Method and apparatus to predict activity

• US 20070073568A1
• Filed: 03/27/2006
• Published: 03/29/2007
• Est. Priority Date: 09/28/2005
• Status: Abandoned Application

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Kobayashi, Hideki
• Application Number:
  US11/389,067
• Publication Number:
  US 20070073568A1
• Time in Patent Office: Days
• US Class Current: 705/8
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0204 Market segmentation

Abstract

An activity prediction method includes reading activity statistics data of an object group of people from a database storing activity statistic data made by consolidating activity patterns of people, and modifying the readout activity statistics data according to the object group to obtain activity occurrence provability of the object group.

Claims

1. An activity prediction method comprising:

reading activity statistics data of an object group of people from a database storing activity statistic data made by consolidating activity patterns of people; and

modifying the readout activity statistics data according to the object group to obtain activity occurrence provability of the object group.

2. The method according to claim 1, wherein the modifying includes modifying the readout activity statistic data according to constrained condition of place which is keyed in according to the object group.

3. The method according to claim 1, which further comprises reading out consumption expenditure data of the object group from a consumption expenditure database, and wherein the modifying includes modifying the activity statistic using the consumption expenditure.

4. The method according to claim 1, wherein the modifying includes modifying the activity statistic by using preference data of the object group.

5. The method according to claim 1, further comprising calculating a total of saved times for number of people in the group which are acquired by modifying activities of the object group, and allocating the total of saved times to the activities in accordance with the activity occurrence probability.

6. The method according to claim 5, wherein the allocating includes allocating an average activity time per capita to each of the activities in accordance with the activity occurrence probability until reaching the total of saved times.

7. An activity prediction method according to claim 1, wherein the modifying includes setting an occurrence probability of activities unselected by the object group to 0 and normalizing an occurrence probability of activities remaining after modification.

8. An activity prediction method according to claim 1, wherein the modifying includes modifying at least one item of the activities of the object group.

9. An activity prediction method comprising:

reading out activity statistic data of an object group of people from a database storing activity statistics data made by consolidating activity patterns of people;

modifying the readout activity statistic data according to constrained condition of consumption expenditure which is keyed in according to the object group to obtain an activity occurrence probability of the object group.

10. An activity prediction method comprising:

reading out activity statistic data of an object group of people from a database storing activity statistics data made by consolidating activity patterns of people; and

modifying the readout activity statistic data according to constrained condition of preference which is keyed in according to the object group.

11. An activity prediction apparatus comprising:

a database to store activity statistics data obtained by consolidating activity patterns of people; and

a processor to read out activity statistics data of an object group of people from the database to obtain an activity occurrence probability of the object group and modify the readout activity statistics data in accordance with a key input constrained condition of place.

12. The apparatus according to claim 11, wherein the process or modifies the activity statistics data regarding the object group by using consumption expenditure data of the object group.

13. The apparatus according to claim 11, wherein the processor calculates a total of saved times of number of people in the object group, which are generated by modifying the activities of the object group, and the total of saved times is allocated to the activities according to the activity occurrence probability.

14. The apparatus according to claim 13, wherein the processor allocates an average activity time per capita to the activities in accordance with the activity occurrence probability until reaching the total of saved times of the number of people in the group.

15. The apparatus according to claim 11, wherein the processor sets occurrence probabilities of activities unselected by the object group to 0 and normalizes occurrence probabilities of activities remaining after modification.

16. The apparatus according to claim 11, wherein the processor modifies at least one item of activities of the object group.

17. An activity prediction program stored in a computer readable medium comprising:

means for instructing a computer to read out activity statistic data of an object group of people from a database storing activity statistics data made by consolidating activity patterns of people; and

means for instructing the computer to modify the readout activity statistic data in accordance with a constrained condition of place keyed in according to the object group to determine an activity occurrence probability of the object group.

18. The program according to claim 17, wherein the means for instructing the computer to modify the readout activity statistic data includes means for instructing to modify the readout activity statistic data according to constrained condition of place which is keyed in according to the object group.

19. The program according to claim 17, which further comprises means for instructing the computer to read out consumption expenditure data of the object group from a consumption expenditure database, and wherein the means for instructing the computer to modify the readout activity statistic data includes means for instructing the computer to modify the activity statistic data using the consumption expenditure.

20. The program according to claim 17, wherein the modifying includes modifying the activity statistic data by using preference data of the object group.

21. The program according to claim 17, further comprising means for instructing the computer to calculate a total of saved times for number of people in the group which are acquired by modifying activities of the object group, and allocating the total of saved times to the activities in accordance with the activity occurrence probability.