Preserving a level of confidence of authenticity of an object

• US 10,572,883 B2
• Filed: 02/17/2017
• Issued: 02/25/2020
• Est. Priority Date: 02/19/2016
• Status: Active Grant

• Current Assignee: Alitheon, Inc.
• Original Assignee: Alitheon, Inc.
• Inventors: Ross, David Justin, Withrow, Justin Lynn, Kim, David Keesu, Tocci, Mark, Land, Scot E.
• Primary Examiner(s): Tabatabai, Abolfazl
• Application Number:
  US15/436,646
• Publication Number:
  US 20170243232A1
• Time in Patent Office: 1,103 Days
• Field of Search: 382103
• CPC Class Codes: G06F 16/51 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/5838 using colour
• CPC Class Codes: G06F 16/5866 using information manually generated, e.g. tags, keywords, comments, manually generated location and time information
• CPC Class Codes: G06F 16/587 using geographical or spatial information, e.g. location
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 21/44 Program or device authentication
• CPC Class Codes: G06F 21/73 by creating or determining hardware identification, e.g. serial numbers
• CPC Class Codes: G06F 2221/2111 Location-sensitive, e.g. geographical location, GPS
• CPC Class Codes: G06Q 30/0185 Product, service or business identity fraud
• CPC Class Codes: G06V 10/44 Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
• CPC Class Codes: G06V 10/462 Salient features, e.g. scale invariant feature transforms [SIFT]
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
• CPC Class Codes: G06V 10/751 Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
• CPC Class Codes: G06V 20/80 Recognising image objects characterised by unique random patterns
• CPC Class Codes: G06V 20/95 Pattern authentication; Markers therefor; Forgery detection
• CPC Class Codes: H04L 9/3278 using physically unclonable functions [PUF]

Abstract

Apparatuses and methods associated with preserving a level of confidence of authenticity of an object are disclosed herein. In embodiments, a method includes acquiring first information corresponding to a physical object; identifying first authentication data based on the first information; initializing a database record associated with the physical object responsive to identifying the first authentication data; determining whether the first authentication data corresponds to original provenance for the physical object; in response to determining that the first authentication data does not correspond to original provenance for the physical object, acquire and store in the database record first record data including authenticity information available in association with identification of the first authentication data; in response to determining that the first authentication data does correspond to original provenance for the physical object, retaining second record data in the database record. Other embodiments may be disclosed or claimed.

Claims

1. A method, comprising:

acquiring digital image data of an image of at least a portion of a physical object;

with one or more processors;

analyzing the digital image data to form a digital fingerprint of the physical object for an authentication of the physical object, wherein the digital fingerprint is responsive to a natural structure of the physical object;

initializing a database record associated with the physical object responsive to forming the digital fingerprint;

determining whether the digital fingerprint corresponds to original provenance for the physical object;

in response to determining that the digital fingerprint does not correspond to original provenance for the physical object, acquiring and storing in the database record first data including authenticity information available in association with formation of the digital fingerprint;

in response to determining that the digital fingerprint does correspond to original provenance for the physical object, retaining second data in the database record.

2. The method of claim 1, wherein the authenticity information includes an indication of a validation of authenticity and metadata for the validation.

3. The method of claim 2, wherein the metadata includes information about a time of ascertainment of said validation.

4. The method of claim 2, wherein the metadata includes information about a location of the physical object at a time of the validation.

5. The method of claim 4, wherein the information about the location includes GPS (global positioning system) coordinates.

6. The method of claim 2, wherein the metadata includes an indicator for a type of the validation, wherein the type is selected from machine-based and expert based types.

7. The method of claim 2, wherein the metadata includes a certificate of authenticity.

8. The method of claim 7, wherein the metadata includes a digital signature on the certificate of authenticity.

9. The method of claim 2, wherein the metadata includes a source of a validation service corresponding to the validation.

10. The method of claim 2, wherein the metadata specifies at least one of machine-based analysis, laboratory analysis, or microscopy.

11. The method of claim 2, wherein the metadata includes a value taken from a label, tag, integrated material, unique identification character, code or other item present on the physical object in association with acquisition of the digital image data.

12. The method of claim 11, wherein an edge of the label, tag, integrated material, unique identification character, code, or other item appears in the image and is represented by the digital image data.

13. The method of claim 1, wherein the digital fingerprint does not rely upon or require any labels, tags, integrated materials, unique identification characters, codes or other items that were added to the physical object specifically for a purpose of identification.

14. The method of claim 1, wherein an authentication process artifact from a label, tag, integrated material, unique identification character, code, or other item appears in the image and is represented by the digital image data.

15. The method of claim 14, wherein the authentication process artifact comprises at least one of an inconsistency in a patina, wear markings, light bleach markings, or corrosion.

16. The method of claim 1, wherein an anti-counterfeiting artifact referenced by an authentication certificate included in metadata of the first data appears in the image and is represented by the digital image data.

17. The method of claim 16, wherein the anti-counterfeiting artifact comprises at least one of an inconsistency in a patina, wear markings, light bleach markings, or corrosion.

18. A method, comprising:

acquiring first information corresponding to a physical object;

with one or more processors;

identifying first authentication data based on the first information;

initializing a database record associated with the physical object responsive to identifying the first authentication data;

determining whether the first authentication data corresponds to original provenance for the physical object;

in response to determining that the first authentication data does not correspond to original provenance for the physical object, acquiring and storing in the database record first record data including authenticity information available in association with identification of the first authentication data;

in response to determining that the first authentication data does correspond to original provenance for the physical object, retaining second record data in the database record.

19. The method of claim 18, wherein the authenticity information includes an indication of a validation of authenticity and metadata for the validation.

20. The method of claim 19, wherein the metadata includes an indicator for a type of the validation, wherein the type is selected from machine-based and expert based types.