Intelligent command prediction

• US 8,627,230 B2
• Filed: 11/24/2009
• Issued: 01/07/2014
• Est. Priority Date: 11/24/2009
• Status: Active Grant

• Current Assignee: ServiceNow Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Boehler, Olivier, Cheng, Gisela C., Deedwaniya, Anuja, Gonzalez, Zamir G., Grant, Shayne M., Kotra, Jagadish B.
• Primary Examiner(s): Tan, Alvin
• Assistant Examiner(s): Yi, Rinna
• Application Number:
  US12/624,607
• Publication Number:
  US 20110126154A1
• Time in Patent Office: 1,505 Days
• Field of Search: 715/811, 715/863, 707/736, 707/E17.044, 707/722, 707/758, 707/E17.014, 707/E17.032, 707/999.006, 707/999.102, 706/52, 704/231, 704/240, 704/246, 704/250, 704/273
• US Class Current: 715/811
• CPC Class Codes: G06F 3/0237 using prediction or retrieval techniques
• CPC Class Codes: G06F 9/453 Help systems

Abstract

A method, system, and computer program product for intelligent command prediction are provided. The method includes determining a command prediction preference associated with a user from user profile data, and selecting one or more command history repositories responsive to the command prediction preference. The one or more command history repositories include command history data collected from a plurality of users and classification data associated with the plurality of users. The method also includes calculating command probabilities for commands in the command history data of the selected one or more command history repositories as a function of the classification data associated with the plurality of users in relation to the user. The method additionally includes presenting a next suggested command as a command from the command history data of the selected one or more command history repositories with a highest calculated command probability.

Claims

1. A method for intelligent command prediction, comprising:

determining a command prediction preference associated with a user from user profile data, the user profile data comprises user identification information, user preferences and a number of command suggestions desired, wherein the user preferences includes a plurality of command history repositories and a weighting associated with each repository, the plurality of command history repositories including a system grouping command history repository, rolebased command history repository and a time-based command history repository;

selecting one or more of the plurality of command history repositories responsive to the command prediction preference, the command history repositories comprising command history data collected from a plurality of users and classification data associated with the plurality of users;

calculating command probabilities for commands in the command history data of the selected command history repositories as a function of a number of times the commands were used and a function of the classification data associated with the plurality of users in relation to the user;

presenting a next suggested command as a command from the command history data of the selected command history repositories with a highest calculated command probability;

sorting commands in the time-based command history repository as time groups by work shift, workdays, weekend days, and holidays; and

filtering the commands used in calculating the command probabilities to align with one of the time groups corresponding to a time that the user is presented with the next suggested command.

2. The method of claim 1 further comprising:

reading the number of command suggestions desired associated with the user from the user profile data; and

presenting additional suggested commands in descending order from the highest calculated command probability until the number of command suggestions desired have been presented to the user.

3. The method of claim 1 wherein the command probabilities are calculated from a frequency of use of the commands as determined from the command history data of the selected command history repositories where the classification data associated with the plurality of users matches a classification associated with the user.

4. The method of claim 1 wherein the command probabilities are calculated according to the weightings, giving a greater weighting to preferred classification data of the selected command history repositories.

5. The method of claim 1 further comprising:

capturing commands input by the plurality of users; and

writing the captured commands to the one or more of the plurality of command history repositories in records associated with the plurality of users.

6. A system for intelligent command prediction, comprising:

a data storage system comprising;

user profile data comprising a user identification information, a user preference and a number of command suggestions desired, wherein the user preference includes a plurality of command history repositories and a weighting associated with each repository, the plurality of command history repositories including a system grouping command history repository, role-based command history repository and a time-based command history repository, each of the plurality of command history repositories comprising command history data collected from a plurality of uses and clarification data associated with the plurality of user;

intelligent command prediction logic configured to execute on one or more servers and communicate with the data storage system and one or more command interfaces, the intelligent command prediction logic further configured to perform;

determining a command prediction preference associated with a user from the user profile data;

selecting one or more of the plurality of command history repositories responsive to the command prediction preference;

calculating command probabilities for commands in the command history data of the selected command history repositories as a function of a number of times the commands were used and a function of the classification data associated with the plurality of users in relation to the user;

presenting a next suggested command as a command from the command history data of the selected command history repositories with a highest calculated command probability;

sorting commands in the time-based command history repository as time groups by work shift, workdays, weekend days, and holidays; and

filtering the commands used in calculating the command probabilities to align with one of the time groups corresponding to a time that the user is presented with the next suggested command.

7. The system of claim 6 wherein the intelligent command prediction logic is further configured to perform:

reading the number of command suggestions desired associated with the user from the user profile data; and

presenting additional suggested commands in descending order from the highest calculated command probability until the number of command suggestions desired have been presented to the user through one of the one or more command interfaces.

8. The system of claim 6 wherein the command probabilities are calculated from a frequency of use of the commands as determined from the command history data of the selected command history repositories where the classification data associated with the plurality of users matches a classification associated with the user.

9. The system of claim 6 wherein the command probabilities are calculated according to the weightings, giving a greater weighting to preferred classification data of the selected command history repositories.

10. A computer program product for intelligent command prediction, the computer program product comprising:

a non-transitory storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for implementing a method, the method comprising;

determining a command prediction preference associated with a user from user profile data, the user profile data comprises user identification information, user preferences and a number of command suggestions desired, wherein the user preferences includes a plurality of command history repositories and a weighting associated with each repository, the plurality of command history repositories including a system grouping command history repository, role-based command history repository and a time-based command history repository;

selecting one or more of the plurality of command history repositories responsive to the command prediction preference, the command history repositories comprising command history data collected from a plurality of users and classification data associated with the plurality of users;

calculating command probabilities for commands in the command history data of the selected command history repositories as a function of a number of times the commands were used and a function of the classification data associated with the plurality of users in relation to the user;

presenting a next suggested command as a command from the command history data of the selected command history repositories with a highest calculated command probability;

sorting commands in the time-based command history repository as time groups by work shift, workdays, weekend days, and holidays; and

filtering the commands used in calculating the command probabilities to align with one of the time groups corresponding to a time that the user is presented with the next suggested command.

11. The computer program product of claim 10 wherein the method further comprises:

reading the number of command suggestions desired associated with the user from the user profile data; and

presenting additional suggested commands in descending order from the highest calculated command probability until the number of command suggestions desired have been presented to the user.

12. The computer program product of claim 10 wherein the command probabilities are calculated from a frequency of use of the commands as determined from the command history data of the selected command history repositories where the classification data associated with the plurality of users matches a classification associated with the user.

13. The computer program product of claim 10 wherein the command probabilities are calculated according to the weightings, giving a greater weighting to preferred classification data of the selected command history repositories.

14. The computer program product of claim 10 wherein the method further comprises:

capturing commands input by the plurality of users; and

writing the captured commands to the one or more command history repositories in records associated with the plurality of users.