Preference setting in a computer system

• US 20090157702A1
• Filed: 12/13/2007
• Published: 06/18/2009
• Est. Priority Date: 12/13/2007
• Status: Active Grant

• Current Assignee: Harris Technology LLC
• Original Assignee: Harris Technology LLC
• Inventors: Harris, Scott C.
• Granted Patent: US 7,788,219 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 9/4451 User profiles; Roaming

Abstract

A profile of likelihoods of actions that will occur on a computer is stored. The computer may be likely to be off between time periods, e.g., between midnight and 7 am. Some action is taken relative to the actions that are likely to occur, before those actions actually happen.

Claims

1. A method comprising:

in a computer-based device, storing a data structure which includes information about a plurality of specific actions and a plurality of specific times, said information defining a likelihood that one of said actions will be carried out at one of said times;

using the computer based device for comparing a current time to said times in said data structure;

determining an action which is probabilistically likely to happen in the computer based device at an operation time, where said operation time includes a current time; and

taking an action on the computer based device, based on what is most likely to happen at said operation time, prior to said actually action actually happening.

2. A method as in claim 1, wherein said data structure is a feature vector that stores information indicative of likelihoods of plural different actions at plural different times.

3. A method as in claim 1, further comprising forming an individual data structure for an individual user of the computer-based device and updating said individual data structure based on actions taken by the individual user and times when those actions are taken.

4. A method as in claim 1, further comprising storing sets of information in the data structure that represents generic users and likely actions and times of actions taken by said generic users, and selecting one of said data sets as a set of information to use for the current time.

5. A method as in claim 1, wherein said data structure also includes information indicative of a time between a first action and a second action.

6. A method as in claim 1, wherein said action comprises determining that a program is likely to be started, and responsive to determining that the program is likely to be started, loading at least some part of said program into a memory prior to the program being actually started.

7. A method as in claim 1, wherein said data structure includes times that are spaced by different amounts of granularity depending on time of day, so that a first amount of granularity between times is present at a first time of day, and a second amount of granularity between times is used at a second time of day.

8. A method as in claim 1, wherein said action comprises determining a likelihood that how likely it is that the computer will be used at said operation time, automatically selecting an aggressive sleep state for a first likelihood that the computer will be used and automatically selecting a less aggressive sleep state for a second likelihood that the computer will be used.

9. A digital video recorder, comprising:

a recording part that includes a computer device that records a video program and which allows watching the video program, allows pausing the video program during recording, and rewinding the video program during recording, said recording part storing data indicative of the video program on a memory device; and

a detector, that detects an indication of activity, wherein said computer device operates to continually record during said indication of activity and automatically stops recording responsive to there being no indication of activity for a specified time period.

10. A digital video recorder as in claim 9, wherein said detector detects operation of the digital video recorder, determines a time since a last operation, and stops the recording when said time is greater than a specified amount.

11. A digital video recorder as in claim 10, wherein said computer device terminates a first part of the video recording when the time since the last operation is greater than a first amount, and stops a second part of the video recording when the time is greater than a second amount, where the second part of the video recording stops more recording than said first part.

12. A device as in claim 11, wherein said first part is a quality of the recording, so the computer device records less quality of the recording when the time since last operation is greater than said first amount.

13. A device as in claim 11 wherein said first part is a data stream from one of multiple different tuners that are simultaneously recording.

14. A device as in claim 9, wherein said detector comprises a profile indicating a likelihood that a specified action will occur at a specified time, and wherein said recording is controlled to start and stop based on said likelihood, even when said action does not occur.

15. A device as in claim 14, wherein said profile is updated based on actual actions taken on the digital video recorder.

16. A method, comprising:

storing a plurality of profiles representing different actions taken on a computer at different times, said profiles representing a likelihood that said actions will occur at each of a plurality of different times, and each of said profiles including different likelihoods for different times;

storing information about users who created said profiles;

allowing a computer user to select one of said profiles; and

based on a selected profile, taking an action on the computer based on something that is likely to happen on the computer, before that action actually happens on the computer.

17. A method as in claim 16, wherein said profiles include computer programs that are likely to be started at each time.

18. A method as in claim 16, wherein said profiles include actions on a device that are likely to be taken at each time.

19. A method as in claim 16, wherein said action comprises loading information into a memory in preparation for a computer program being started.

20. A method comprising:

During a first mode, continually recording an incoming video program;

Allowing the continually recorded video to be played while recording and to be paused while recording;

detecting an indication of activity on a device that is used to play the video; and

based on said indication, changing a characteristic of the recording.

21. A method as in claim 20, wherein said changing comprises accessing a stored profile to determine a likelihood that an action will occur, and changing said characteristic based on said likelihood, without said action actually occurring.