item; identify a plurality of selected graphical elements from the plurality of graphical elements; and add a plurality of name suggestions corresponding to the plurality of selected graphical elements to the name of the content item. 19. The non-transitory computer readable storage medium of claim 18, further comprising instructions that, when executed by the at least one processor, cause the computer system to: determine the plurality of name suggestions for the content item by generating, using the machine-learning model, a plurality of scores for the plurality of name suggestions; and provide the plurality of graphical elements corresponding to the plurality of name suggestions for naming the content item by selecting, for display in the graphical user interface and based on the plurality of scores for the plurality of name suggestions, a plurality of graphical elements. 20. The non-transitory computer readable storage medium of claim 18, further comprising instructions that, when executed by the at least one processor, cause the computer system to train, based on a plurality of content items for a plurality of related user accounts, a machine-learning model to identify a naming pattern associated with the plurality of related user accounts.