DESC:FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(See section 10, rule 13)

“SYSTEM AND METHOD FOR CREATING NON-DUPLICABLE AND UNCLONABLE UNIQUE IDENTIFIER FOR PHYSICAL OBJECTS, STORING IDENTIFIER USING VIDEO, VERIFICATION AND AUTHENTICATION WITH STORED VIDEO USING NEURAL NETWORKS”
By
Rezmo Solutions Private Limited
B2, Block-2, BSR Splendor Park Apartment, Vijaya Bank Colony Extn, Horamavu, Bangalore-560043

The following specification particularly describes the invention and the manner in which it is to be performed.

Field of the Invention

The invention relates to create non-duplicable unique identifier of physical objects using random pattern made of small plastic particles. Further, the invention captures videos of unique identifiers by taking videos of group of unique identifiers. Furthermore, the invention authenticate the objects by matching stored identifiers in video form using neural Networks

Background of the Invention

In digital era, we need develop to digital/ software solutions (Block chain applications, supply chain, track & trace, inventory management solution, anti-counterfeiting solution etc.) for physical products.
A unclonable unique identifier(UID) is required for physical objects to give these products a unique identity so that digital/ software solution for can be developed.
However, current solution such as holograms, special inks, barcodes, chemical markers, labelling and serial numbers, hidden patterns, Radio Frequency Identification (RFID) tags are not unclonable & non-duplicable.
There are few solutions available with random signatures/ finger-prints. However, these solutions require for finger prints/ signatures to be scanned and images need to be stored on server for identification. These labels or tags need to be scanned and images of labels need to be stored on server for verification. However, scanning each label and verification has issue in scalability, and also these techniques of methods can be duplicated, and many add substantial costs.

In view of the shortcomings of the prior art method and techniques, there is a need in the art for creating non-duplicable and unclonable unique identifier for objects/ records/ goods/ products, which solves the problem of scalability, ease the process of operation and also cost effective.

Summary of the Invention

In view of the above problems, it is an object of the present invention to provide a system and method for creating non-duplicable and unclonable unique identifier for physical objects for verification and authentication using neural networks.
The following is a summary of the present disclosure in order to provide a basic understanding of some features and context. This summary is not intended to identify key/critical elements of the disclosure or to delineate the scope of the disclosure. Its sole purpose is to present some concepts of the present disclosure in a simplified form as a prelude to a more detailed description that is presented later.
In one configuration, the present invention help the manufactures to track & trace fake products in the supply chain using non-cloneable unique identifier. In an another configuration, the present invention develop blockchain application using non-cloneable unique identifier. In yet another configuration, the present invention develops unadulterated supply chain and inventory management system. In an example operative embodiment, the method creates encrypted physical tags using small plastic particles, further captures videos of tag rolls at the time of printing the tags and convert them into vectors and stores the same on servers. Furthermore, the invention verifies when user sends image of tag via mobile device or web browser, where the image Tag is matched with the vector of video stored in server.
In one aspect of the present invention provides a method of creating non-duplicable and unclonable unique identifier for physical objects, and verifying the same using neural networks. The method creates a roll or sheet of security label as a combination of a base layer and at least one security element disposed on a surface of the base layer. The security element disposed are of at least one unique identifier of random patterns for verifying the authenticity of the object. The method further records a video of the patterns of the security label while creating the rolls or sheets. The recording of the patterns including imaging, measuring reflectivity, or measuring spectrum output of the pattern portion. The method further encrypts the patterns of the recorded video using auto encoder (neural networks) and storing the same in a database of a webserver. Furthermore, the method receives one or more request for authenticating of a product having the security label including the pattern, wherein the request is received as a scanned pattern image of the security label. The method further decrypts the pattern by filtering the noise from the security label, and matches the decrypted pattern of the security label with the recorded video reference pattern in association with the identifier which are stored in the database of the webserver to verify the identifier and thereby authenticating the uniqueness of the object.
In another aspect of the present invention, the invention provides a system of creating non-duplicable and unclonable unique identifier for physical objects, and verifying the same using neural networks. The system includes a controller including a memory. The system further includes an imaging device which is coupled with the controller and configured for capturing or recording of video of patterns of the security label while creating or manufacturing of the rolls or sheets. The roll or sheet of security label is a combination of a base layer and at least one security element disposed on a surface of the base layer, wherein the security element disposed are of at least one unique identifier of random patterns for verifying the authenticity of the object. The system further includes a database in operative communication with the controller configured to store the unique identifier of random patterns. The controller is configured for receiving a request for authenticating of a product having the security label including the pattern, wherein the request is received as a scanned pattern image of the security label. Further, decrypts the pattern by filtering the noise from the security label, and matches the decrypted pattern of the security label with the recorded video reference pattern in association with the identifier which are stored in the database to verify the identifier and thereby authenticating the uniqueness of the object.
It is to be understood that both the foregoing general description and the following detailed description describe various embodiments and are intended to provide an overview or framework for understanding the nature and character of the claimed subject matter. The accompanying drawings are included to provide a further understanding of the various embodiments, and are incorporated into and constitute a part of this specification. The drawings illustrate the various embodiments described herein, and together with the description serve to explain the principles and operations of the claimed subject matter.

Brief description of the drawings
The above and other aspects, features, and advantages of certain exemplary embodiments of the present invention will be more apparent from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 shows a simplified conceptual diagram illustrating use of a handheld device application to query authentication information related to an object.
FIG. 2 shows a flow chart of a method for creating non-duplicable and unclonable unique identifier for physical objects for verification and authentication in a network using neural networks, in accordance with one embodiment of the present invention.
FIG. 3 shows the block diagram showing the flow of storing of videos and verification of the tags in conjunction with FIG. 2, according to one embodiment of the present invention.
FIG. 4 illustrates one of the embodiments of the identification features.
FIG. 5 is a diagrammatic representation of a machine in the example form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed.

Detailed description of the Invention
The invention can be implemented in numerous ways, including as a method; an apparatus; a system; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor. In this specification, these implementations, or any other form that the invention may take, may be referred to as techniques. In general, the order of the steps of disclosed method may be altered within the scope of the invention. Unless stated otherwise, a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task. As used herein, the term ‘processor’ refers to one or more devices, circuits, and/or processing cores configured to process data, such as computer program instructions.
A detailed description of one or more embodiments of the invention is provided below along with accompanying figures that illustrate the principles of the invention. The invention is described in connection with such embodiments, but the invention is not limited to any embodiment. The scope of the invention is limited only by the claims and the invention encompasses numerous alternatives, modifications and equivalents. Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. These details are provided for the purpose of example and the invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured.
FIG. 1 is a simplified conceptual diagram illustrating a system for verification and authentication of unique identifier (e.g. TAGs) on physical objects using neural networks. The system as shown generally includes a video capturing device 104 which is configured to capture one or more videos of one or more tag rolls 102, a data analysis unit 110 including a database 112, one or more handheld device with a scanner (114, 116), at least one network 108 with a server 106 and a datastore (not shown). In an example operational embodiment of the system, various devices or terminals may have access over a network, for example, the Internet, cloud computing facilities/ services such as a cloud server/ datastore. The system is configured to upload all the videos of the tags captured at the time of printing the tags by the video capturing device to the server over one or more network. The handheld devices may be located at various points along a distribution chain as illustrated in Figure 1, each location scanning an object and authenticate before updating to the cloud server/ datastore. The server may be provisioned to provide tracking and/ or tracing data analysis and reporting. The server has access to a datastore which may be used to store digital information (e.g. video of tags or other and related data). The server can query or search the database for an image of the tag. The database preferably is coupled to the cloud server in some embodiments. A handheld device such as a smartphone, tablet, laptop computer or dedicated device may be configured for communications with the server to request and receive a reply or authentication report for an object of interest. This architecture is simplified and in any event is merely illustrative and not intended to be limiting.
FIG. 2 is a flow chart of creating non-duplicable and unclonable unique identifier for physical objects, and verifying the same using neural networks. At step 210, the method creates a roll or sheet of security label as a combination of a base layer and at least one security element disposed on a surface of the base layer. The security element disposed are of at least one unique identifier of random patterns for verifying the authenticity of the object. In an embodiment, the unique identifier of random patterns is encrypted physical tags which are made up of small plastic particles. In another example embodiment, the security label may have two portions where the first portion may represent a first identifier which may be Quick Response (QR) Code and the second portion may represent a second identifier which is the unique identifier of random patterns of plastic particles. In an example, the random patterns may be sprayed on the rolls or sheet of security label using a nozzle system which is synchronized with the manufacture line of the rolls/sheet. In an example embodiment, the first identifier may be an image, or a random number or a pseudorandom number, and the second identifier is a random pattern of small plastic particles.
At step 220, the method records a video of the patterns of the security label while creating the rolls or sheets, the recording of the patterns including imaging, measuring reflectivity, or measuring spectrum output of the pattern portion. In an embodiment, the recorded video of rolls or sheets are converted into vectors before storing in the database of the webserver. The detailed flow of the storing of videos is represented in Figure 3(A). The video recording of labels is executed during the printing process which may solves scalability, where the labels can be verified directly with videos using neural networks, thereby removing the cumbersome process of scanning the labels.
At 230, the method encrypting the patterns of the recorded video using auto encoder (neural networks) and storing the same in a database of a webserver. In an example embodiment, the patterns of the recorded video are encrypted and stored as an image concerning the co-ordinates, where the encrypted co-ordinates information are stored on the database of the webserver.
At step 240, the method receives one or more request for authenticating of a product having the security label including the pattern, the request is received as a scanned pattern image of the security label.
At step 250, the method decrypts the pattern by filtering the noise from the security label. In an example embodiment, the scanned pattern images are decrypted and compared with the encrypted stored image for matching the pattern co-ordinates to verify and authenticate of the object.
At step 260, the method matches the decrypted pattern of the security label with the recorded video reference pattern in association with the identifier which are stored in the database of the webserver to verify the identifier and thereby authenticating the uniqueness of the object. The verification of the authenticity of origin of the object is performed by comparison of the unique identifier pattern on the object with the electronic reproduction of the unique identifier pattern obtained from the database which was captured or recorded while creating of the roll or sheet. In an embodiment, the security labels are matched with the vector of video stored at the webserver. The process of verification is represented in detail in Figure 3(B). In an embodiment, the step of matching the pattern of the security label includes checking the first identifier for first level authenticity of the object, and further verify the second identifier for the second level authenticity of the object thereby renders the security label unique to said object.
FIG. 4 illustrates an example embodiment of the non-duplicable and unclonable unique identifier on a security label having identification features. The first identification feature “A” in this embodiment is in the form of an encoded unique barcode/data matrix serial number. The second identification feature “B” is in the form of a unique identifier of random patterns which are made up of small plastic particles. In one of the variants, this second identification feature where small plastic particles are sprayed randomly on the rolls or sheet of security label using a nozzle system which is synchronized with the manufacture line of the rolls/sheet. Upon creation of the security label, the invention records a video of the patterns of the security label while creating the rolls or sheets. In an example, the recording of the patterns including imaging, measuring reflectivity, or measuring spectrum output of the pattern portion. In use, the user captures the identification feature by taking image of security label including “A & B” by say a mobile phone and is then transmitted to the said centralized system for authentication. The verification of the authenticity of origin of the object is performed by comparison of the unique identifier pattern on the object with the electronic reproduction of the unique identifier pattern obtained from the database which was captured or recorded while creating of the roll or sheet. In an example embodiment, matching the pattern of the security label includes checking the first identifier for first level authenticity of the object, and further verify the second identifier for the second level authenticity of the object thereby renders the security label unique to said object.
FIG. 5 is a block diagram of machine in the example form of a computer system 500 within which instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.
The example computer system 500 includes a processor 502 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 504 and a static memory 506, which communicate with each other via a bus 508. The computer system 500 may further include a video display unit 510 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 500 also includes an alphanumeric input device 512 (e.g., a keyboard or a touch-sensitive display screen), a user interface (UI) navigation device 514 (e.g., a mouse), a disk drive unit 516, a signal generation device 518 (e.g., a speaker) and a network interface device 520.
The disk drive unit 516 includes a machine-readable medium 522 on which is stored one or more sets of instructions and data structures (e.g., software) 524 embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 524 may also reside, completely or at least partially, within the main memory 504 and/or within the processor 502 during execution thereof by the computer system 500, the main memory 504 and the processor 502 also constituting machine-readable media.
While the machine-readable medium 522 is shown in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions or data structures. The term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example semiconductor memory devices, e.g., Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.
The instructions 524 may further be transmitted or received over a communications network 526 using a transmission medium. The instructions 524 may be transmitted using the network interface device 520 and any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), the Internet, mobile telephone networks, Plain Old Telephone (POTS) networks, and wireless data networks (e.g., WiFi and WiMax networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. The terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting. As used in the specification and appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.
Further, in the foregoing detailed description of embodiments of the invention, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the detailed description of embodiments of the invention, with each claim standing on its own as a separate embodiment.
It is understood that the above description is intended to be illustrative, and not restrictive. It is intended to cover all alternatives, modifications and equivalents as may be included within the spirit and scope of the invention as defined in the appended claims. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the invention should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein,” respectively.

,CLAIMS:
We Claim:

1. A method of creating non-duplicable and unclonable unique identifier for physical objects, and verifying the same using neural networks, the method comprising:
creating a roll or sheet of security label as a combination of a base layer and at least one security element disposed on a surface of the base layer, wherein the security element disposed are of at least one unique identifier of random patterns for verifying the authenticity of the object;
recording a video of the patterns of the security label while creating the rolls or sheets, wherein recording of the patterns including imaging, measuring reflectivity, or measuring spectrum output of the pattern portion;
encrypting the patterns of the recorded video using auto encoder (neural networks) and storing the same in a database of a webserver;
receiving one or more request for authenticating of a product having the security label including the pattern, wherein the request is received as a scanned pattern image of the security label;
decrypting the pattern by filtering the noise from the security label; and
matching the decrypted pattern of the security label with the recorded video reference pattern in association with the identifier which are stored in the database of the webserver to verify the identifier and thereby authenticating the uniqueness of the object.

2. The method as claimed in claim 1, wherein the unique identifier of random patterns are encrypted physical tags which are made up of small plastic particles.

3. The method as claimed in claim 1, wherein the recorded video of rolls or sheets are converted into vectors before storing in the database of the webserver.

4. The method as claimed in claim 1, wherein the security labels are matched with the vector of video stored at the webserver.

5. The method as claimed in claim 1, wherein the security label has two portions, the first portion represent a first identifier which is a Quick Response (QR) Code and the second portion represents a second identifier which is the unique identifier of random patterns of plastic particles.

6. The method as claimed in claim 1 and 5, wherein the step of matching the pattern of the security label includes checking the first identifier for first level authenticity of the object, and further verify the second identifier for the second level authenticity of the object thereby renders the security label unique to said object.

7. The method as claimed in claim 5, wherein the first identifier may be an image, or a random number or a pseudorandom number, and the second identifier is a random pattern of small plastic particles.

8. The method as claimed in claim 1, wherein the verification of the authenticity of origin of the object is performed by comparison of the unique identifier pattern on the object with the electronic reproduction of the unique identifier pattern obtained from the database which was captured or recorded while creating of the roll or sheet.

9. The method as claimed in claim 1, wherein recording videos of labels during printing solves scalability, where the labels can be verified directly with videos using neural networks, thereby removing the cumbersome process of scanning the labels.

10. The method as claimed in claim 1, wherein the patterns of the recorded video are encrypted and stored as an image concerning the co-ordinates, wherein the encrypted co-ordinates information are stored on the database of the webserver.

11. The method as claimed in claim 1, wherein the scanned pattern image is decrypted and compared with the encrypted stored image for matching the pattern co-ordinates to verify and authenticate of the object.

12. A system of creating non-duplicable and unclonable unique identifier for physical objects, and verifying the same using neural networks, the system comprising:
a controller including a memory;
an imaging device coupled with the controller and configured to capture or record a video of patterns of the security label while creating or manufacturing of the rolls or sheets, wherein the roll or sheet of security label is a combination of a base layer and at least one security element disposed on a surface of the base layer, wherein the security element disposed are of at least one unique identifier of random patterns for verifying the authenticity of the object;
a database in operative communication with the controller configured to store the unique identifier of random patterns;
the controller receive a request for authenticating of a product having the security label including the pattern, wherein the request is received as a scanned pattern image of the security label;
the controller decrypt the pattern by filtering the noise from the security label; and
the controller matches the decrypted pattern of the security label with the recorded video reference pattern in association with the identifier which are stored in the database to verify the identifier and thereby authenticating the uniqueness of the object.

13. The system as claimed in claim 12, wherein the verification of the authenticity of origin of the object is performed by matching of the unique identifier pattern on the object with the electronic reproduction of the unique identifier pattern obtained from the database which was captured or recorded while creating of the roll or sheet.

14. The system as claimed in claim 12, wherein the random patterns are sprayed on the rolls or sheet of security label using a nozzle system which is synchronized with the manufacture line of the rolls/sheet.