Systems and methods are provided for a personal entity modeling for computing devices. For example, a computing device comprises at least one processor and memory storing instructions that, when executed by the at least one processor, cause the mobile device to perform operations including identifying a personal entity in content generated for display on the mobile device, generating training examples for the personal entity from the content, and updating an embedding used to model the personal entity using the training examples. The embedding may be used to make predictions regarding the personal entity. For example, the operations may also include predicting an association between a first personal entity displayed on the computing device and a second entity based on the embedding, and providing a recommendation, to be displayed on the computing device, related to the second entity.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A mobile device comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the mobile device to: identify a personal entity in content generated for display on the mobile device, generate training examples for the personal entity from the content, and update an embedding used to model the personal entity using the training examples. 2. The mobile device of claim 1, wherein the personal entity is identified based on location in a sender or recipient field of a message. 3. The mobile device of claim 1, wherein identifying the personal entity includes: identifying an n-gram in the content; and determining that a frequency of occurrence for the n-gram in private-content applications meets or exceeds a threshold. 4. The mobile device of claim 1, wherein identifying the personal entity includes: determining whether the personal entity is present in a personal knowledge base; and assigning the personal entity an initial embedding when the personal entity does not exist in the personal knowledge base. 5. The mobile device of claim 1, wherein the content is first content and the memory further stores instructions that, when executed by the at least one processor, causes the mobile device to: identify input provided by a user of the mobile device, the input being associated with second content displayed on the mobile device; determine completions for the input, the completions including the personal entity based on the second content; rank the completions, the rank of the personal entity accounting for a score determined by the embedding for the personal entity given the second content; and provide highest ranked completions for display on the mobile device. 6. The mobile device of claim 1, wherein generating the training examples include, for a set of other personal entities identified in the content, generating a prediction between the set and the personal entity. 7. The mobile device of claim 1, wherein generating the training examples include, for public entities identified in the content, generating a prediction between the public entity and the personal entity. 8. The mobile device of claim 1, wherein the content generated for display includes content generated during a window of time. 9. A method comprising: identifying a first personal entity in content generated for display on a computing device, the first personal entity being associated with an embedding in a personal knowledge base associated with a user of the computing device; predicting an association between the first personal entity and a second entity based on the embedding; and providing a recommendation related to the second entity, the recommendation to be displayed on the computing device. 10. The method of claim 9, wherein the recommendation is an advertisement related to the second entity. 11. The method of claim 9, wherein the second entity represents a topic and recommendation is a third personal entity associated with the second entity. 12. The method of claim 11, wherein displaying the recommendation includes: suggesting sharing the content with the third personal entity. 13. The method of claim 12, wherein the content includes a message and the recommendation is to invite the third personal entity to view the message. 14. The method of claim 9, wherein the second entity represents a context and displaying the recommendation includes suggesting an action that includes the first personal entity and the context. 15. The method of claim 14, wherein the context is a time and the action is a reservation related to the first personal entity. 16. A computing system comprising: at least one processor; a display device; memory storing a personal knowledge base, the personal knowledge base modeling personal entities as embeddings; and memory storing instructions that, when executed by the at least one processor, cause the computing system to perform operations including: identifying features in content generated for display on the display device; identifying a personal entity predicted by the features using an embedding for the personal entity in the personal knowledge base; and providing a recommendation related to the personal entity, the recommendation to be displayed on the display device. 17. The system of claim 16, wherein the recommendation is an action involving the personal entity. 18. The system of claim 16, wherein the recommendation is a suggestion to share the content associated with a current screen with the personal entity. 19. The system of claim 16, wherein the content generated for display is generated by a first application and the recommendation is a suggestion to open a second application. 20. The system of claim 16, wherein the recommendation is a completion for an input control prior to receiving input from a user in the input control.