Cognitive auto-fill content recommendation
Embodiments for cognitively recommending auto-fill content by a processor. Communications generated from one or more applications or devices may be tracked. Auto-fill content extracted from the communications may be recommended to automatically fill into a target application. User reaction to the auto-fill content may be learned to refine user-interaction patterns on the target application or the one or more applications or devices.
1. A method for cognitively recommending auto-fill content by a processor, comprising:
tracking communications generated from one or more applications or devices;
recommending auto-fill content from the communications to automatically fill into a target application; wherein recommending the auto-fill content further includes extracting the content from a captured screenshot of the communications generated from the one or more applications or devices such that the screenshot captures a virtual image of electronic conversations between users comprising the communications, and automatically filling the auto-fill content into the target application; and
learning user reaction to the auto-fill content to refine user-interaction patterns on the target application or the one or more applications or devices.
2. The method of claim 1 , further including extracting the auto-fill content from the communications based on a plurality of contextual factors.
3. The method of claim 1 , further including:
processing the communications using natural language processing (NLP);
converting an image or video data of the communications to text data; or
converting audio data of the communications to text data.
4. The method of claim 1 , further including synchronizing one or more events of the communications based on chronological order or logical order.
5. The method of claim 1 , further including:
recommending a list of auto-fill content to enable a user to select the auto-fill content from the list of auto-fill content; and
selecting the auto-fill content from a list of auto-fill content.
6. The method of claim 1 , further including initializing a machine learning mechanism using feedback information to learn the user reaction to the auto-fill content and the user-interaction patterns.
7. The method of claim 1 , further including:
merging the communications to generate the auto-fill content for automatically filling the auto-fill content into the target application; or
inferring one or more relationships between values or extrapolating one or more new values based on a cognitive model.
8. A system for cognitively recommending auto-fill content, comprising:
one or more computers with executable instructions that when executed cause the system to:
track communications generated from one or more applications or devices;
recommend auto-fill content from the communications to automatically fill into a target application; wherein recommending the auto-fill content further includes extracting the content from a captured screenshot of the communications generated from the one or more applications or devices such that the screenshot captures a virtual image of electronic conversations between users comprising the communications, and automatically filling the auto-fill content into the target application; and
learn user reaction to the auto-fill content to refine user-interaction patterns on the target application or the one or more applications or devices.
9. The system of claim 8 , wherein the executable instructions further extract the auto-fill content from the communications based on a plurality of contextual factors.
10. The system of claim 8 , wherein the executable instructions further:
process the communications using natural language processing (NLP);
convert an image or video data of the communications to text data; or
convert audio data of the communications to text data.
11. The system of claim 8 , wherein the executable instructions further synchronize one or more events of the communications based on chronological order or logical order.
12. The system of claim 8 , wherein the executable instructions further:
recommend a list of auto-fill content to enable a user to select the auto-fill content from the list of auto-fill content; and
select the auto-fill content from a list of auto-fill content.
13. The system of claim 8 , wherein the executable instructions further initialize a machine learning mechanism using feedback information to learn the user reaction to the auto-fill content and the user-interaction patterns.
14. The system of claim 8 , wherein the executable instructions further:
merge the communications to generate the auto-fill content for automatically filling the auto-fill content into the target application; or
infer one or more relationships between values or extrapolating one or more new values based on a cognitive model.
15. A computer program product for facilitating communications of a user 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 tracks communications generated from one or more applications or devices;
an executable portion that recommends auto-fill content from the communications to automatically fill into a target application; wherein recommending the auto-fill content further includes extracting the content from a captured screenshot of the communications generated from the one or more applications or devices such that the screenshot captures a virtual image of electronic conversations between users comprising the communications, and automatically filling the auto-fill content into the target application; and
an executable portion that learns user reaction to the auto-fill content to refine user-interaction patterns on the target application or the one or more applications or devices.
16. The computer program product of claim 15 , further including an executable portion that:
extracts the auto-fill content from the communications based on a plurality of contextual factors;
merges the communications to generate the auto-fill content for automatically filling the auto-fill content into the target application; or
infers one or more relationships between values or extrapolating one or more new values based on a cognitive model.
17. The computer program product of claim 15 , further including an executable portion that:
processes the communications using natural language processing (NLP);
converts an image or video data of the communications to text data; or
converts audio data of the communications to text data.
18. The computer program product of claim 15 , further including an executable portion that synchronizes one or more events of the communications based on chronological order or logical order.
19. The computer program product of claim 15 , further including an executable portion that:
recommends a list of auto-fill content to enable a user to select the auto-fill content from the list of auto-fill content; and
selects the auto-fill content from a list of auto-fill content.
20. The computer program product of claim 15 , further including an executable portion that initializes a machine learning mechanism using feedback information to learn the user reaction to the auto-fill content and the user-interaction patterns.