Method for automatic identification and data capture

• US 20030136833A1
• Filed: 01/18/2002
• Published: 07/24/2003
• Est. Priority Date: 01/18/2002
• Status: Active Grant

• Current Assignee: Metaform Ltd. (Panasonic Holdings Corporation)
• Original Assignee: ImageID Ltd. (Panasonic Holdings Corporation)
• Inventors: Nemet, Yaron, Sali, Erez, Shniberg, Moti
• Granted Patent: US 6,801,245 B2
• Time in Patent Office: Days
• US Class Current: 235/385
• CPC Class Codes: G06K 17/00 Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations
• CPC Class Codes: G06K 19/06009 with optically detectable marking G06K19/063, G06K19/08 take precedence
• CPC Class Codes: G06K 19/06046 Constructional details
• CPC Class Codes: G06K 19/0717 the sensor being capable of sensing environmental conditions such as temperature history or pressure
• CPC Class Codes: G06K 7/1095 the scanner comprising adaptations for scanning a record carrier that is displayed on a display-screen or the like
• CPC Class Codes: G06K 7/14 using light without selection of wavelength, e.g. sensing reflected white light G06K7/10831 - G06K7/1097 take precedence

Abstract

A system and methodology for tracking objects including affixing at least one imagable identifier onto each of a multiplicity of objects to be tracked, imaging at least a portion of at least one of the multiplicity of objects at a known location to provide an at least partial image of the at least one of the multiplicity of objects, containing the at least one imagable identifier and employing the at least partial image of the object containing the at least one imagable identifier to provide an output indication of the location of the at least one of the multiplicity of objects.

Claims

1. A methodology for tracking objects comprising:

affixing at least one imagable identifier onto each of a multiplicity of objects to be tracked,

imaging at least a portion of at least one of the multiplicity of objects at a known location to provide an at least partial image of said at least one of said multiplicity of objects, containing said at least one imagable identifier; and

employing said at least partial image of said object containing said at least one imagable identifier to provide an output indication of the location of said at least one of said multiplicity of objects.

2. A methodology for tracking objects according to claim 1 and also comprising communicating at least one of said at least partial image and said output indication to a remote location.

3. A methodology according to claim 1 and wherein said affixing includes adhesively attaching said at least one imagable identifier onto a surface of each of said multiplicity of objects.

4. A methodology according to claim 1 and wherein said affixing includes molding said at least one imagable identifier onto a surface of each of said multiplicity of objects.

5. A methodology according to claim 1 and wherein said affixing includes printing said at least one imagable identifier onto a surface of each of said multiplicity of objects.

6. A methodology according to claim 1 and wherein said at least one imagable identifier comprises a multi-color identifier.

7. A methodology according to claim 1 and wherein said at least one imagable identifier comprises a multi-segment, multi-color identifier.

8. A methodology according to claim 7 and wherein said multi-segment, multi-color identifier is capable of identifying and distinguishing a plurality of objects at least equal to approximately:

Plurality of objects=(n×

(n−

1)<sup>(p−

2)</sup>×

(n−

2))/p

where

n is the number of different colors and

p is the number of segments.

9. A methodology according to claim 7 and wherein said multi-segment, multi-color identifier is capable of identifying and distinguishing a plurality of objects at least equal to approximately:

Plurality of objects=n×

(n−

1)<sup>(p−

2)</sup>×

(n−

2)

where

n is the number of different colors and

p is the number of segments.

10. A methodology according to claim 9 and wherein said multi-segment, multi-color identifier has an inherent orientation.

11. A methodology according to claim 9 and wherein said imaging comprises photographing.

12. A methodology according to claim 1 and wherein said imaging comprises imaging a plurality of said objects together within a single image.

13. A methodology according to claim 1 and wherein said imaging comprises sequentially imaging a plurality of said objects passing a given imaging location

14. A methodology according to claim 1 and wherein said at least one imagable identifier comprises a plurality of imagable identifiers arranged in at least predetermined propinquity to each other.

15. A methodology according to claim 9 and wherein said imaging comprises imaging a plurality of said objects together within a single image.

16. A methodology according to claim 1 and wherein employing said at least partial image of said object containing said at least one imagable identifier comprises extracting an identification code from said at least partial image.

17. A methodology according to claim 1 and wherein said identifier is capable of identifying and distinguishing a plurality of objects at least equal to approximately

Plurality of objects=(n×

(n−

1)<sup>(p−

2)</sup>×

(n−

2))/p

where

n is the number of different colors and

p is the number of segments.

18. A methodology according to claim 1 and wherein said multi-segment, multi-color identifier is capable of identifying and distinguishing a plurality of objects at least equal to approximately:

Plurality of objects=n×

(n−

1)<sup>(p−

2)×

(</sup>n−

2)

where

n is the number of different colors and

p is the number of segments.

19. A methodology according to claim 1 and wherein said identifier has an inherent orientation.

20. A methodology according to claim 1 and wherein said imaging comprises photographing.

44. A methodology according to claim 1 and wherein output of said imaging can be stored for future retrieval

45. A methodology according to claim 7 and wherein output of said imaging can be stored for future retrieval.

46. A methodology according to claim 9 and wherein output of said imaging can be stored for future retrieval.

21. An object tracking system comprising.

at least one imagable identifier affixed onto each of a multiplicity of objects to be tracked;

an imager, imaging at least a portion of at least one of said multiplicity of objects at a known location to provide an at least partial image of said at least one of said multiplicity of objects, containing said at least one imagable identifier; and

a processor employing said at least partial image of said object containing said at least one imagable identifier to provide an output indication of the location of said at least one of said multiplicity of objects.

22. An object tracking system according to claim 21 and also comprising a communicator communicating at least one of said at least partial image and said output indication to a remote location

23. An object tracking system according to claim 21 and wherein said affixing includes adhesively attaching said at least one imagable identifier onto a surface of each of said multiplicity of objects.

24. An object tracking system according to claim 21 and wherein said identifier is molded onto a surface of each of said multiplicity of objects.

25. An object tracking system according to claim 21 and wherein said identifier is printed onto a surface of each of said multiplicity of objects.

26. An object tracking system according to claim 21 and wherein said at least one imagable identifier comprises a multi-color identifier.

27. An object tracking system according to claim 21 and wherein said at least one imagable identifier comprises a multi-segment, multi-color identifier.

28. An object tracking system according to claim 27 and wherein said multi-segment, multi-color identifier is capable of identifying and distinguishing a plurality of objects at least equal to approximately:

Plurality of objects=(n×

(n−

1)<sup>(p−

2)</sup>×

(n−

2))/p

where

n is the number of different colors and

p is the number of segments.

29. An object tracking system according to claim 27 and wherein said multi-segment, multi-color identifier is capable of identifying and distinguishing a plurality of objects at least equal to approximately:

Plurality of objects=n×

(n−

1)<sup>(p−

2)</sup>×

(n−

2)

where

n is the number of different colors and

p is the number of segments.

30. An object tracking system according to claim 29 and wherein said multi-segment, multi-color identifier has an inherent orientation.

31. An object tracking system according to claim 29 and wherein said imaging comprises photographing.

32. An object tracking system according to claim 21 and wherein said imaging comprises imaging a plurality of said objects together within a single image.

33. An object tracking system according to claim 21 and wherein said imaging comprises sequentially imaging a plurality of said objects passing a given imaging location.

34. An object tracking system according to claim 21 and wherein said at least one imagable identifier comprises a plurality of imagable identifiers arranged in at least predetermined propinquity to each other.

35. An object tracking system according to claim 29 and wherein said imaging comprises imaging a plurality of said objects together within a single image.

36. An object tracking system according to claim 21 and wherein said processor is operative to extract an identification code from said at least partial image.

37. An object tracking system according to claim 21 and wherein said identifier is capable of identifying and distinguishing a plurality of objects at least equal to approximately

Plurality of objects=(n×

(n−

1)<sup>(p−

2)</sup>×

(n−

2))/p

where

n is the number of different colors and

p is the number of segments.

38. An object tracking system according to claim 21 and wherein said multi-segment, multi-color identifier is capable of identifying and distinguishing a plurality of objects at least equal to approximately:

Plurality of objects=n×

(n−

1)<sup>(p−

2)</sup>×

(n−

2)

where

n is the number of different colors and

p is the number of segments.

39. An object tracking system according to claim 21 and wherein said identifier has an inherent orientation.

40. An object tracking system according to claim 21 and wherein said imaging comprises photographing.

41. An object tracking system according to claim 21 and wherein output from said imager can be stored for future retrieval.

42. An object tracking system according to claim 27 and wherein output from said imager can be stored for future retrieval.

43. An object tracking system according to claim 29 and wherein output from said imager can be stored for future retrieval.