Intelligent communication message completion

1. A method for intelligent communication message completion in a computing system by a processor, comprising:

automatically completing a communication message according to a plurality of contextual factors associated with a communication dialog, wherein the plurality of contextual factors are classified as primary or secondary factors; and

utilizing the primary factors to derive the secondary factors relevant to the communication dialog, wherein the secondary factors comprise spatiotemporal characteristics observed of a current and a predicted future environment of a user initiating the communication message and are utilized to enhance or adjust context decisions constituent to automatically completing the communication message such that textual content used to automatically complete the communication message is predicted to be contextually accurate according to the observed spatiotemporal characteristics.

2. The method of claim 1 , further including determining the plurality of contextual factors to complete text input data of the communication message using a machine learning operation.

3. The method of claim 1 , wherein the primary factors include at least a user profile, a geographical location, or combination thereof and the secondary factors include at least a selected period of time, weather data, calendar data, one or more events, a defined location, or a combination thereof.

4. The method of claim 3 , further including automatically completing the communication message according to the primary factors, the secondary factors, or a combination thereof.

5. The method of claim 1 , further including defining the plurality of contextual factors to include geolocation data, a time period, calendar data, a current status of one or more parties associated with the communication dialog, travel data, physical and biometric data of the one or more parties, user preferences, semantic preferences, or a combination thereof.

6. The method of claim 1 , further including:

learning one or more user preferences for preferred text data previously used in historical communication dialogs; and

combining the one or more learned user preferences with the plurality of contextual factors for automatically completing the communication message.

7. The method of claim 1 , further including initiating a machine learning mechanism to learn and predict one or more semantic candidates to complete or correct the communication message according to the plurality of contextual factors.

8. A system for intelligent communication message completion in a computing system, comprising:

one or more computers with executable instructions that when executed cause the system to:

automatically complete a communication message according to a plurality of contextual factors associated with a communication dialog, wherein the plurality of contextual factors are classified as primary or secondary factors; and

utilize the primary factors to derive the secondary factors relevant to the communication dialog, wherein the secondary factors comprise spatiotemporal characteristics observed of a current and a predicted future environment of a user initiating the communication message and are utilized to enhance or adjust context decisions constituent to automatically completing the communication message such that textual content used to automatically complete the communication message is predicted to be contextually accurate according to the observed spatiotemporal characteristics.

9. The system of claim 8 , wherein the executable instructions further determine the plurality of contextual factors to complete text input data of the communication message using a machine learning operation.

10. The system of claim 8 , wherein the primary factors include at least a user profile, a geographical location, or combination thereof and the secondary factors include at least a selected period of time, weather data, calendar data, one or more events, a defined location, or a combination thereof.

11. The system of claim 10 , wherein the executable instructions further complete the communication message according to the primary factors, the secondary factors, or a combination thereof.

12. The system of claim 8 , wherein the executable instructions further define the plurality of contextual factors to include geolocation data, a time period, calendar data, a current status of one or more parties associated with the communication dialog, travel data, physical and biometric data of the one or more parties, user preferences, semantic preferences, or a combination thereof.

13. The system of claim 8 , wherein the executable instructions further:

learn one or more user preferences for preferred text data previously used in historical communication dialogs; and

combine the one or more learned user preferences with the plurality of contextual factors for automatically completing the communication message.

14. The system of claim 8 , wherein the executable instructions further initiate a machine learning mechanism to learn and predict one or more semantic candidates to complete or correct the communication message according to the plurality of contextual factors.

15. A computer program product for intelligent communication message completion by a processor, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:

an executable portion that automatically completes a communication message according to a plurality of contextual factors associated with a communication dialog, wherein the plurality of contextual factors are classified as primary or secondary factors; and

an executable portion that utilizes the primary factors to derive the secondary factors relevant to the communication dialog, wherein the secondary factors comprise spatiotemporal characteristics observed of a current and a predicted future environment of a user initiating the communication message and are utilized to enhance or adjust context decisions constituent to automatically completing the communication message such that textual content used to automatically complete the communication message is predicted to be contextually accurate according to the observed spatiotemporal characteristics.

16. The computer program product of claim 15 , further including an executable portion that determines the plurality of contextual factors to complete text input data of the communication message using a machine learning operation.

17. The computer program product of claim 15 , wherein the primary factors include at least a user profile, a geographical location, or combination thereof and the secondary factors include at least a selected period of time, weather data, calendar data, one or more events, a defined location, or a combination thereof; and

further including an executable portion that completes the communication message according to the primary factors, the secondary factors, or a combination thereof.

18. The computer program product of claim 15 , further including an executable portion that defines the plurality of contextual factors to include geolocation data, a time period, calendar data, a current status of one or more parties associated with the communication dialog, travel data, physical and biometric data of the one or more parties, user preferences, semantic preferences, or a combination thereof.

19. The computer program product of claim 15 , further including an executable portion that:

learns one or more user preferences for preferred text data previously used in historical communication dialogs; and

combines the one or more learned user preferences with the plurality of contextual factors for automatically completing the communication message.

20. The computer program product of claim 15 , further including an executable portion that initiates a machine learning mechanism to learn and predict one or more semantic candidates to complete or correct the communication message according to the plurality of contextual factors.