Tracking Potentially Lost Items Without Beacon Tags

• US 20190311274A1
• Filed: 04/05/2018
• Published: 10/10/2019
• Est. Priority Date: 04/05/2018
• Status: Active Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Byron, Donna K., Gadde, Srinavasa, Kumar, Ashok, Winkler, Timothy P.
• Application Number:
  US15/945,924
• Publication Number:
  US 20190311274A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24522 Translation of natural language queries to structured queries
• CPC Class Codes: G06F 16/909 using geographical or spatial information, e.g. location spatiotemporally dependent retrieval from the web G06F16/9537
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

An approach is provided for performing an item tracking operation. The item tracking operation includes defining a knowledge model where the knowledge model correlates usage of an item away from a parked location with other locations visited by a user. The item tracking operation also includes tracking the item when the item is removed from the parked location where the tracking includes determining when the item is moved to a particular location. The item tracking operation also includes determining whether the item is returned to the parked location and notifying the user when the item was not returned to the parked location where the notifying calculates a probability that the item was left behind at the particular location.

Claims

1. A computer-implementable method for performing an item tracking operation, comprising:

defining a knowledge model, the knowledge model correlating usage of an item away from a parked location with other locations visited by the user;

tracking the item when the item is removed from the parked location, the tracking including determine when the item is moved to a particular location;

determining whether the item is returned to the parked location; and

,

notifying the user when the item was not returned to the parked location, the notifying calculating a probability that an item was left behind at the particular location.

2. The method of claim 1, further comprising:

training the knowledge model, the training comprising tuning the probability of the user leaving the item in the particular location over time based on a particular history of the user.

3. The method of claim 1, wherein:

the knowledge model comprises a plurality of intervals, each interval of the plurality of intervals having a respective set of features.

4. The method of claim 3, wherein:

each time the user transitions between events, the tracking for the item updates an estimation of whether the item is still with user.

5. The method of claim 1, wherein:

the notifying is tunable to change an alerting sensitivity of the user.

6. The method of claim 1, further comprising:

tracking a plurality of items, each of the plurality of items having an associated item type, the tracking the plurality of items taking into account that each associated item type has different features useful when tracking each item.

7. A system comprising:

a processor;

a data bus coupled to the processor; and

a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for;

defining a knowledge model, the knowledge model correlating usage of an item away from a parked location with other locations visited by the user;

tracking the item when the item is removed from the parked location, the tracking including determine when the item is moved to a particular location;

determining whether the item is returned to the parked location; and

,

notifying the user when the item was not returned to the parked location, the notifying calculating a probability that an item was left behind at the particular location.

8. The system of claim 7, wherein the instructions executable by the processor are further configured for:

training the knowledge model, the training comprising tuning the probability of the user leaving the item in the particular location over time based on a particular history of the user.

9. The system of claim 7, wherein:

the knowledge model comprises a plurality of intervals, each interval of the plurality of intervals having a respective set of features.

10. The system of claim 9, wherein:

each time the user transitions between events, the tracking for the item updates an estimation of whether the item is still with user.

11. The system of claim 7, wherein:

the notifying is tunable to change an alerting sensitivity of the user.

12. The system of claim 7, wherein the instructions executable by the processor are further configured for:

tracking a plurality of items, each of the plurality of items having an associated item type, the tracking the plurality of items taking into account that each associated item type has different features useful when tracking each item.

13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:

defining a knowledge model, the knowledge model correlating usage of an item away from a parked location with other locations visited by the user;

tracking the item when the item is removed from the parked location, the tracking including determine when the item is moved to a particular location;

determining whether the item is returned to the parked location; and

,

notifying the user when the item was not returned to the parked location, the notifying calculating a probability that an item was left behind at the particular location.

14. The non-transitory, computer-readable storage medium of claim 13, wherein the computer executable instructions are further configured for:

training the knowledge model, the training comprising tuning the probability of the user leaving the item in the particular location over time based on a particular history of the user.

15. The non-transitory, computer-readable storage medium of claim 13, wherein:

the knowledge model comprises a plurality of intervals, each interval of the plurality of intervals having a respective set of features.

16. The non-transitory, computer-readable storage medium of claim 15, wherein:

each time the user transitions between events, the tracking for the item updates an estimation of whether the item is still with user.

17. The non-transitory, computer-readable storage medium of claim 13, wherein:

the notifying is tunable to change an alerting sensitivity of the user.

18. The non-transitory, computer-readable storage medium of claim 13, wherein the computer executable instructions are further configured for:

tracking a plurality of items, each of the plurality of items having an associated item type, the tracking the plurality of items taking into account that each associated item type has different features useful when tracking each item.

19. The non-transitory, computer-readable storage medium of claim 13, wherein:

the computer executable instructions are deployable to a client system from a server system at a remote location.

20. The non-transitory, computer-readable storage medium of claim 13, wherein:

the computer executable instructions are provided by a service provider to a user on an on-demand basis.