RFID systems and methods for probabalistic location determination

• US 7,388,494 B2
• Filed: 12/20/2005
• Issued: 06/17/2008
• Est. Priority Date: 12/20/2005
• Status: Expired due to Fees

• Current Assignee: Pitney Bowes Incorporated (Böwe Systec AG)
• Original Assignee: Pitney Bowes Incorporated (Böwe Systec AG)
• Inventors: Campagna, Matthew J.
• Primary Examiner(s): Lee; Benjamin C.
• Assistant Examiner(s): Mehmood; Jennifer
• Application Number:
  US11/314,358
• Publication Number:
  US 20070143072A1
• Time in Patent Office: 910 Days
• Field of Search: 3405681-5729, 702/181
• US Class Current: 340/572.1
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G08B 31/00 Predictive alarm systems characterised by extrapolation or other computation using updated historic data

Abstract

Systems and methods for probabilistic determination of real world events in a sensory environment such as a predicting location in a responsive environment are described. A responsive environment system includes a set of sensors for making probabilistic observations of RFID sensor tags. The system also includes a control system employing a set of possible real world events and statistical processing system for predicting a particular real world event state based upon the sensor observations. In one configuration, a Hidden Markov Model is used for the statistical processing system and may be updated based upon the prediction of the model. The responsive environment will then launch a response based upon the predicted real world event state.

Claims

1. A method for determining a real world event in a sensory environment comprising:

receiving a series of sensory inputs;

using a present state of a statistical model to compute a most likely sequence of real world events that would produce the received series of sensory inputs;

updating the statistical model to an updated state using the computed most likely sequence of real world events; and

outputting the computed most likely sequence of real world events determining if a response includes an immediate response event; and

if the response includes an immediate response event, initiating the immediate response event before updating the statistical model.

2. The method of claim 1, further comprising:

determining an appropriate response of the sensory environment based upon the computed most likely sequence of real world events.

3. The method of claim 2, further comprising:

initiating the appropriate response.

4. The method of claim 3, wherein:

the statistical model is a Hidden Markov Model.

5. The method of claim 4, wherein:

the sensory environment includes an RFID responsive environment having a plurality of RFID tags and a plurality of RFID sensors.

6. The method of claim 5, wherein:

the series of sensory inputs includes RFID sensor data.

7. The method of claim 6, wherein:

the RFID sensor data includes an indication that at least one of the RFID tags is present in a field of view of at least one of the RFID sensors; and

the response comprises launching an application on a computer.

8. The method of claim 7, wherein:

the statistical model is updated to the updated state after ten sensory inputs.

9. The method of claim 6, wherein:

the sequence of real world events includes an item placed on a surface.

10. The method of claim 6, wherein:

the sequence of real world events includes a person entering a room.

11. The method of claim 6, wherein:

the sequence of real world events includes a person passing by a room.

12. The method of claim 4, wherein:

the Hidden Markov Model utilizes a forward-backward procedure.

13. The method of claim 4, wherein:

the Hidden Markov Model utilizes at least one definite zero transition coefficient to optimize the model.

14. The method of claim 4, wherein:

the Hidden Markov Model utilizes a controlled initial condition probability.

15. A method for determining a real world event in a sensory environment comprising:

receiving a series of sensory inputs;

using a present state of a statistical model to compute a most likely sequence of real world events that would produce the received series of sensory inputs;

updating the statistical model to an updated state using the computed most likely sequence of real world events;

outputting the computed most likely sequence of real world events;

determining an appropriate response of the sensory environment based upon the computed most likely sequence of real world events;

initiating the appropriate response, wherein;

the statistical model is a Hidden Markov Model,

the sensory environment includes an RFID responsive environment having a plurality of RFID tags and a plurality of RFID sensors,

the series of sensory inputs includes RFID sensor data,

the RFID sensor data includes an indication that at least one of the RFID tags is present in a field of view of at least one of the RFID sensors,

the response comprises launching an application on a computer,

the statistical model is updated to the updated state after ten sensory inputs; and

further comprising;

determining if the response includes an immediate response event; and

if the response includes an immediate response event, initiating the immediate response event before updating the statistical model.

16. A method for determining real world events including a location of at least one item in a sensory environment comprising:

receiving a series of sensory inputs associated with location information including at least one sensory input associated with the at least one item;

using a present state of a Hidden Markov Model to compute a most likely sequence of real world events that would produce the received series of sensory inputs;

updating the Hidden Markov Model to an updated state using the computed most likely sequence of real world events; and

outputting the computed most likely sequence of real world events comprising location information associated with the at least one item determining if a response includes an immediate response event; and

if the response includes an immediate response event, initiating the immediate response event before updating the Hidden Markov Model.

17. The method of claim 16, further comprising:

determining an appropriate response of the sensory environment based upon the computed most likely sequence of real world events.

18. The method of claim 17, further comprising:

initiating the appropriate response, wherein;

the sensory environment includes an RFID responsive environment having a plurality of RFID tags and a plurality of RFID sensors; and

the series of sensory inputs includes RFID sensor data.

19. The method of claim 16, wherein, the sequence of real world events includes a person passing by a room.