System for generating a unique digital fingerprint for handwoven carpets

Abstract

The present invention relates to a system for generating a unique digital fingerprint for handwoven carpets, comprising at least one data storage device in which at least one image of a specific section of the back side of a handwoven carpet is compared with the corresponding original map (digital design map). Through this comparison, the system detects human weaving errors by creating an intermediate map representing the actual woven structure. The intermediate map is produced such that the knot colors are determined according to both (a) the weaving errors detected in the finished carpet and (b) the original color codes of the digital design map. This generated intermediate map serves as the unique digital fingerprint of the carpet, enabling precise identification, authentication, and traceability of any handmade carpet based on their weaving characteristics and human error patterns.

Description

[0001] Description of the Invention

[0002] The present application claims priority to Iranian Patent Application No. 140350140003005322, filed on November, 2 2024, entitled “System for Generating a Unique Fingerprint for Handwoven Carpets,” at the Intellectual Property Center of the Organization for Registration of Deeds and Properties of Iran

[0003] Title of the Invention

[0004] System for Generating a Unique Digital Fingerprint for Handwoven Carpets

[0005] Technical Field of the Invention

[0006] The present invention relates to the authentication and identification of handwoven carpets, and more specifically, to a system and method for verifying authenticity based on unintentional and random weaving errors that naturally occur during the carpet weaving process.

[0007] The invention integrates advanced digital technologies — such as image processing, artificial intelligence (Al), and blockchain systems — to establish a unique, non-reproducible digital fingerprint for each handmade carpet.

[0008] Technical Problem and Objectives of the Invention

[0009] Technical Problem

[0010] Existing authentication methods for artistic products such as handwoven carpets primarily rely on storing product-related information on cloud servers and blockchain networks. In these methods, a dataset corresponding to each product is stored in the cloud, and scanning a tag or code reveals the stored data.

[0011] However, these systems typically lack a reliable mechanism to validate whether the product itself is genuine or a replica, or, if such a mechanism exists, the technical details of the authentication method are not disclosed.

[0012] The present invention provides a novel method for verifying the authenticity of handwoven carpets by comparing each carpet against its potential replicas or imitations.

[0013] In traditional handwoven carpets, imperfections are intrinsic indicators of authenticity. Every handmade carpet inevitably contains minor weaving errors — a flawless carpet cannot exist if woven by hand. These imperfections arise from natural human factors such as fatigue, inattention, or unintentional mistakes during the knotting process. The pattern and color selection errors occur randomly and are non-reproducible, thereby forming a unique signature of manual craftsmanship.

[0014] Conversely, carpets produced by machines or computer-controlled systems lack these imperfections, making the absence of such errors a clear sign of mechanical production / reproduction.

[0015] The invention therefore utilizes these naturally occurring and unique imperfections to generate a digital fingerprint for each handwoven carpet. This fingerprint serves as the carpet’s identity, authenticity, and distinctive characteristics.

[0016] The data are embedded into a tamper-proof electronic certificate and stored on the blockchain and can be accessed through scanning a Quick Response (QR) code with a mobile / smart phone.

[0017] In this way, the invention provides a secure, reliable, and easily accessible method for customers and stakeholders to verify and share the authenticity and provenance of handwoven carpets.

[0018] Objectives of the Invention

[0019] The primary objective of this invention is to establish and preserve the digital identity and authenticity of genuine handwoven carpets, ensuring that each carpet is uniquely identifiable and distinguishable from imitations or counterfeit products.

[0020] Specifically, the invention aims to:

[0021] • Digitally record the identity and authenticity of each handwoven carpet, together with its production history and provenance, in a verifiable blockchain ledger.

[0022] • Differentiate authentic handwoven carpets from counterfeit or copied versions by utilizing a digital fingerprint based on unique weaving imperfections.

[0023] • Prevent counterfeiting and fraudulent trading of imitation carpets that replicate authentic designs for unlawful economic gain.

[0024] • Create a trustworthy and transparent ecosystem for buyers, sellers, and collectors by integrating advanced digital technologies to guarantee authenticity.

[0025] • Enhance the cultural and economic value of authentic handwoven carpets through traceable digital certification and historical documentation.

[0026] Technical Solution Overview

[0027] To achieve these objectives, the invention integrates multiple digital technologies, including image processing, deep learning, cognitive computing, Internet of Things (loT), blockchain, distributed ledger systems, asymmetric cryptography, and mobile applications. By combining these technologies, the system generates a digital fingerprint for each handwoven carpet — based on the concept of unique weaving errors — issues a digital identity certificate, and permanently records it on the blockchain.

[0028] A review of prior patents indicates that, in the domain of digital authentication and identity verification for handwoven carpets, no comprehensive or technically robust solution has yet been proposed that can effectively eliminate the risk of forgery or duplication.

[0029] Thus, the present invention introduces a novel, integrated, and secure framework for verifying authenticity and provenance in handwoven carpets.

[0030] Background and Review of Prior Art

[0031] As mentioned earlier, in most existing authentication methods, a set of product-related data is typically stored in a cloud environment. Access to this information is provided through the scanning of a Quick Response (QR) code or a Radio Frequency Identification (RFID) tag attached to the product.

[0032] However, these systems generally lack a precise and systematic mechanism for verifying the authenticity of the physical product itself in comparison to potential counterfeit or imitation versions.

[0033] A review of relevant prior patent documents demonstrates this limitation:

[0034] U.S. Patent Application No. US 2014 / 0367463 Al (filed December 18, 2014, by PHOENIX WEAVE LLC) discloses an innovative method for encoding the supply chain and distribution process of handwoven carpets. While the system provides traceability within the supply chain, it does not offer a technical solution for authenticating the physical originality of a carpet or detecting imitations.

[0035] Korean Patent No. KR 102109538 Bl describes an art authentication system in which product information is retrieved through the scanning of an RFID tag. The system focuses on data access and registration but does not define a comparative verification method between the genuine and counterfeit products.

[0036] International Patent Application No. WO 2019 / 068893 Al (filed April 11, 2019, by IM8 DATA LIMITED) discloses a system and method for product authenticity verification in which the specifications of each product are recorded in an electronic certificate. This certificate is then converted into a QR code, and both the code and the certificate’s storage address are saved within a database and blockchain network to prevent tampering and unauthorized modification.

[0037] While this system effectively protects ownership information using blockchain technology, it does not include any mechanism or process for verifying the physical authenticity of the product itself. Distinction from the Prior Art

[0038] In summary, all of the above inventions rely primarily on data-based traceability — recording and retrieving product information from databases or blockchain systems.

[0039] None of them employ a technical method for verifying the physical originality of the product through measurable characteristics.

[0040] The present invention distinguishes itself by introducing a novel digital fingerprinting system that derives from the unique, non-reproducible weaving errors inherent in each handwoven carpet.

[0041] This fingerprint is generated through Al -based pattern analysis, registered immutably on the blockchain, and used as the core parameter for authenticity validation, providing a technical advancement beyond the capabilities of the known prior art.

[0042] Detailed Description of the Invention and Technical Solution

[0043] The present invention aims to address and overcome the limitations of existing systems and methods for the authentication of artistic or handcrafted products — particularly handwoven carpets — by developing a novel, computer-based system and method that enables precise verification of authenticity through unique digital fingerprinting.

[0044] Current systems used for artwork or carpet authentication either rely on subjective human expertise or on general image recognition methods that are unable to capture the intrinsic and random nature of handwoven flaws. These conventional systems lack the precision and reproducibility required to establish a verifiable and tamper-proof digital identity for each handmade carpet.

[0045] To solve this technical problem, the present invention provides an integrated intelligent system based on a computing device configured to register, trace, and verify the authenticity of an artistic product such as a handwoven carpet. The invention combines robotic imaging, artificial intelligence (Al), image processing, and blockchain technologies to generate and record a unique digital fingerprint corresponding to the carpet’s weaving pattern.

[0046] Overview of the System and Method

[0047] In various embodiments, the system and method of the present invention — due to their distinctive technical characteristics — can be utilized across multiple commercial and consumer domains. These include, but are not limited to:

[0048] Carpet trading businesses for authentication and provenance verification,

[0049] Domestic consumers for checking authenticity and accessing the digital genealogy of handwoven carpets, and Certification authorities for issuing verifiable digital certificates.

[0050] The system is implemented through at least one computing unit configured to:

[0051] Capture high-resolution images of the carpet’s backside using a robotic imaging device;

[0052] Compare the acquired image(s) with the original design map using a Deep Learning algorithm;

[0053] Generate an intermediate map that accounts for human weaving errors;

[0054] Identify and record the unique weaving flaws as a digital fingerprint; and Securely store this fingerprint and its ownership claim on a Blockchain ledger.

[0055] Terminology and Interpretation

[0056] Unless otherwise explicitly defined in the present description, all scientific and technical terms used herein bear the same meaning as commonly understood by a person skilled in the art to which the invention pertains.

[0057] In this application, the articles “a” or “an” are not intended to denote a numerical limitation and should be interpreted as including plural forms unless explicitly stated otherwise.

[0058] Terms such as “comprising,” “including,” or “consisting of,” as used in this specification, are non-limiting and should be construed to mean “including but not limited to.” Therefore, the components, elements, or process steps recited after such terms are merely exemplary and do not exclude other unmentioned components or steps that fall within the scope of the claimed invention.

[0059] Integration and Exemplary Embodiments

[0060] The following sections of the specification, in reference to the accompanying technical drawings (“Figure Set - Technical Maps”), describe various embodiments and configurations of the invention.

[0061] It is understood that these embodiments are presented merely as representative examples and do not limit the invention to the specific configurations illustrated.

[0062] The invention may be implemented using equivalent components, methods, or technologies, provided they fall within the scope of the claims defined herein.

[0063] As previously stated, due to inadvertent and random human errors during the weaving of a handwoven carpet, the concept of unique weaving flaws can be leveraged to generate a unique digital fingerprint for each carpet. Given the random nature of these weaving errors, it is impossible to find two handwoven carpets with exactly the same characteristics and flaws — similar to a human fingerprint.

[0064] To create this weave-based fingerprint, the initial step involves high-resolution and precise imaging of the carpet's backside using a robotic device (as depicted in Figure 4) to capture the exact pattern of the handwoven structure.

[0065] Intermediate map Generation (Core Innovation)

[0066] At least one acquired image of the carpet's backside is stored in the memory of at least one system, alongside the original map from which the carpet was woven. The original map is then compared with the acquired image of the carpet’s backside.

[0067] Using image processing and cognitive computing, an intermediate map is generated. The colors in this intermediate map are primarily derived from the original map but simultaneously account for human errors in color selection that occurred during the knotting process. The generation of the intermediate map is based on the RGB1color codes of the knots in the original map. This method introduces a novel mechanism for carpet authentication, representing an innovation that is unprecedented in the prior art.

[0068] Generation of the Intermediate Map and Fingerprint Extraction

[0069] Upon generation of the intermediate map, the algorithm compares the original digital design map of the carpet with the intermediate map. The differences between these two maps, particularly in the incorrect selection of color codes for individual knots, are identified and stored as the unique digital fingerprint of the carpet in the system’s memory.

[0070] The algorithm, implemented in Python software using deep learning techniques, produces at least one intermediate map in which the color codes are derived from the original map (digital design map), while the weaving errors present in the acquired map of the carpet are also embedded.

[0071] The intermediate map is generated through a supervised deep learning algorithm, wherein the acquired map from the back side of the carpet is compared knot-by-knot with the original digital design map.

[0072] During this comparison, the algorithm performs color correction based on the digital map as the supervising reference. This color correction process is necessary because the colors captured in the acquired map of the carpet’s backside exhibit substantial variations and cannot be directly compared to the digital design colors.

[0073] The Al algorithm developed in Python scans (or “sweeps”) both maps knot by knot, correcting the color values of the acquired map and storing the corrected data in the intermediate map.

[0074] Referring to Figure 1, as an illustrative embodiment, the color of the first knot of the acquired map labeled 101(A) — w hich has an RGB value of (89, 89, 89) — is compared with the corresponding first knot of the original design map labeled 101, which has an RGB value of.(0 ,0 ,0)

[0075] The algorithm replaces this color in the intermediate map at knot 101(B) with the corrected RGB code.(0 ,0 ,0) This learning process is successively repeated for the subsequent knots (e.g., 102 to 104). The corrected colors are then stored in the intermediate map at positions 102(B) to 104(B) respectively.

[0076] At Knot 105(A) in the acquired map, a weaving error has occurred.

[0077] Instead of the intended black color (0, 0, 0), a red color (255, 0, 0) has been woven. During the first sweep, this error is not detected because the algorithm has not yet learned the color red from the original map. Therefore, at this stage, the algorithm substitutes black (0, 0, 0) for red (255, 0, 0) in Knot 105(B) of the intermediate map.

[0078] The learning of the red color code occurs later when the algorithm reaches Knot 107. At Knots 107 and 108, the correct red color (255, 0, 0) is substituted in the intermediate map.

[0079] It is important to note that this substitution occurs during a subsequent sweep of the acquired image, rather than during the initial pass.

[0080] 1Red, Green, Blue In the second sweep, the same knot-by-knot replacement is repeated. When the algorithm revisits Knot 105(A), it recalls that the correct color for this knot should be red. Thus, it replaces the black color (0, 0, 0) with red (255, 0, 0) in Knot 105(B) of the intermediate map.

[0081] Through this iterative learning process, the algorithm generates an intermediate map where the colors of the acquired map are corrected to match the original digital map. Moreover, the weaving errors of the acquired map are also systematically corrected and recorded within this intermediate map.

[0082] After several sweeps, the algorithm produces a high-resolution and high-accuracy intermediate map, in which both color fidelity and structural alignment are achieved.

[0083] Verification of Authenticity (Referring to Figure 2)

[0084] Referring to Figure 2, the authenticity of the handwoven carpet is verified by comparing the acquired design map and the original digital design map on a knot-by-knot basis (e.g., Knot 101 with 101(A) through Knot 109 with 109(A)).

[0085] Pairs of knots with identical RGB color codes are interpreted as error-free and indicative of accurate human weaving. As illustrated, all knots from 201(B) through 209(B), except 205(B), are detected as error-free.

[0086] Only Knot 205(B), exhibiting a color difference between 105(A) (intermediate map) and 105 (original map), is identified as a human weaving error.

[0087] This process enables the system to detect and record the unique fingerprint of each handmade carpet — an unrepeatable pattern resulting from human weaving variability.

[0088] Figure 2 conceptually illustrates this unique fingerprint produced through deep learning and intermediate map generation.

[0089] Iterative Sweeping and Resolution Enhancement (Referring to Figure 3)

[0090] In practical applications, knot and color separation in the acquired map is complex, as each knot contains tens of pixels with varying shades.

[0091] Therefore, the enhanced Python algorithm performs multiple iterative comparisons — referred to as “sweeping” — between the acquired and digital maps.

[0092] In this process, sweeping begins at low resolution (larger pixels per knot) and progressively increases resolution automatically, producing finer pixel-level accuracy.

[0093] This iterative sweeping may be repeated more than 100 times until the resolution and fidelity of the intermediate map converge with the original digital map.

[0094] Referring to Figure 3, the acquired map of the carpet’s backside (302) is iteratively processed using color codes from the digital design map (304) to generate intermediate maps.

[0095] Successive sweeps produce intermediate maps with varying precision levels, such as:

[0096] First sweep intermediate map(310)

[0097] 20-sweep intermediate map(320)

[0098] 50-sweep intermediate map(350)

[0099] 100-sweep intermediate map(390)

[0100] Through this progressive enhancement, the intermediate map achieves high precision and visually accurates color correction. Blockchain Registration of Digital Ownership (Referring to Figure 6)

[0101] In one embodiment, the invention further enables ownership authentication of the handmade carpet by embedding the digital fingerprint into a blockchain-based certificate.(606)

[0102] A hash function (803) is applied to the digital certificate file stored within the system, producing a fixed-length alphanumeric hash string that is then permanently recorded on the blockchain.

[0103] Hashing is a process that converts any data input of arbitrary length into a fixed-size text string using a mathematical function. This ensures that every digital certificate, regardless of its size or content, generates a unique, tamper-proof digital signature for the corresponding carpet.

[0104] Robotic Imaging Apparatus (Referring to Figure 4)

[0105] In another embodiment, the backside scanning of the handwoven carpet can be performed by an intelligent robotic imaging device (403) as depicted in Figure 4.

[0106] The robot traverses the carpet’s surface while capturing high-resolution images using a camera (401) aligned parallel to the carpet.

[0107] The camera’s stability is maintained through dual -axis balancing arms.(402)

[0108] Preferably, a white-light projector is employed to ensure uniform illumination across the scanning area, thereby minimizing the impact of environmental light variations on the image capture quality.

[0109] Summary

[0110] Accordingly, the generated intermediate map, created by iterative learning and correction based on the original digital map and the acquired carpet image, constitutes a unique digital fingerprint of each handmade carpet.

[0111] Through this architecture, the invention provides a comprehensive, tamper-resistant authentication system for handwoven carpets, combining Al -driven image analysis with blockchain-based provenance tracking.

[0112] The solution not only verifies authenticity but also preserves the cultural and artistic heritage embodied in each handmade carpet.

[0113] Brief Description of the Drawings

[0114] The accompanying drawings illustrate one or more embodiments of the present invention by way of example and are not intended to limit the scope of the invention in any manner. It should be understood that identical reference numerals used in the drawings denote identical or functionally similar elements or process steps throughout the figures.

[0115] FIG. 1 illustrates one embodiment of the invention showing the original map (original digital carpet design map), the acquired image of the back side of the handwoven carpet, and intermediate maps generated by deep learning.

[0116] FIG. 2 illustrates the iterative process of generating intermediate maps to enhance the quality and accuracy of the intermediate map prior to comparison. FIG. 3 illustrates the comparison process between the original carpet design map (304) and the acquired image of the back side (302), resulting in the generation of intermediate maps (310, 320, 350, 390) for the purpose of detecting human weaving errors.

[0117] FIG. 4 illustrates a proposed intelligent robotic device designed for scanning the back side of the carpet.

[0118] FIG. 5 A illustrates a flowchart of the system architecture.

[0119] FIG. 5B illustrates at least one proposed method for implementing the fingerprint generation process and subsequently verifying the authenticity of the handwoven carpet. A step-by-step explanation of the proposed method is provided in the detailed description of at least one embodiment of the invention.

[0120] FIG. 6 illustrates an embodiment of the system configured to record the unique characteristics of the handwoven carpet on a blockchain. (606)

[0121] FIG. 7 illustrates the periodic registration of the physical location of the carpet on the blockchain (606) via an embedded smart chip attached to the carpet.

[0122] FIG. 8 illustrates the application of a hash function (803) to the electronic certificate of the carpet, followed by the registration of the corresponding hash string on the blockchain.(606)

[0123] FIG. 9 illustrates the installation of a Quick Response (QR) code on the back of the carpet and the display of carpet-related information on a mobile device upon scanning the QR code.

[0124] FIG. 10 illustrates the display of the authenticity certificate of the carpet and all relevant information stored on the blockchain. (606)

[0125] FIG. 11 illustrates the meaningful and inseparable linkage between the QR code and the carpet’s unique fingerprint, establishing a secure and verifiable connection between the electronic certificate and the physical handwoven carpet.

[0126] Detailed Description of at Least One Embodiment for Implementing the Invention

[0127] The operational process and the sequence of interactions between its components are illustrated in Figure 5, which depicts a total of 26 procedural steps. The process may be implemented as follows :

[0128] Step (1): Determination and Storage of Physical Location

[0129] Referring to Figure 5, the smart chip module (602) integrated with the carpet identifies the physical location where the carpet is woven. This geographic data, together with the carpet’s physical attributes, photographs, sound, and video recordings of the weaving process, is stored on a cloud computing server. (606)

[0130] This step is illustrated in Figure 6.

[0131] Furthermore, the physical location of the handwoven carpet may be periodically (e.g., monthly) registered on the blockchain (606), as shown in Figure 7.

[0132] Step (2): Creation of Digital Identity (Electronic Certificate) The operator inputs the carpet’s specifications — such as dimensions, knot density, colors, pattern, manufacturer’s name, and owner’s name — into the system.

[0133] This information is stored as an electronic certificate fde (801) on the cloud server (606). A hash function (803), represented as a character string derived from the certificate data, is also stored on the same cloud server.

[0134] This process is shown in Figure 8.

[0135] Step (3): Imaging robot / High-Resolution Scanning

[0136] A imaging robot with high-resolution camera / scanner captures the reverse side of the carpet, and the acquired file is stored on a computer along with the original digital design map, which includes the color codes.

[0137] Step (4): Generation of Intermediate Pattern Map

[0138] The acquired image file is compared to the original / digital design map. Using a deep learning algorithm implemented in Python, the system generates an intermediate pattern map of the carpet based on artificial intelligence analysis.

[0139] Step (5): Creation of Digital Fingerprint

[0140] The intermediate map is compared with the original map to extract a unique digital fingerprint of the carpet.

[0141] This fingerprint is then stored on the cloud server for subsequent verification.

[0142] Step (6): Generation of Quick Response (QR) Code

[0143] The storage address of the electronic certificate (801) created in Step (2) is converted by the desktop program into a Quick Response (QR) code. (901)

[0144] Step (7): Storing QR Access Data

[0145] The generated QR code (901) provides access to the electronic certificate file and is stored in the system’s cloud-based database.(606)

[0146] Step (8): Hash Registration on Blockchain

[0147] The hash value (803) of the electronic certificate, generated in Step (2), is recorded on the blockchain.(606)

[0148] This ensures that the contents of the electronic certificate (801) remain immutable and verifiable on the blockchain. Step (9): QR Code Printing

[0149] The QR code (901) is printed on washable fabric using a compatible printer.

[0150] Step (10): QR Code Integration with the Carpet

[0151] As shown in Figure 9, the printed QR code (901) is affixed onto the reverse side of the carpet.

[0152] Step (11): QR Code Scanning via Mobile Application

[0153] The QR code (901) is acquired by a mobile application. (902)

[0154] Step (12): Loading Digital Certificate Data

[0155] Upon scanning, the information corresponding to the carpet’s electronic certificate (801), located at the QR code address on the cloud server (606), is loaded into the mobile application. (902)

[0156] Step (13): Authenticity Verification - Blockchain Query

[0157] When the “Verify Authenticity” button is pressed, the hash value (803) recorded on the blockchain (606) is retrieved for verification.

[0158] Step (14): Hash Comparison

[0159] A new hash (803) is generated from the stored electronic certificate (801) in the database and is compared with the hash value (803) previously recorded on the blockchain. (606)

[0160] Step (15): Authenticity Confirmation

[0161] If both hash values are identical, the authenticity of the electronic certificate (801) recorded on the blockchain (606) is confirmed.

[0162] This operation is illustrated in Figures 10 and 11.

[0163] Step (16): Uploading the Carpet’s Reverse Scan

[0164] The user uploads a new scan of the reverse side of the carpet whose digital fingerprint has been previously registered. Step (17): Regeneration of Intermediate Pattern

[0165] Using a deep learning algorithm, a new intermediate map is generated based on the newly uploaded scan .(302)

[0166] Step (18): Fingerprint Comparison and Verification

[0167] The newly generated intermediate map is compared with the original intermediate map stored in the database.

[0168] If the error patterns correspond, the authenticity of the carpet is confirmed. (See Figure 3.)

[0169] Step (19): Ownership Transfer and Certificate Update

[0170] Upon sale of the carpet, the operator enters the details of the new owner.

[0171] A new electronic certificate (801) is generated and stored on the cloud server (606), along with its corresponding hash value. (803)

[0172] Step (20): Registration of Ownership Hash on Blockchain

[0173] The hash (803) of the newly created electronic certificate (801) from Step (19) is recorded on the Ethereum blockchain (606) as a second smart contract, using the administrator’s private key.

[0174] Thus, two consecutive hash values (803) are maintained sequentially on the blockchain.(606)

[0175] Access to these is provided through the unique QR code (901) and can be viewed sequentially by scrolling on the mobile device. (902)

[0176] However, scrolling access is limited to the owner, who authenticates using the private key stored in their digital wallet, while general users can only view the latest electronic certificate. (801)

[0177] Step (21): QR Code Rescanning

[0178] The QR code (901) is acquired again using the mobile device camera.

[0179] Step (22): Certificate Data Retrieval

[0180] The information of the electronic certificate (801), stored at the QR code address on the cloud server (606), is reloaded into the mobile application. (902)

[0181] Step (23): Authenticity Verification - Updated Query Upon pressing the “Verify Authenticity” key, the hash value (803) stored on the blockchain (606) is retrieved once more.

[0182] Step (24): New Hash Generation and Comparison

[0183] A new hash value (803) is generated from the electronic certificate (801) currently in the database and compared to the hash (803) recorded on the blockchain.(606)

[0184] Step (25): Final Authenticity Validation

[0185] If the two hash values are identical, the authenticity of the carpet’s electronic certificate (801) as recorded on the blockchain (606) is reconfirmed.

[0186] Step (26): Owner Authentication via Digital Wallet

[0187] The carpet owner connects to their digital wallet via the mobile device.

[0188] The system verifies and confirms the ownership identity based on this authentication.

[0189] This multi-step implementation ensures end-to-end traceability, tamper-proof verification, and secure ownership management for authentic handwoven carpets through the combined use of blockchain, loT -enabled smart chips, Al -based pattern recognition, and cloud computing infrastructure

[0190] Explicit Statement of Industrial Applicability

[0191] The present invention is designed to identify, register, and validate the authenticity and provenance of handwoven carpets, while simultaneously preventing the purchase and sale of counterfeit carpets that are imitations or replicas of genuine handwoven pieces.

[0192] In this system, the fingerprint of each carpet, along with its relevant characteristics and identifying information, is securely recorded on a blockchain. These authenticated records are then made safely and reliably accessible to potential customers.

[0193] As described above, the invention provides a highly valuable and practical industrial application for authentic handwoven carpets, particularly in enhancing their credibility, traceability, and market value. The benefits are especially evident for newly produced carpets, where the weaving process has not yet begun. The advantages of the claimed invention over prior art can be clearly and precisely summarized in six key aspects:

[0194] Verification of Origin and Place of Manufacture

[0195] The invention enables proof of the carpet’s geographic origin and production site through a smart chip module (Smart Chip - GSM / 4G) integrated with Internet of Things (loT) technology.

[0196] Authentication and Provenance through a Digital Certificate

[0197] A digital identity (electronic certificate) is provided for each carpet, containing all technical and descriptive information, together with real-time multimedia evidence — video, audio, and images — collected from the moment weaving begins.

[0198] Ownership Assertion and Transfer via Blockchain

[0199] Ownership details and transfer history are immutably stored on the blockchain. Each owner’s information is recorded as tamper-proof data. Upon sale, ownership and all historical records are transferred to the next buyer, while preserving the details of previous owners.

[0200] Emphasis on Uniqueness through a Digital Fingerprint

[0201] Each carpet is assigned a unique digital fingerprint derived from the natural human errors occurring during the weaving process, ensuring that no two carpets can ever be identical.

[0202] Value Appreciation and Historical Traceability

[0203] Handwoven carpets naturally appreciate in value over time. By providing a trustworthy, evidence -based historical record stored on a secure and immutable blockchain, this invention strengthens consumer confidence and loyalty, thereby enhancing the market value of authentic carpets.

[0204] Recognition of Each Carpet as a Unique Work of Art

[0205] The invention empowers carpet producers to register each piece as a singular, non-reproducible artistic creation — similar to a painting — through the combination of digital fingerprinting and blockchain-based ownership. As a result, no duplicate can exist worldwide, and no predefined price ceiling applies.

[0206] Consequently, the application of this invention creates substantial added value, particularly for high-end and luxury handwoven carpets.

Claims

CLAIMSClaim 1 (System and Core Method)A system for generating a unique digital fingerprint for a handwoven carpet, comprising: a data storage device configured to store a set of instructions; and at least one processor configured to execute a method of generating the unique digital fingerprint, the method comprising:• Acquiring at least one image of one or more designated portions of the back side of the handwoven carpet, where at least one image is captured by a robotic imaging device.• Creating an intermediate map by performing colour correction on at least one acquired image, the colour correction comprising: o Comparing the RGB colour code of each knot in the acquired carpet image with the RGB colour code of the corresponding knot in the original map of the carpet; o Applying the RGB colour code from the original map to the intermediate map if the acquired carpet knot's RGB colour code matches that of the corresponding knot in the original map; and o Applying the RGB colour code from the acquired carpet image to the intermediate map if the acquired carpet knot's RGB colour code differs from the corresponding knot in the original map.Claim 2 (Blockchain Storage)The system of Claim 1, wherein the above method for generating a unique digital fingerprint comprises storing on the blockchain within a cloud, the following information: the original map of the carpet, the intermediate map corresponding to the design map, the real-time physical location of the carpet, images, audio, and video records of weaving processes, and carpet characteristics including dimensions, knot density, colours, patterns, producer name, and owner’s name.Claim 3 (Location Tracking)The system of Claim 2, wherein the real-time physical location of the carpet is stored on the blockchain by means of a smart chip attached to the carpet, the smart chip comprising a Global Positioning System (GPS) module.Claim 4 (Digital Certificate)The system of Claim 2, wherein the method further comprises generating a digital certificate for the carpet to be stored on the blockchain.Claim 5 (QR Code)The system of Claim 4, wherein the digital certificate includes a Quick Response (QR) code that represents the stored information, said QR code configured to be printed and affixed to the back side of the handwoven carpet via a tag or label.

Images