Techniques and architectures manage tasks in an electronic communications environment, such as in electronic calendars, email accounts, displays, and databases. A computing system may determine a number of task-oriented actions based, at least in part, on a history of execution patterns followed by a particular user for performing particular tasks. Such a history may be generated or modified by a machine learning process. Task-oriented actions may include: prioritizing a set of tasks by using such a history in view of various parameters of each task; extracting an action, subject, and keyword from an individual task; generating a visual cue that represents various parameters of a set of tasks; and generating a productivity report that provides an analysis on the time spent by the user on different task categories.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a processor; a memory accessible by the processor; a machine learning module stored in the memory and executable by the processor to generate at least a portion of a database containing parameters representative of performance of a first task that is a particular type of task; an input port configured to receive information regarding a second task from one or more data sources, wherein the second task is the particular type of task; and a task operations module configured to set a level of priority of the second task based, at least in part, on the parameters representative of the performance of the first task. 2. The system of claim 1, wherein the task operations module includes an extractor engine configured to extract an action, a subject, and a keyword from the second task based, at least in part, on identifying attributes of the second task from the one or more data sources. 3. The system of claim 2, further comprising a graphics generator configured to generate a visual cue of the second task based, at least in part, on the action, the subject, and the keyword. 4. The system of claim 1, wherein the performance of the first task comprises a history of performance of additional tasks each being the particular type of task. 5. The system of claim 1, wherein the input port is further configured to receive task attributes of the second task from the one or more data sources, and wherein the task operations module is configured to set the level of priority of the second task based, at least in part, on the task attributes. 6. The system of claim 5, wherein the task attributes comprise parameters of task type. 7. The system of claim 1, wherein the one or more data sources comprise one or more personal databases of a user and the parameters representative of performance of the first task comprise parameters representative of performance of the user for the particular type of task. 8. The system of claim 7, wherein the parameters representative of performance of the user for the first task include a predicted behavior of the user for the first task. 9. The system of claim 1, wherein the machine learning module is further configured to use the information regarding the second task as training data. 10. The system of claim 1, wherein the task operations module is configured to categorize the second task in real time. 11. A method comprising: receiving data indicating a set of tasks for a user; based, at least in part, on the set of tasks, querying one or more data sources for information regarding each of the set of tasks; and in response to the query of the one or more data sources, receiving the information regarding each of the set of tasks from the one or more data sources; receiving a history of performance of the user for each type of task corresponding to each of the set of tasks, respectively; and identifying priority for each of the set of tasks based, at least in part, on the information regarding each of the set of tasks and the history of performance. 12. The method of claim 11, wherein the history of performance includes a user-preferred device for each of the types of tasks. 13. The method of claim 11, further comprising: applying the information regarding each of the set of tasks received from the one or more data sources as training data for a machine learning process to generate the history of performance of the user. 14. The method of claim 11, further comprising: generating a productivity report based, at least in part, on the history of performance of the user. 15. A computing device comprising: a transceiver port to receive and to transmit data; one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receive data indicating a task via the transceiver port; extract at least one of an action, a subject, and a keyword from the data indicating the task; search in a database for a history of execution of similar tasks that are similar to the task; and categorize the task based, at least in part, on the history of execution of the similar tasks and the action, the subject, or the keyword extracted from the task. 16. The computing device of claim 15, wherein the operations further comprise: receiving information regarding the task from one or more data sources; and determining importance of the task based, at least in part, on the received information. 17. The computing device of claim 16, wherein the one or more data sources include a calendar, and email account. 18. The computing device of claim 16, wherein the operations further comprise: applying the information regarding the task from the one or more data sources as training data for a machine learning process. 19. The computing device of claim 15, wherein categorizing the task is performed using a machine learning process. 20. The computing device of claim 15, further comprising: an electronic display, and wherein the operations further comprise causing an image to be displayed on the electronic display, wherein the image includes a visual representation of a productivity report of the task.