Recipient based text prediction for electronic messaging

1. A method comprising:

receiving, by a computing system, an identification of a recipient of a first electronic message being composed from a message account associated with a user;

predicting, by the computing system and based on text contained in previous electronic messages sent from the message account, first text for a body of the first electronic message, wherein predicting the first text comprises predicting the first text using a machine learning model trained based on text contained in the previous electronic messages sent from the message account that were addressed to the recipient;

outputting, for display and at a first time, the first predicted text for optional selection and insertion into the body of the first electronic message;

receiving, at a second time that is after the first time, user input to not insert the first predicted text into the body of the first electronic message;

updating, at a third time that is after the second time and based on the user input to not insert the first predicted text into the body of the first electronic message, the machine learning model to generate an updated machine learning model;

predicting, based on text contained in previous electronic messages sent from the message account and using the updated machine learning model, second predicted text for a body of a subsequent electronic message to the recipient, wherein the second predicted text is different than the first predicted text; and

outputting, for display, the second predicted text for optional selection and insertion into the body of the subsequent electronic message.

2. The method of claim 1 , wherein receiving the identification of the recipient of the electronic message being composed comprises receiving identifications of a plurality of recipients of the electronic message being composed, and wherein predicting the text comprises predicting the text based on past text used by the user in electronic messages addressed to the plurality of recipients.

3. The method of claim 1 , wherein predicting the text for the body of the electronic message comprises:

predicting, by the computing system and based on greetings contained in previous electronic messages sent from the message account, a greeting for the body of the electronic message.

4. The method of claim 3 , wherein predicting the greeting for the body of the electronic message comprises predicting, by the computing system and based on greetings contained in previous electronic messages sent from the message account that were addressed to the recipient, the greeting for the body of the electronic message.

5. The method of claim 3 , further comprising:

identifying, by the computing system and using a machine learning model, the greetings contained in the previous electronic messages sent from the message account.

6. The method of claim 3 , wherein the predicted greeting does not include a name of the recipient.

7. The method of claim 3 , wherein the predicted greeting includes one or more words other than a name or a salutation of the recipient.

8. The method of claim 3 , wherein the greeting is further predicted based on a domain of an e-mail address of the recipient.

9. The method of claim 3 , wherein outputting the predicted greeting comprises outputting the predicted greeting before receiving user input associated with composition of a body of the electronic message being composed.

10. The method of claim 1 , wherein updating the machine learning model based on the user input to not insert the first predicted text to generate an updated machine learning model comprises updating the machine learning model based on text entered in place of the first predicted text.

11. The method of claim 1 , wherein outputting the predicted text for optional selection and insertion into the body of the electronic message comprises:

outputting, in-line with user entered text and with different formatting than the user entered text, the predicted text.

12. A computing system comprising:

one or more user interface components configured to receive typed user input; and

one or more processors configured to:

receive an identification of a recipient of a first electronic message being composed from a message account associated with a user;

predict, based on text contained in previous electronic messages sent from the message account and using a machine learning model trained based on text contained in the previous electronic messages sent from the message account that were addressed to the recipient, first text for a body of the first electronic message;

output, for display and at a first time, the first predicted text for optional selection and insertion into the body of the first electronic message;

receive, at a second time that is after the first time, user input to not insert the first predicted text into the body of the first electronic message;

update, at a third time that is after the second time and based on the user input to not insert the first predicted text was selected for insertion into the body of the first electronic message, the machine learning model to generate an updated machine learning model;

predict, based on text contained in previous electronic messages sent from the message account and using the updated machine learning model, second predicted text for a body of a subsequent electronic message to the recipient, wherein the second predicted text is different than the first predicted text; and

output, for display, the second predicted text for optional selection and insertion into the body of the subsequent electronic message.

13. The computing system of claim 12 , wherein, to predict the text for the body of the electronic message, the one or more processors are configured to:

predict, based on greetings contained in previous electronic messages sent from the message account, a greeting for the body of the electronic message.

14. The computing system of claim 13 , wherein, to predict the greeting for the body of the electronic message, the one or more processors are configured to:

predict, based on greetings contained in previous electronic messages sent from the message account that were addressed to the recipient, the greeting for the body of the electronic message.

15. The computing system of claim 13 , wherein the one or more processors are further configured to:

identify, using a machine learning model, the greetings contained in the previous electronic messages sent from the message account.

16. The computing system of claim 13 , wherein the predicted greeting does not include a name of the recipient.

17. The computing system of claim 13 , wherein the predicted greeting includes one or more words other than a name or a salutation of the recipient.

18. The computing system of claim 13 , wherein the greeting is further predicted based on a domain of an e-mail address of the recipient.

19. A computer-readable storage medium storing instructions that, when executed, cause one or more processors of a computing system to:

receive an identification of a recipient of an electronic message being composed from a message account associated with a user;

predict, based on text contained in previous electronic messages sent from the message account and using a machine learning model trained based on text contained in the previous electronic messages sent from the message account that were addressed to the recipient, text for a body of the electronic message;

output, for display, the predicted text for optional selection and insertion into the body of the electronic message; and

update, based on whether or not the predicted text was selected for insertion into the body of the electronic message, the machine learning model, wherein the electronic message is a first electronic message, wherein the predicted text is first predicted text, and wherein the instructions further cause the one or more processors to:

receive user input to not insert the first predicted text into the body of the first electronic message, wherein the instructions that cause the one or more processors to update the machine learning model comprise instructions that cause the one or more processors to update the machine learning model based on the user input to not insert the first predicted text to generate an updated machine learning model;

predict, based on text contained in previous electronic messages sent from the message account and using the updated machine learning model, second predicted text for a body of a subsequent electronic message to the recipient, wherein the second predicted text is different than the first predicted text; and

output, for display, the second predicted text for optional selection and insertion into the body of the electronic message.