Management system for enhanced RFID system performance

• US 20060082444A1
• Filed: 10/19/2004
• Published: 04/20/2006
• Est. Priority Date: 10/19/2004
• Status: Abandoned Application

• Current Assignee: Quake Global, Inc.
• Original Assignee: Alysis Interactive Corporation
• Inventors: Sweeney, Patrick Joseph II, Cox, Earl Dale Jr.
• Application Number:
  US10/904,020
• Publication Number:
  US 20060082444A1
• Time in Patent Office: Days
• US Class Current: 340/10.300
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G07C 2209/06 Involving synchronization or resynchronization between transmitter and receiver; reordering of codes
• CPC Class Codes: G07C 9/28 the pass enabling tracking or indicating presence

Abstract

A method and system managing radio frequency identification (RFID) systems. The system monitors the performance of systems that use RFID tags and readers. It operates at multiple levels to ensure the optimal performance of readers, tags, antennae, and the information processing systems that acquire and convey tag data. The management system may employ artificial intelligence techniques such as genetic algorithms, fuzzy logic, neural networks, Bayesian networks, support vector machines or statistical methods to develop, maintain and exploit models of RFID system behavior. By comparing the actual performance of RFIDs and related components, the management system can detect and report failures and partial failures of components. The management system may also send signals to components to enhance performance of the overall RFID system.

Claims

1. A system for managing radio frequency identification systems comprising:

one or more radio frequency identification interrogators;

a computation device;

one or more radio frequency identification tags;

and a set of logical instructions for the computation device directing it to monitor the signals generated by one or more interrogators and one or more tags to assess their performance, detect their failures, predict their failures, and send correcting signals to them to maintain operation.

2. A system according to claim 1 wherein the logical instructions determine a failure or pending failure of one or more interrogators and one or more tags by means of at least one of a neural network, fuzzy logic, a decision tree, an evolutionary algorithm, a rule-based model, a support vector machine, and a Bayesian network.

3. A system according to claim 1 wherein the signals communicated between the computation device and one or more interrogators are transmitted first to a middleware application.

4. A system according to claim 1 wherein the signals communicated between the computation device and one or more interrogators are transmitted across a data network such as the Internet.

5. A system according to claim 1 wherein the signals communicated between the computation device and one or more interrogators effect a change in the operating parameters of one or more interrogators.

6. A system according to claim 1 wherein the signals communicated between the computation device and one or more interrogators effect a change in the operating parameters of one or more tags.

7. A system according to claim 1 wherein the assessed performance is made available to human inspection via a visual display.

8. A system according to claim 1 wherein the logical instructions to send correcting signals may be selected by a human operator.

9. A system according to claim 1 wherein the correcting signals may direct actions to be carried out by a human operator.

10. A system according to claim 1 wherein the computation device is hardwired in the form of at least one of a computer system on a chip, a gate array, and an EEPROM.

11. A system according to claim 1 wherein the computation device is a computer workstation.

12. A system according to claim 1 wherein the computation device is a distributed network of computers.

13. A system according to claim 1 wherein the signals of the system are mediated by middleware for operating radio frequency identification systems.

14. A system according to claim 1 wherein a fraction of the signals of the system are communicated to middleware for operating radio frequency identification systems.

15. A system according to claim 1 wherein the logical instructions consist entirely of weights in at least one of a neural network, Bayesian network, and a support vector network.

16. A system according to claim 1 wherein the logical instructions consist in part of weights in at least one of a neural network, Bayesian network, and a support vector network.

17. A system according to claim 1 wherein the logical instructions may be directed to operate upon one or more interrogators by a human operator.

18. A system according to claim 1 wherein the logical instructions automatically detect and operate upon additional interrogators.

19. In a system for managing radio frequency identification systems including one or more radio frequency identification interrogators, a computation device, one or more radio frequency identification tags and a set of logical instructions for the computation device, a method comprising:

monitoring the signals generated by one or more interrogators and one or more tags;

assessing the performance of one or more interrogators and one or more tags;

detecting failures of one or more interrogators and one or more tags;

predicting failures in one or more interrogators and one or more tags;

and sending correcting signals to one or more interrogators and one or more tags to maintain operation.

20. A method according to claim 19 wherein assessing the performance of one or more interrogators and one or more tags is performed by comparing the series of monitored signals against that generated by autoregressive series.

21. A method according to claim 19 wherein assessing the performance of one or more interrogators and one or more tags is performed by comparing statistical indicators of the monitored signals to those previously acquired by the system.

22. A method according to claim 19 wherein assessing the performance of one or more interrogators and one or more tags is performed by feeding the monitored signals into a neural network trained on signals previously acquired by the system.

23. A method according to claim 19 wherein assessing the performance of one or more interrogators and one or more tags is performed by feeding the monitored signals into a support vector machine trained on signals previously acquired by the system.

24. A method according to claim 19 wherein assessing the performance of one or more interrogators and one or more tags is performed by feeding the monitored signals into a Bayesian network trained from signals previously acquired by the system.

25. A method according to claim 19 wherein sending correcting signals is performed by presenting information on a visual display prompting a human operator to observe and take action upon.

26. A method according to claim 19 wherein correcting signals changes the operating parameters of one or more tags.

27. A method according to claim 19 wherein correcting signals changes the operating parameters of one or more interrogators.

28. A method according to claim 19 wherein a human operator selects one or more interrogators.

29. A method according to claim 19 wherein the system automatically detects the presence of one or more interrogators.

30. A method according to claim 19 wherein the system acquires information necessary to acquire signals from one or more interrogators and one or more tags by communicating with a middleware device for radio frequency identification systems.