Item information management method for grading state of ai-based real collecting item and linking with digital value

Abstract

The item information management method may include: receiving target image data including a captured image of a target item from a user terminal; extracting state feature information from the target image data, including at least one of corner damage, surface scratch, discoloration, or warping of the target item; calculating a state grade corresponding to the target item according to a predefined state determination criterion based on the state feature information; adjusting a tradable price range and an estimated sales amount of a digital item linked to the target item based on the state grade; and providing the state grade, the tradable price range, and the estimated sales amount to the user terminal by linking them to target item information corresponding to the target item.

Description

Technology Field

[0001] The present invention relates to an item information management method that grades the status of AI-based physical collectible items and links them to digital value. Background Technology

[0002] Recently, there has been an increasing demand to trade and manage physical collectible items, such as sports cards, trading cards, and limited edition physical goods, through digital platforms. On these platforms, it is becoming common practice to link physical collectible items with digital items to manage transaction history, ownership history, and price information.

[0003] However, in conventional collecting platforms, it is difficult to quantitatively evaluate the condition of physical collectible items, leading to a problem where transaction prices for identical items depend heavily on user subjectivity or seller descriptions. In particular, minor differences in condition, such as corner damage, surface scratches, discoloration, and warping, have a significant impact on the value of an item, but technology to objectively reflect these differences has not been sufficiently provided.

[0004] In some systems, methods were used where users directly entered status grades or administrators manually registered appraisal results; however, these methods had limitations, such as difficulty in processing a large volume of items and difficulty in maintaining consistency in evaluation criteria. Additionally, problems arose where different results were obtained for the same item depending on the appraiser.

[0005] Meanwhile, there have been attempts to analyze the condition of physical collectible items based on images, but most were limited to simple image comparisons or visual aids, and thus had limitations in reliably determining conditions such as warping or micro-deformation. In particular, there was a problem of false detection caused by distortion due to lighting conditions or shooting angles.

[0006] Consequently, there has been a continuous demand for technology capable of objectively and repeatably analyzing the condition of physical collectible items and directly linking the results to the transaction value of digital items. In particular, there is a need for technology that can automatically analyze the condition of an item using only image data acquired through a user terminal and calculate its digital value based on this analysis. The problem to be solved

[0007] According to the present invention, by automatically extracting condition feature information including corner damage, surface scratches, discoloration, and warping based on a photograph of a physical collectible item, the condition of the item can be objectively analyzed without user intervention.

[0008] According to the present invention, by applying various image analysis techniques such as image contours, pixel contrast change rates, reflection pattern distortion, luminance distribution, and brightness contours in stages, it is possible to precisely identify even minute damage or warping conditions that are difficult to distinguish through simple visual inspection.

[0009] According to the present invention, by calculating a state grade based on the state feature information and automatically adjusting the tradable price range and estimated sales amount of a digital item according to the state grade, a structure can be provided in which the state of a physical item is directly reflected in the digital transaction value.

[0010] According to the present invention, even for identical items, a more reasonable grade assessment is possible by calculating an item damage score by considering both the number of issues and the number of damages, thereby reflecting both scarcity and condition simultaneously. This can improve the reliability of transactions for high-value collectible items.

[0011] According to the present invention, by integrating the condition analysis, grading, and digital value linkage of physical collectible items into a single automated processing flow, it is possible to maintain consistent evaluation standards even in large-scale collecting platforms and provide the effect of reducing disputes that may arise in transactions between users.

[0012] The technical problems of the present invention are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0013] An item information management method performed by an electronic device including a processor according to an embodiment of the present invention, which grades the condition of an AI-based physical collectible item and links it with digital value, may include the following operations: the processor receiving target image data including a captured image of a target item from a user terminal; the processor extracting condition feature information from the target image data, including at least one of corner damage, surface scratch, discoloration, or warping of the target item; the processor calculating a condition grade corresponding to the target item based on the condition feature information according to a predefined condition determination criterion; the processor adjusting a tradable price range and an estimated sale amount of a digital item linked to the target item based on the condition grade; and the processor linking the condition grade, the tradable price range, and the estimated sale amount to the target item information corresponding to the target item and providing it to the user terminal.

[0014] According to one embodiment, the operation of extracting state feature information from the target image data may include the operation of the processor detecting the outer contour of the physical collecting item within the target image data and identifying a corner area based on the outer contour, and the operation of the processor determining the number of points within the corner area where the rate of change relative to a set pixel exceeds a reference value as the number of corner damages and including this in the state feature information.

[0015] According to one embodiment, the operation of extracting state feature information from the target image data may include: the operation of the processor controlling to output a plurality of mutually parallel reference straight line patterns through the screen of the user terminal before the shooting of the target item is performed, and acquiring the target image data to include an image in which the surface of the target item is shot with the reference straight line patterns reflected; the operation of the processor detecting a plurality of reflected straight lines corresponding to the reflected image of the reference straight line patterns within the target image data, and calculating a change in spacing and a change in curvature between the plurality of reflected straight lines; and the operation of the processor determining that there is bending in the target item and including it in the state feature information only when the change in spacing or the change in curvature satisfies a preset bending judgment criterion.

[0016] According to one embodiment, the operation of extracting state feature information from the target image data may include: the operation of the processor generating a luminance deviation matrix by comparing the distribution of actual received pixel-by-pixel luminance data with a virtual reference plane defined by connecting the four corner vertex coordinates of the target item when the spacing change or the curvature change does not satisfy a preset bending judgment criterion; the operation of the processor generating a brightness contour line by linearly connecting pixels having the same luminance deviation within the luminance deviation matrix; and the operation of the processor identifying a closed loop shape among the brightness contour lines, and then determining the number of target closed loop shapes whose internal area exceeds 15% of the total area of ​​the target item as the number of bends existing in the target item and including this in the state feature information.

[0017] According to one embodiment, the item information management method may include the operation of the processor identifying a number of damages including the number of corner damages, the number of surface scratches, the degree of discoloration, and the number of warping based on the state feature information; the operation of the processor identifying the number of issuances of an item identical to the target item in an item database; the operation of the processor confirming an item damage score inversely proportional to the number of damages and the number of issuances; and the operation of the processor determining the grade of the target item among a plurality of grades based on the item damage score.

[0018] The above method for managing item information is,

[0019] The above processor includes an operation to calculate the item damage score based on the following [Mathematical Formula 1], and

[0020] [Mathematical Formula 1]

[0021]

[0022] S corresponds to the item damage score, k corresponds to the score scaling constant, N_issue corresponds to the number of issues of the same item as the target item, e corresponds to the natural constant, w_i corresponds to the weight per damage item, and d_i corresponds to the damage value of the i-th damage item among a total of n damage items.

[0023] The operation of extracting the state feature information including the surface scratch mentioned above is,

[0024] The above processor identifies a surface area excluding the corner area based on the outer contour of the above target item;

[0025] The above processor identifies a linear pixel change pattern within the surface region that contrasts with a surrounding region as a scratch candidate;

[0026] The above processor performs the operation of clustering the above scratch candidate pixels, which are spatially adjacent to each other and have continuous directionality, into a single scratch candidate line segment; and

[0027] The processor includes an operation of calculating the number of separated scratch candidate line segments as the number of surface scratches present on the target item and including this in the state feature information.

[0028] The operation of extracting the state feature information including the above discoloration is,

[0029] The above processor performs the operation of obtaining reference color information corresponding to the target item from an item database;

[0030] The above processor performs the operation of dividing the surface area of ​​the target item included in the target image data into a plurality of sub-regions;

[0031] The above processor performs the operation of calculating a color deviation between an actual color value and the reference color information for each of the plurality of sub-regions; and

[0032] The processor calculates the degree of discoloration by synthesizing the distribution and deviation magnitudes of sub-regions where the color deviation satisfies preset discoloration judgment criteria, and includes the degree of discoloration in the state feature information.

[0033] The operation of calculating the degree of discoloration among the above state feature information is,

[0034] The operation of the processor controlling the target item to be illuminated through the display of the user terminal with a specific color light source that is in a complementary relationship with a reference color previously registered for the target item; and

[0035] The above processor includes an operation of analyzing pixel value fluctuations within an image captured under conditions where the specific color light source is irradiated, and determining an area where the chroma deviation from the surroundings is greater than or equal to a set threshold as a discolored area.

[0036] The operation of extracting surface scratches from the above state feature information is,

[0037] The above processor receives a plurality of angle-specific captured images synchronized with tilt information obtained from the gyro sensor of the user terminal; and

[0038] The above processor includes an operation of tracking the rate of change of light intensity of specular reflection between the plurality of angle-based captured images in pixel units, and identifying points where discontinuous luminance peaks occur only at specific angles as fine scratches.

[0039] The above method for managing item information is,

[0040] The above processor performs the operation of checking the shooting time included in the target image data received from the user terminal; and

[0041] The above processor includes an operation to block the calculation of a status grade for the target item when the shooting time exceeds a preset valid time interval.

[0042] The above method for managing item information is,

[0043] The above processor performs the operation of receiving location information along with the target image data from the user terminal; and

[0044] The above processor includes an operation of extracting state feature information only when the location information is included in a pre-registered list of allowed evaluation locations.

[0045] The above method for managing item information is,

[0046] The above processor performs the operation of analyzing the luminance distribution included in the target image data and calculating an average luminance value; and

[0047] The above processor includes an operation in which, if the average luminance value deviates from a preset luminance range, it does not perform a state grade calculation for the target item.

[0048] The operation of extracting the above state feature information is,

[0049] The above processor controls the display of the user terminal to output a white light source of maximum brightness, and acquires an image captured while the target item is placed on the display;

[0050] The above processor analyzes the transmittance distribution of a light source from the captured image and identifies linear regions with discontinuously high light transmittance compared to the periphery as interlayer separation or microcracks within the item; and

[0051] The above processor includes an operation of determining the state grade of the target item to the lowest grade in response to the detection of interlayer separation or microcracks within the item. Effects of the invention

[0052] The effects of the item information management method according to the embodiments of the present invention are described as follows.

[0053] According to the present invention, by automatically extracting condition feature information including corner damage, surface scratches, discoloration, and warping based on a photograph of a physical collectible item, the condition of the item can be objectively analyzed without user intervention.

[0054] According to the present invention, by applying various image analysis techniques such as image contours, pixel contrast change rates, reflection pattern distortion, luminance distribution, and brightness contours in stages, it is possible to precisely identify even minute damage or warping conditions that are difficult to distinguish through simple visual inspection.

[0055] According to the present invention, by calculating a state grade based on the state feature information and automatically adjusting the tradable price range and estimated sales amount of a digital item according to the state grade, a structure can be provided in which the state of a physical item is directly reflected in the digital transaction value.

[0056] According to the present invention, even for identical items, a more reasonable grade assessment is possible by calculating an item damage score by considering both the number of issues and the damage parameter (or the number of damages), thereby reflecting both scarcity and condition simultaneously. This can improve the transaction reliability of high-value collectible items.

[0057] According to the present invention, by integrating the condition analysis, grading, and digital value linkage of physical collectible items into a single automated processing flow, it is possible to maintain consistent evaluation standards even in large-scale collecting platforms and provide the effect of reducing disputes that may arise in transactions between users.

[0058] In addition, various effects that can be identified directly or indirectly through this document may be provided. Brief explanation of the drawing

[0059] FIG. 1 is a block diagram showing the components of an electronic device according to one embodiment of the present invention. FIG. 2 is a block diagram showing the components of an item information management system according to one embodiment of the present invention. FIG. 3 is a flowchart of the operation of an item information management method according to one embodiment of the present invention. FIG. 4 is a flowchart of the operation of an item information management method according to one embodiment of the present invention. In relation to the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Specific details for implementing the invention

[0060] Hereinafter, some embodiments of the present invention will be described in detail with reference to exemplary drawings. It should be noted that in assigning reference numerals to the components of each drawing, the same components are given the same reference numeral whenever possible, even if they are shown in different drawings. Furthermore, in describing the embodiments of the present invention, if it is determined that a detailed description of related known components or functions would hinder understanding of the embodiments of the present invention, such detailed description is omitted.

[0061] In describing the components of the embodiments of the present invention, terms such as first, second, A, B, (a), (b), etc., may be used. These terms are intended merely to distinguish the components from other components, and the essence, order, or sequence of the components is not limited by the terms. Furthermore, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0062] Hereinafter, embodiments of the present invention will be described in detail with reference to FIGS. 1 to 4.

[0064] FIG. 1 is a block diagram showing the components of an electronic device according to one embodiment of the present invention.

[0065] According to one embodiment, the electronic device (100) may include a memory (110), a processor (120), a communication interface (130), and / or a display device (140). The configuration of the electronic device (100) shown in FIG. 1 is exemplary and the embodiments of the present invention are not limited thereto. For example, the electronic device (100) may further include components not shown in FIG. 1 (e.g., a web crawler, a user interface, an input device, a notification unit, a sensor unit, or at least one of any combination thereof).

[0066] According to one embodiment, the memory (110) may store instructions or data. For example, the memory (110) may store one or more instructions that cause the electronic device (100) to perform various operations when executed by the processor (120).

[0067] For example, the memory (110) may be implemented as a single chipset with the processor (120). The processor (120) may include at least one of a communication processor or a modem.

[0068] For example, the memory (110) can store various information related to the electronic device (100). For example, the memory (110) can store information regarding the operation history of the processor (120). For example, the memory (110) can store input data acquired by the electronic device (100), output data output by the electronic device (100), data acquired from an external server, a wearable device, a plurality of cameras and / or user terminals, etc.

[0069] For example, the memory (110) may include multiple storage devices of different types. For example, the memory (110) may include volatile and / or non-volatile storage media. For example, the memory (110) may include at least one of RAM (random-access memory), ROM (read only memory), eMMC (Embedded Multi-Media Card), or any combination thereof.

[0070] The steps of the method or algorithm described in connection with the embodiments disclosed in this specification may be directly implemented in hardware, software modules, or a combination of both, executed by the processor (120). The software modules may reside in a storage medium (i.e., memory (110)) such as RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, or a CD-ROM.

[0071] For example, the memory (110) is coupled to a processor (120), and the processor (120) can read information from a storage medium and write information to a storage medium. Alternatively, the memory (110) may be integrated with the processor (120). The memory (110) and the processor (120) may reside within an application-specific integrated circuit (ASIC). The ASIC may reside within a user terminal. Alternatively, the memory (110) and the processor (120) may reside as separate components within the user terminal.

[0072] According to one embodiment, the processor (120) may be operatively connected to the memory (110), the communication interface (130), and / or the display device (140). For example, the processor (120) may control the operation of the memory (110), the communication interface (130), and / or the display device (140).

[0073] According to one embodiment, the communication interface (130) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (100) and an external device (e.g., user terminal (210) of FIG. 2, external server (220), and database, etc.), and the performance of communication through the established communication channel. The communication interface (130) may include one or more communication processors that operate independently of the processor (120) (e.g., application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication interface (130) may include a wireless communication module (e.g., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (e.g., LAN (local area network) communication module, or power line communication module). The corresponding communication module among these communication modules can communicate with an external electronic device through a first network (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (e.g., a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a long-range communication network such as a computer network (e.g., LAN or WAN). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module can identify or authenticate the electronic device (100) within a communication network, such as the first network or the second network, using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in a subscriber identification module.

[0074] According to one embodiment, the display device (140) may include at least one output device that provides various information and a user interface to the user.

[0075] For example, the display device (140) may include a display device, an audio output device, a virtual reality output device, etc.

[0076] For example, the display device (140) can provide the administrator with various types of user interfaces described in the present disclosure visually and / or audibly.

[0077] The components of the electronic device (100) illustrated in FIG. 1 are exemplary, and the embodiments of the present disclosure are not limited thereto.

[0078] At least some of the embodiments of the present disclosure may be implemented as artificial intelligence (AI) through a processor (120) and memory (110) of an electronic device (100). The processor (120) may be composed of one or more processors, and the one or more processors may be general-purpose processors such as a CPU, AP, DSP (digital signal processor), etc., graphics-dedicated processors such as a GPU, VPU (vision processing unit), or artificial intelligence-dedicated processors such as an NPU. The one or more processors may be controlled to process input data according to predefined operation rules or artificial intelligence models stored in memory (110). Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0079] The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a predefined operation rules or artificial intelligence models configured to perform a desired characteristic (or purpose) are created by a basic artificial intelligence model being trained using a number of learning data by a learning algorithm. Such learning may be performed within the electronic device (100) itself where the artificial intelligence according to the present disclosure is performed, or it may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0080] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and can perform neural network operations through operations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights can be updated so that the loss value or cost value obtained from the artificial intelligence model during the learning process is reduced or minimized. Artificial neural networks may include, but are not limited to, deep neural networks (DNN), convolutional neural networks (CNN), recurrent neural networks (RNN), restricted Boltzmann machines (RBM), deep belief networks (DBN), bidirectional recurrent deep neural networks (BRDNN), or deep Q-networks.

[0081] According to one embodiment, the electronic device (100) may utilize a pre-trained artificial intelligence model in the process of extracting state feature information from target image data. For example, the artificial intelligence model may be an image analysis model trained to classify areas related to corner damage, surface scratches, discoloration, and warping in pixel units when a captured image of a physical collectible item is input. In this case, the artificial intelligence model may include a convolutional neural network structure, but is not limited thereto.

[0082] In one embodiment, the electronic device (100) provides the results of contour detection and pixel-to-change rate calculation as input to an artificial intelligence model, and receives a probability value from the artificial intelligence model that corner damage or surface scratches exist. The electronic device (100) may confirm the location as a damage point and include it in the state feature information only when the probability value exceeds a preset confidence threshold. This reduces false detections that may occur in judgments based on simple thresholds.

[0083] According to one embodiment, luminance contour information generated from a reflection image of a reference straight line pattern or a luminance deviation matrix can be used as input data for an artificial intelligence model. The artificial intelligence model can be trained to compare and analyze changes in the spacing of reflection straight lines, changes in curvature, or closed curve distribution patterns of luminance contours based on training data for an item image in a normal state and an item image in which warping has occurred. The electronic device (100) can finally determine whether the warping judgment criteria are satisfied by referring to the output result of the artificial intelligence model.

[0084] In one embodiment, the electronic device (100) can use an artificial intelligence model to correct the condition determination criteria when calculating a condition grade based on damage parameters (or, number of damages), the degree of discoloration, and damage parameters (or, number of damages) corresponding to the number of bends. For example, the artificial intelligence model can learn past transaction history and actual appraisal results to output a correction coefficient so that even with the same combination of damage parameters (or, number of damages), different condition grades are assigned depending on the item type or the number of issuances.

[0085] According to one embodiment, the electronic device (100) may utilize an artificial intelligence-based price prediction model in the process of adjusting the tradable price range and the estimated sales amount according to the condition grade. The price prediction model may be configured to predict the range of price fluctuations according to changes in the condition grade by learning data including the number of items issued, condition grade, damage score, past transaction prices, and transaction frequency. The electronic device (100) may set the tradable price range and the estimated sales amount of the digital item based on the prediction results.

[0086] In one embodiment, the artificial intelligence model may be periodically retrained or its parameters updated as a new transaction occurs or additional image data is received from a user terminal. In this case, actual transaction completion prices, status grade change history, and user feedback data may be utilized for retraining, thereby gradually improving the discrimination accuracy and price prediction reliability of the artificial intelligence model.

[0088] FIG. 2 is a block diagram showing the components of an item information management system according to one embodiment of the present invention.

[0089] According to one embodiment, the item information management system may include an electronic device (100), a user terminal (210), and an external server (220).

[0090] According to one embodiment, the electronic device (100) may be implemented as a server device, a cloud-based computing device, or a central control device, and may be configured to perform various data processing functions including a processor and memory. The electronic device (100) communicates with a user terminal and an external server via a network and may be responsible for control logic and data management functions for the entire service.

[0091] The electronic device (100) may include a database or storage module for storing and managing image data, status information, setting information, and history data received from a user. Such storage module may be configured to manage item information, user account information, device mapping information, and service operation logs, etc., in a structured form.

[0092] The electronic device (100) can analyze data received through a computation module and perform judgment logic such as state determination, score calculation, and grade determination based on a predefined rule or a learned model. At this time, the electronic device (100) may be implemented as a single computation unit or as a distributed processing structure in which multiple computation nodes cooperate.

[0093] The electronic device (100) may include a communication interface to manage communication with a user terminal (210) and an external server (220), and may be configured to perform data transmission and reception in a wired or wireless network environment. The electronic device (100) may be configured to monitor the communication status, response delay, or occurrence of an error, and to select a retransmission or alternative path as needed.

[0094] The electronic device (100) may include access control, authentication, and authorization management functions to ensure service stability, and may be configured to manage access rights on a user account or device basis. Additionally, the electronic device (100) may be configured to record events occurring during service operation and to utilize the analysis results for service quality improvement or subsequent function expansion.

[0095] According to one embodiment, the user terminal (210) may be implemented as a smartphone, tablet, laptop, or other portable electronic device and may be configured to receive personalized services linked to a user account. The user terminal (210) may communicate with the electronic device (100) to transmit and receive service-related information.

[0096] The user terminal (210) can provide functions for capturing images, inputting information, changing settings, and checking results through a user interface. For example, the user terminal (210) may be configured to capture an image of a target item using a camera module and transmit the captured image to an electronic device (100).

[0097] The user terminal (210) may include an input processing module for collecting user input and transmitting the input data to the electronic device (100). At this time, the user terminal (210) may be configured to perform validation on the format or range of the input value to prevent error input in advance.

[0098] The user terminal (210) may be configured to visually display status information, analysis results, or setting results received from the electronic device (100). For example, the user terminal (210) may provide the user with status grades, score changes, or price adjustment results in the form of graphic elements or text.

[0099] The user terminal (210) may be configured to perform user authentication, session management, and data protection, including security functions. Additionally, the user terminal (210) may be configured to switch communication methods according to changes in the network environment or to provide some functions in a limited manner while offline.

[0100] According to one embodiment, the external server (220) is a separate server system linked with the electronic device (100) and may be configured to be responsible for providing data, assisting in computation, or linking with external services. The external server (220) may be implemented as one or more server devices or cloud infrastructure.

[0101] The external server (220) can perform the role of storing and providing item issuance information, market data, statistical information, or external standard data. The electronic device (100) can be configured to perform more precise judgments by reflecting the data received from the external server (220) into internal processing logic.

[0102] An external server (220) can provide computational resources to support large-scale data processing or the execution of a learning model. For example, the external server (220) may be configured to perform learning or inference operations on an image analysis model or a prediction model and to transmit the results to an electronic device (100).

[0103] The external server (220) may provide a standardized interface or an application program interface for communication with the electronic device (100). Through this, the electronic device (100) can reliably exchange data with the external server (220) and expand service functions.

[0104] The external server (220) may be configured to monitor the service operation status and perform recovery procedures in the event of a failure. Additionally, the external server (220) may be configured to apply security policies and access controls to prevent unauthorized access from the outside and maintain data integrity.

[0106] FIG. 3 is a flowchart of the operation of an item information management method according to one embodiment of the present invention.

[0107] According to one embodiment, the components of an item information management system may perform the operations disclosed in FIG. 3. For example, at least some of the components included in the item information management system and / or at least some of the components included in the electronic device (100) (e.g., memory (110), processor (120), communication interface (130) and display device (140) of FIG. 1) may be configured to perform the operations of FIG. 3.

[0108] In the following embodiments, the operations S310 to S350 may be performed sequentially, but are not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. Additionally, content corresponding to or overlapping with the above description in relation to FIG. 3 may be briefly explained or omitted.

[0109] According to one embodiment, the processor (120) can receive target image data including a captured image of a target item from a user terminal (S310).

[0110] The electronic device (100) is configured to receive target image data including a captured image of a target item from a user terminal.

[0111] Here, the “target item” is a physical collectible item and may be a card, physical goods, limited edition printed material, or a physical object equivalent thereto. The user photographs the target item using a user terminal to evaluate the status of the target item or to link it with a digital item, and the electronic device (100) receives the photographed image through a network.

[0112] When the electronic device (100) receives target image data, it may not simply receive only the image file, but may also receive or verify metadata such as shooting time information, image resolution, shooting direction information, or user terminal identification information. This metadata may be used as a standard for determining the reliability of the image, determining whether there is duplicate shooting of the same item, or correction based on shooting conditions during a subsequent state analysis process.

[0113] The electronic device (100) can identify images in which the entire shape of the target item is not included within the frame, images that are excessively blurred, or images taken at extreme angles, and determine whether the images are suitable for state analysis. If necessary, the electronic device (100) may be configured to send a re-shooting request message to a user terminal.

[0114] In this way, the electronic device (100) does not treat the target image data as a simple input value, but performs the role of managing the suitability of the image in the preprocessing stage so that the state feature information extraction operation can be performed stably thereafter.

[0115] According to one embodiment, the processor (120) can extract state feature information from the target image data, including at least one of edge damage, surface scratch, discoloration, and warping of the target item (S320).

[0116] The electronic device (100) is configured to analyze received target image data and extract state feature information indicating the state of the target item. The state feature information may include at least one of corner damage, surface scratches, discoloration, or warping of the target item, which are factors that directly affect the value judgment of the physical collectible item.

[0117] The electronic device (100) can identify the outer contour of a target item within the target image data and distinguish corner areas, central areas, or surface areas based on the contour. Through this, the electronic device (100) can perform analysis by distinguishing between areas prone to damage and areas that are not.

[0118] For example, since minor damage or dents occurring in the corner area have a significant impact on the condition grade of the item, the electronic device (100) extracts condition feature information centered on that area.

[0119] The electronic device (100) can be configured to distinguish between simple contamination or shadows and actual damage by comprehensively considering visual features such as changes in pixel brightness, color distribution, pattern distortion, or reflection shape.

[0120] For example, scratches on a surface may appear as linear patterns with a certain directionality, and discoloration may show a distribution where color values ​​in a specific area consistently differ from those in surrounding areas.

[0121] Based on these analysis results, the electronic device (100) may not only determine whether damage exists using binary values, but may also include the number, range, or relative degree of damage as state feature information. This enables a more detailed judgment in the subsequent state grade calculation stage.

[0122] According to one embodiment, the processor (120) can calculate a state grade corresponding to a target item based on state feature information and a predefined state determination criterion (S330).

[0123] The electronic device (100) is configured to calculate a state grade corresponding to a target item based on extracted state feature information.

[0124] Here, “condition grade” is an indicator that summarizes the overall preservation status of the target item and can be determined as one of multiple grades.

[0125] For example, the condition grade can be set into stepwise classifications such as best condition, good condition, average condition, or damaged condition.

[0126] The electronic device (100) calculates a state grade by referring to predefined state determination criteria. The state determination criteria may include the number of corner damages, the presence or absence of scratches, the degree of discoloration, the number or combination of warping, and these criteria may be set differently depending on the item type or service policy.

[0127] For example, the electronic device (100) may be configured to be given a top condition grade if there is no corner damage at all and no scratches or discoloration are detected, and a good condition grade if there is a few corner damages but no discoloration or warping. On the other hand, if multiple warpings are detected or extensive discoloration occurs, a relatively lower condition grade may be given.

[0128] In this way, the electronic device (100) organizes the state feature information not as a simple list of state features, but as a single result called a state grade by comprehensively considering multiple state elements, thereby enabling the subsequent digital value adjustment operation to be performed efficiently.

[0129] According to one embodiment, the processor (120) can adjust the tradable price range and estimated sales amount of the digital item linked to the target item based on the status grade (S340).

[0130] The electronic device (100) is configured to adjust the tradable price range and estimated sale amount of the digital item linked to the target item based on the calculated condition grade. This is a step to ensure that the condition of the physical collectible item is directly reflected in the digital transaction value.

[0131] The electronic device (100) applies a state grade correction factor to the standard price information under the premise that even if the target item is the same, the transaction value should differ depending on the state grade.

[0132] For example, items with the highest condition grade may be assigned an increased price range relative to the base price, while items with a damaged condition grade may be set a decreased price range relative to the base price.

[0133] The electronic device (100) can take into account market volatility or room for negotiation between users by setting the tradable price range in the form of a range rather than a single fixed value. Additionally, the estimated sales amount can be calculated by reflecting past transaction history, the number of items issued, or current demand conditions within the price range.

[0134] In this process, the electronic device (100) can indirectly provide the user with the basis for price fluctuations by clearly linking the price adjustment results with the status grade, and can reduce disputes caused by arbitrary price setting.

[0135] Additionally or generally, the electronic device (100) is configured to adjust the tradable price range and estimated sale amount of the digital item linked to the target item by using the calculated condition grade for the target item as an input value. This operation is a key step to ensure that the condition evaluation result of the physical collectible item is directly reflected in the digital transaction value.

[0136] The electronic device (100) may refer to predefined reference price information for a target item. Here, the reference price information may include at least one of the transaction price when the item is in its best or reference state, the historical average transaction price, or the initial price set at the time of issuance. The electronic device (100) utilizes this reference price information as a reference point for state grade correction.

[0137] The electronic device (100) calculates a tradable price range by applying a price correction factor according to the condition grade. For example, for an item assigned the highest condition grade, a price range higher than the reference price can be set, and for a condition grade with some damage, a price range lower than the reference price can be set. In this case, the price range is not defined as a single price value, but as a range including the minimum tradable price and the maximum tradable price.

[0138] This range setting takes into account the characteristics of the digital trading environment, and the electronic device (100) provides the price as a range value rather than a fixed value so as to reflect market volatility, the possibility of negotiation between users, or platform policies. Through this, trading flexibility is ensured while the results of the physical item condition evaluation are reflected in the digital transaction.

[0139] The electronic device (100) can calculate a range of tradable prices and then additionally calculate an estimated sales amount within that range. The estimated sales amount may be determined by comprehensively considering additional data, such as past transaction history, the actual transaction price of items of the same grade, scarcity based on the number of issued items, or recent transaction frequency, rather than simply the median value of the price range.

[0140] In one embodiment, the electronic device (100) may calculate an estimated sales amount by utilizing an artificial intelligence-based price prediction model. In this case, the price prediction model may be configured to output the price range at which the item is most likely to be actually traded, based on the results of learning from condition grades, damage scores, the number of issues, and past transaction data. The electronic device (100) sets this output value as the estimated sales amount.

[0141] In this way, the electronic device (100) converts an abstract evaluation result, such as a condition grade, into concrete numerical information, such as a tradable price range and an estimated sales amount, thereby allowing the condition of the physical item to be directly and quantitatively reflected in the digital transaction value. This has a technical effect that goes beyond simple information provision and enables the condition evaluation result to be utilized in actual transaction decision-making.

[0142] According to one embodiment, the processor (120) can provide the status grade, the tradable price range, and the estimated sale amount to the user terminal by linking them to the target item information corresponding to the target item (S350).

[0143] The electronic device (100) is configured to provide the calculated status grade, the adjusted tradable price range, and the estimated sale amount to the user terminal by linking them to the target item information corresponding to the target item. The target item information may be a set of information that integrates status evaluation results and value information, along with basic information for identifying the target item.

[0144] The electronic device (100) can clearly distinguish and transmit status grade and price information so that the information provided to the user terminal can be intuitively understood by the user. For example, the electronic device (100) can provide the status grade in the form of a visual icon or a step display, and can also provide the tradable price range and the estimated sale amount as numerical information.

[0145] The electronic device (100) may be configured to retransmit updated target item information to a user terminal when the status evaluation for the same target item is updated or the price standard is changed. This allows the user to continuously check the status and value changes of the target item.

[0146] In this way, the electronic device (100) goes beyond simply providing analysis results and performs the role of a final stage connecting the entire process from status evaluation to digital value linkage with the user terminal, thereby strengthening the connectivity between the physical collectible item and the digital trading environment.

[0147] Additionally or generally, the electronic device (100) is configured to provide the calculated status grade, tradable price range, and estimated sale amount to the user terminal in conjunction with the target item information corresponding to the target item. This operation serves as a final step in delivering the analyzed and calculated results to the user, and performs the role of connecting the physical collectible item with the digital trading environment.

[0148] The electronic device (100) manages target item information by integrating condition evaluation results and value information, along with basic information for identifying the target item. For example, the target item information may be composed of a data structure including an item identifier, item name, and issuance information, along with a condition grade, damage score, tradable price range, and estimated sale amount.

[0149] When the electronic device (100) provides such target item information to a user terminal, it is configured so that the relationship between each information item can be clearly recognized. That is, the arrangement of information can be adjusted so that the user terminal can intuitively check how the status grade was calculated and how the status grade is reflected in the tradable price range and the estimated sales amount.

[0150] For example, the electronic device (100) may provide a status grade on the screen of a user terminal in the form of a stepwise display or a visual icon, and may also display a tradable price range and an estimated sale amount as sub-information. Through this, the user can easily understand the correlation between the item status and the price.

[0151] The electronic device (100) may be configured to regenerate target item information and provide updated information to the user terminal when the status evaluation for the same target item is updated, or when the transaction reference price or market data is changed. For example, if the damage parameter (or number of damages) is changed as a result of additional image analysis, or if the issuance count information is updated, the electronic device (100) may recalculate the status grade and price information in response and reflect them to the user terminal.

[0152] In addition, the electronic device (100) may be configured so that the target item information provided to the user terminal is not limited to simple inquiry information, but can be linked with functions for transaction registration, price proposal, or item management. That is, the status grade and price information can be used as reference data for subsequent user actions.

[0153] In this way, the electronic device (100) integrates the condition evaluation result, the price calculation result, and the item identification information into a single target item information and provides it to the user terminal, thereby delivering the entire process from the condition analysis of the physical collectible item to the reflection of its digital transaction value to the user in a consistent form. This provides a technical effect that improves user trust and ensures transaction transparency within the platform.

[0154] Below, the process of the electronic device (100) extracting state feature information will be described in more detail.

[0156] According to one embodiment, the electronic device (100) detects the outer contour of a target item within the target image data, identifies a corner area based on the outer contour, and determines the number of points within the corner area where the rate of change relative to a set pixel exceeds a reference value as the number of corner damages and includes this in the state feature information.

[0157] The electronic device (100) is configured to detect the outline of a real collecting item within the target image data and then identify a corner area based on the outline.

[0158] Here, “outline” refers to a boundary line that distinguishes the target item from the background within the target image, meaning a boundary in the form of a closed curve that is continuously formed along the entire shape of the target item.

[0159] The electronic device (100) can distinguish between the area where the target item is located and the background area by using the difference in brightness, color, or texture included in the target image data, and derive an outline through this.

[0160] The electronic device (100) does not use the detected outer contour line merely as simple line information, but uses it as reference information that reflects the structural characteristics of the target item.

[0161] For example, the electronic device (100) can identify the four corners or multiple corner locations of a target item by setting sample points at regular intervals along the outer contour and analyzing the change in curvature or direction of each point.

[0162] In this case, a corner can be defined as a point on the outer outline where a sudden change in direction occurs, and this definition is applicable regardless of whether the item is in the form of a rectangular card or an irregular goods.

[0163] The electronic device (100) sets an area within a certain distance from the outer contour, centered on the identified corner location, as a corner area.

[0164] In this case, the size or range of the corner area can be set as a relative ratio to the total size of the target item, for example, defined as a certain ratio of the length of the item's shorter side or a certain ratio of the total area.

[0165] Through this, the electronic device (100) can selectively analyze only the parts of the entire item where damage frequently occurs.

[0166] In this way, the electronic device (100) identifying corner regions based on the outer contour line is of significant importance as a preprocessing step to reduce unnecessary central region analysis during the subsequent process of determining damage and to concentrate computational resources on regions with a high probability of damage.

[0167] The electronic device (100) is configured to calculate the number of corner damages by determining the number of points within the identified corner area where the rate of change relative to pixels exceeds a reference value.

[0168] Here, “pixel-to-color change rate” refers to an indicator representing how rapidly the difference in brightness or color values ​​between adjacent pixels or between sets of pixels within a given area changes over time or space.

[0169] The electronic device (100) can calculate the contrast change rate by comparing the luminance value or color channel value of each pixel with the value of an adjacent pixel for pixels included in the corner area. While normal corner areas show relatively continuous and gradual pixel value changes, corner areas that are dented, torn, or damaged tend to show abrupt changes in brightness or color discontinuities. The electronic device (100) can use these differences to distinguish between actual damage and simple image noise.

[0170] The electronic device (100) uses a preset reference value to determine only the points where the calculated pixel change rate exceeds the reference value as candidate damage points. This reference value may be set differently depending on the shooting resolution, lighting conditions, or material characteristics of the target item, and may be configured to be adjustable during the service operation process. Through this, the electronic device (100) can prevent temporary pixel changes caused by lighting reflection or shadows from being mistaken for damage.

[0171] The electronic device (100) determines the number of corner damages by counting the number of points exceeding a reference value, rather than simply determining whether the candidate damage points exist. For example, if multiple points exceeding a reference value are distributed consecutively within a corner area, the electronic device (100) may be configured to group them into a single damage or count them as separate damages.

[0172] In this way, the electronic device (100) can perform repeatable and consistent damage determination by excluding subjective judgment by calculating the number of corner damages using quantitative indicators such as the rate of change relative to pixels and the number of points exceeding them. This damage parameter (or number of damages) information is subsequently used as an important input value in the state grade calculation step.

[0173] The electronic device (100) is configured to calculate the number of corner damages by grouping points where the pixel contrast change rate within the corner area exceeds a reference value, rather than treating them simply as individual pixels. This is to prevent mistaking the number of pixels itself for a damage parameter, as the same physical damage can appear as a high contrast change in multiple adjacent pixels simultaneously.

[0174] The electronic device (100) can identify pixels whose rate of change relative to the pixel exceeds a reference value as candidate pixels for damage, and then group spatially adjacent candidate pixels for damage into a single damage cluster. At this time, proximity can be determined based on at least one of the distance between pixels, connectivity, or continuity in the same corner direction. For example, candidate pixels for damage located within a certain distance within the same corner area can be grouped into a single damage cluster.

[0175] The electronic device (100) determines each damage cluster formed in this way as a single independent corner damage and calculates the number of such clusters as the number of corner damages. That is, the number of corner damages is defined not by the total number of pixels exceeding a reference value, but by the number of physically distinguishable damage clusters.

[0176] For example, if there is a small dent on one corner of a target item, a high contrast change rate may be detected in multiple pixels in that area, but the electronic device (100) groups these pixels into one damage cluster and calculates the number of corner damages as 1. On the other hand, if there are two dents at separate locations, the electronic device (100) can identify them as different damage clusters and calculate the number of corner damages as 2.

[0177] Additionally, the electronic device (100) may be configured to prevent extremely minute noise-level pixel changes from being mistaken for damage by referring to the size, shape, or distribution of the damage cluster. For example, if the number of candidate pixels for damage is less than a predetermined number, or if the area of ​​the damage cluster does not meet a preset minimum area criterion, the electronic device (100) may not count the cluster as actual damage.

[0178] In this way, the electronic device (100) defines the number of corner damages as “the number of results obtained by clustering a set of damage candidate pixels into physical damage units,” thereby ensuring that consistent damage parameters are calculated even when repeated measurements are taken under the same shooting conditions or similar lighting environments. This number of corner damages is subsequently used as a quantitative input value in the item damage score calculation step.

[0179] Meanwhile, the corner damage parameter (or number of damages) calculated as above serves as an indicator that quantitatively represents the degree of damage to the target item and is used as a key input value for calculating the item damage score. The electronic device (100) may be configured to determine that the physical damage to the item has increased as the number of corner damages increases, and to increase the item damage score accordingly.

[0180] In particular, as the number of corner damages is defined in cluster units, the standard for the number of damages is stably maintained regardless of changes in shooting resolution or image noise. This prevents the item damage score from being distorted by shooting conditions or device performance, and ensures the comparability of damage scores even between images captured on different user terminals.

[0182] According to one embodiment, the electronic device (100) controls the output of a plurality of mutually parallel reference straight line patterns through the screen of the user terminal before the shooting of the target item is performed, acquires target image data including an image in which the surface of the target item is shot with the reference straight line patterns reflected, detects a plurality of reflected straight lines corresponding to the reflected image of the reference straight line patterns within the target image data, calculates the change in spacing and the change in curvature between the plurality of reflected straight lines, identifies a location where the change in spacing or the change in curvature satisfies a preset bending judgment criterion as a bending candidate region, and determines the number of spatially separated bending candidate regions as the number of bends existing in the target item and includes them in state feature information.

[0183] The electronic device (100) acquires target image data that includes an image in which a reference straight line pattern is reflected, rather than a normal captured image, in order to determine whether the target item is bent.

[0184] To this end, the electronic device (100) may be configured to control the screen of a user terminal to display a plurality of mutually parallel reference straight line patterns before the shooting of the target item is performed.

[0185] The reference straight line pattern may consist of visual elements in the form of straight lines or bands arranged at regular intervals, and is designed to cause distortion depending on the bending when reflected on the surface of the target item.

[0186] The electronic device (100) induces a user terminal to photograph a target item while displaying a reference straight line pattern, and as a result, receives an image including the reflection of the reference straight line pattern on the surface of the target item as target image data. At this time, the reflected reference straight line pattern may appear with uneven spacing between straight lines depending on the surface shape of the target item, or may appear distorted in a curved shape in a specific area.

[0187] This method provides the effect of indirectly revealing fine bends or curves of the target item without directly printing or attaching a physical reference pattern to the surface of the target item. The electronic device (100) then uses this reflection pattern-based image as key input data for determining the bend.

[0188] The electronic device (100) is configured to detect a plurality of reflected lines corresponding to a reflected image of a reference straight line pattern within the target image data, and to calculate the change in spacing between the reflected lines and the change in curvature of each reflected line.

[0189] Here, “reflected line” refers to the linear pattern that appears on the image as a reference line pattern is reflected off the surface of the target item.

[0190] The electronic device (100) identifies the area where a reflection line appears within the target image data, and then analyzes the position, direction, and shape of each reflection line.

[0191] For example, the electronic device (100) can identify a section where the spacing between the reflection lines narrows or widens rapidly at a specific location based on the average spacing between the reflection lines. Additionally, if the reflection lines fail to maintain a straight shape and bend in a specific direction, it can be determined that a change in curvature has occurred in that section.

[0192] At this time, the electronic device (100) does not treat the entire single reflection line as a single judgment unit, but divides it into multiple detailed sections along the length direction of the reflection line to calculate the change in spacing and / or the change in curvature. Through this, the electronic device (100) can more precisely determine where on the surface of the target item distortion has occurred.

[0193] The spacing change and curvature change information calculated in this way is used as a candidate indicator representing localized bending or curvature on the surface of the target item, and is subsequently used for comparison with a bending judgment criterion.

[0194] The electronic device (100) is configured to identify a location as a candidate bending area where the change in spacing between reflection lines or the change in curvature of each reflection line satisfies a preset bending judgment criterion.

[0195] Here, the “bending judgment criteria” are set based on the normal spacing distribution and linearity of the reflection line that appears when the target item is in a flat state, and include a reference value for judging that if a deformation deviating from the criteria occurs, it is a distortion caused by bending.

[0196] The electronic device (100) first checks the information on changes in spacing and curvature calculated along each reflection line by location. At this time, the change in spacing can be defined as the change in the relative distance between adjacent reflection lines, and the change in curvature can be defined as the degree to which the reflection line fails to maintain a straight shape and bends in a specific direction. The electronic device (100) performs an analysis based on whether these changes appear locally concentrated at a specific location.

[0197] The electronic device (100) individually identifies locations where the spacing change or curvature change satisfies the bending judgment criteria, and then defines these locations as bending candidate regions by expanding them into region units rather than simple point units. That is, if the electronic device (100) continuously satisfies the bending judgment criteria at adjacent locations, it can group them into a single bending candidate region and process them. This prevents duplicate calculations as a single bend even if a single bend is repeatedly detected at multiple locations.

[0198] Additionally, the electronic device (100) treats spatially separated candidate bending regions as independent bending regions. For example, if independent spacing distortion or curvature distortion is detected at the center and bottom of the target item, respectively, the electronic device (100) may identify them as two different candidate bending regions. In this case, spatial separation may be determined based on at least one of the distance between positions on a reflection line, the minimum spacing between candidate regions, or the relative positional difference relative to the total size of the target item.

[0199] The electronic device (100) aggregates the number of identified candidate bending regions and calculates this as the number of bending present in the target item. That is, the number of bending is defined not as a simple number of locations where the spacing change or curvature change satisfies the criteria, but as the number of physically distinguishable candidate bending regions. Through this definition, it is possible to prevent the same bending from being excessively divided and calculated due to differences in shooting resolution or the density of the reflection pattern.

[0200] The electronic device (100) makes a final determination that there is a bend in the target item only when there is one or more candidate bend regions, and includes the result of the determination in the state feature information. At the same time, the number of calculated bends can be stored internally or transmitted to a subsequent processing step, and can be used as a quantitative input value in the process of calculating the item damage score or determining the state grade.

[0201] In this way, the electronic device (100) can objectively and repeatably evaluate the bending state of a target item by identifying candidate bending regions in area units and calculating the number of such regions based on observable optical deformation information such as changes in spacing and changes in curvature. This structure has technical significance in that it does not rely on simple subjective judgment or visual evaluation by the user, but provides consistent bending judgment results under the same shooting conditions.

[0203] According to one embodiment, if the change in spacing or the change in curvature does not satisfy a preset bending judgment criterion, the electronic device (100) may generate a luminance deviation matrix by comparing the distribution of actual received pixel-by-pixel luminance data with a virtual reference plane defined by connecting the four corner vertex coordinates of the target item, generate a brightness contour line by linearly connecting pixels having the same luminance deviation within the luminance deviation matrix, identify the closed loop shape among the brightness contour lines, and then determine the number of target closed loop shapes whose internal area exceeds 15% of the total area of ​​the target item as the number of bends present in the target item and include this in the state feature information.

[0204] The electronic device (100) is configured to perform an alternative bending judgment procedure when the spacing change and curvature change calculated from the reflection image of the reference straight line pattern do not satisfy the preset bending judgment criteria. This is because a situation may occur where the reflection of the reference straight line pattern does not appear sufficiently clearly depending on the material properties, surface reflection properties, or shooting environment of the target item.

[0205] For example, if the surface of the target item is a matte material or has low reflectivity, the reflected image of the reference straight line pattern may appear blurry. In this case, the spacing change or curvature change may not satisfy the reference value even though actual bending exists. The electronic device (100) takes these situations into consideration and performs an auxiliary judgment procedure only when it is determined that the judgment result based on the reference straight line is invalid.

[0206] That is, the electronic device (100) selectively performs the judgment based on the above-described spacing change and curvature change only when the result is insufficient or has low reliability, thereby preventing unnecessary calculations while ensuring the reliability of the bending judgment.

[0207] The electronic device (100) identifies the coordinates of the four corner vertices of the target item from the target image data and defines a virtual reference plane by connecting these vertices. Here, the reference plane refers to a reference plane assumed to be a flat surface in an ideal state where the target item is not bent.

[0208] The electronic device (100) generates a luminance deviation matrix by comparing pixel-by-pixel luminance data received from this reference plane and an actual captured image. The luminance deviation can be defined as the difference between the expected luminance distribution on the reference plane and the actual pixel luminance value, which is the result of numerically expressing the change in brightness caused by the surface curvature of the target item.

[0209] The electronic device (100) calculates a luminance deviation value for each pixel location in the image and organizes it into a two-dimensional matrix. This luminance deviation matrix is ​​a data structure that indicates how brightness differences are distributed across the surface of the target item, and is subsequently used as basic data for identifying warping areas.

[0210] The electronic device (100) generates brightness contours by linearly connecting pixels having the same brightness deviation value using a generated brightness deviation matrix. Similar to contour lines on a map, brightness contours represent a linear structure representing the boundaries of pixels having the same level of brightness change.

[0211] The electronic device (100) can divide the range of change of luminance deviation values ​​into multiple stages and generate multiple luminance contour lines by connecting sets of pixels corresponding to each stage. In this process, if the surface of the target item is flat, the luminance contour lines appear in a relatively simple or widely dispersed form. On the other hand, if there is bending or curvature, there is a high possibility that dense contour lines will be formed around a specific area or that contour lines in the form of closed curves will appear.

[0212] The electronic device (100) uses the shape of these brightness contour lines as an intermediate result for determining curvature, and uses them as a visual and numerical basis to distinguish between simple brightness differences and structural curvature.

[0213] The electronic device (100) identifies closed curve shapes among the generated brightness contour lines and determines closed curve shapes among them, in which the internal area exceeds 15% of the total area of ​​the target item, as bending candidate regions.

[0214] Here, “closed curve shape” refers to a contour line that forms a closed shape by connecting the starting point and the ending point, which strongly suggests the existence of local curvature or bending on the surface of the item.

[0215] The electronic device (100) calculates the area contained within the closed curve and recognizes it as the area corresponding to the actual bend only when the area exceeds a preset ratio standard. This prevents minute changes in brightness caused by light reflection or shadows from being mistaken for bending.

[0216] The electronic device (100) treats spatially separated closed curve-shaped candidate regions as independent bends and calculates the number of such regions as the number of bends present in the target item. That is, one closed curve shape corresponds to one bend, and if multiple closed curves are identified, it is determined that multiple bends exist.

[0217] The electronic device (100) includes the number of bends calculated in this way in the state feature information, and simultaneously determines that there is a bend in the target item if there is one or more candidate bend regions. This result is subsequently used as a quantitative input value in the item damage score calculation and state grade determination steps.

[0219] FIG. 4 is a flowchart of the operation of an item information management method according to one embodiment of the present invention.

[0220] According to one embodiment, the components of an item information management system may perform the operations disclosed in FIG. 4. For example, at least some of the components included in the item information management system and / or at least some of the components included in the electronic device (100) (e.g., memory (110), processor (120), communication interface (130) and display device (140) of FIG. 1) may be configured to perform the operations of FIG. 4.

[0221] In the following embodiments, the operations S410 to S440 may be performed sequentially, but are not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. Additionally, content corresponding to or overlapping with the above description in relation to FIG. 4 may be briefly explained or omitted.

[0222] According to one embodiment, the processor (120) can identify the number of damages, including the number of corner damages, the number of surface scratches, the degree of discoloration, and the number of warping, based on state feature information (S410).

[0223] The electronic device (100) is configured to identify the number of damages (or damage parameters) present in the target item using state feature information extracted from the target image data. Here, the state feature information refers to a composite damage indicator including the number of corner damages, the number of surface scratches, the number of bends, and the degree of discoloration calculated in the preceding step.

[0224] The electronic device (100) manages each damage item by classifying it into independent damage types. That is, corner damage can be defined by the number of damage clusters detected in the corner area, surface scratches can be defined by the number of linear damage units identified in the surface area, and warping can be defined by the number of warping candidate areas identified on the surface of the target item. In the case of discoloration, instead of a number, it can be managed by a continuous or stepwise numerical value indicating the degree of discoloration.

[0225] The electronic device (100) does not simply list these damage indicators in parallel, but organizes them into normalized damage count information from the common perspective of “factors that degrade the physical state of the item.”

[0226] For example, the electronic device (100) can form a set of damage counts (or a set of damage parameters) by treating the number of corner damages, the number of scratches, and the number of bends as separate damage occurrence units.

[0227] An important point at this stage is that the number of damages is not based on user subjectivity or arbitrary judgment, but is a value calculated according to criteria clearly defined in the previous stage. Therefore, the electronic device (100) can always identify the same number of damages when analyzing the same item under the same conditions, and this serves as the basis for ensuring the reliability of the damage score calculation thereafter.

[0228] Meanwhile, the electronic device (100) according to the present disclosure can identify “number of corner damages,” “number of surface scratches,” “degree of discoloration,” and “number of bends” based on state feature information.

[0229] The number of corner damages is calculated not simply by measuring the size of the damaged area, but by counting the number of distinguishable damaged objects within the corner area.

[0230] The electronic device (100) first detects the outer contour of a target item from the target image data and separates corner regions corresponding to each corner based on the contour. At this time, the corner region is defined as a vertex adjacent section where a sudden change in the direction of the contour occurs, and is set as an independent region of interest for each corner.

[0231] Subsequently, the electronic device (100) detects clusters of candidate pixels for damage based on pixel contrast change rates, edge discontinuities, or local brightness abrupt change patterns within each corner region. These candidate pixel clusters are divided into units of damage objects that are separable from one another through connectivity component analysis or a density-based clustering algorithm.

[0232] The electronic device (100) determines that if there are multiple damaged objects within the same corner area that are not adjacent to or connected to each other, they are each independent corner damages. Conversely, if a cluster of pixels is continuously connected within a certain distance to form a single damaged object, it is counted as a single corner damage.

[0233] In this way, the electronic device (100) calculates the number of damaged objects for each corner area and determines the number of corner damages by summing the number of damaged objects calculated for all corner areas.

[0234] In other words, the coefficient result at the object level, rather than the area or ratio, is used as the damage parameter.

[0235] The number of surface scratches is also calculated not simply by determining the total length or area of ​​the region where scratches exist, but by identifying the number of distinguishable scratch objects.

[0236] The electronic device (100) uses multiple angle-based captured images to track pixel locations where discontinuous changes in specular reflection light levels occur in each image. Subsequently, discontinuous luminance peaks that are repeatedly detected at the same location or adjacent locations are classified as scratch candidate pixels.

[0237] The electronic device (100) groups these scratch candidate pixels through linear structure analysis. Specifically, by analyzing the spatial arrangement direction, length, continuity, curvature, etc. of the pixels, a continuous linear pattern is defined as a single scratch object.

[0238] Linear patterns that have different directions, are separated by a certain distance or longer, or have a broken continuity are separated into different scratch objects. On the other hand, patterns that are continuous in the same direction for a certain length or longer are grouped into a single scratch.

[0239] The electronic device (100) aggregates the total number of scratch objects defined in this way and calculates this as the number of surface scratches.

[0240] Thus, regardless of the length or thickness of the scratch, the number of actual visually perceptible damage units is reflected in the damage score calculation.

[0241] In the case of discoloration, simply determining its presence or absence or using only the area ratio may result in a lack of consistency with the concept of damage parameters. To prevent this, the degree of discoloration can be quantified and utilized based on the number of discolored regions or the number of discoloration clusters.

[0242] The electronic device (100) identifies pixels whose chromaticity deviation from the periphery exceeds a threshold from an image captured under conditions of complementary light source illumination as candidate pixels for discoloration. Subsequently, it performs connectivity component analysis on these candidate pixels for discoloration to group clusters of pixels that are adjacent to each other and have similar chromaticity deviation characteristics into a single discoloration region.

[0243] The electronic device (100) defines separate discoloration regions as independent discoloration clusters, and each cluster is treated as a single discoloration occurrence unit. For example, if color degradation occurs at different locations on the surface of an item, each location is separated into different discoloration clusters.

[0244] The electronic device (100) can calculate the number of such discolored clusters or calculate a discoloration degree value by weighting the average color deviation per cluster to the number of discolored clusters. In this case, the basic unit is also the number of discolored areas, which is structurally compatible with the damage parameter calculation system.

[0245] The number of bends is calculated by counting the number of distinguishable bend generation units, rather than using the area or bending strength of the region where bends exist.

[0246] The electronic device (100) identifies candidate regions where curvature occurs on the surface of an item through a reference straight line pattern reflection method or luminance deviation-based analysis. Subsequently, it analyzes pixel clusters within these candidate regions in which spacing changes or curvature changes appear continuously.

[0247] The electronic device (100) defines a continuous bending pattern as a single bending object based on the bending direction, centerline position, position of extreme curvature value, etc. Bend patterns that occur at different locations, have different bending directions, or are separated by more than a certain distance are separated into different bending objects.

[0248] In addition, even if there is a single wide curved area, if multiple maximum curvature points exist separately within it, it can be determined as multiple bends. The electronic device (100) calculates the number of bends by tallying the total number of such bending objects.

[0249] According to the above method, corner damage is calculated as the number of damaged objects within the corner area, surface scratches as the number of linear scratch objects, discoloration as the number or weight of discoloration clusters, and bending as the number of bending objects.

[0250] Therefore, the “damage parameter including the number of corner damages, the number of surface scratches, the degree of discoloration, and the number of warpings” can be understood as being calculated based on quantitative values ​​that can be clearly counted on an object-by-object basis, rather than a simple area-based judgment.

[0251] According to one embodiment, the processor (120) can identify the number of issuances of items identical to the target item in the item database (S420).

[0252] The electronic device (100) is configured to identify the number of issues of an item identical to the target item from an item database. Here, the number of issues is an indicator of how many quantities of the physical collectible item exist in the market, and is information intended to quantitatively reflect the scarcity of the item.

[0253] The electronic device (100) queries an item database using item identification information corresponding to a target item. This item identification information may include the type of item, series, year of publication, edition number, or other unique attribute information. Based on this attribute information, the electronic device (100) identifies items classified as the same item and checks the number of issues of the corresponding items.

[0254] The number of issues may be official data provided by a manufacturer or issuing institution, or it may be a value estimated based on transaction history or registration history accumulated during the operation of the platform. Regardless of the source or calculation method of the number of issues, the electronic device (100) treats it as a “number indicating the scarcity of the item” and reflects it in the subsequent calculation of the damage score.

[0255] The reason for considering the number of issues at this stage is that even with the same damage condition, the degree of value depreciation may be evaluated differently for high-scarcity items and low-scarcity items. The electronic device (100) obtains base information to ensure that the impact of the same damage on the item value is reflected more significantly as the number of issues decreases.

[0256] According to one embodiment, the processor (120) can check the damage parameter and the item damage score inversely proportional to the number of issues (S430).

[0257] The electronic device (100) is configured to determine the item damage score using the identified damage parameters and the number of issues. The item damage score is a quantitative indicator that comprehensively reflects the degree of physical damage and scarcity of the target item, and is subsequently used as a key input value for determining the status grade.

[0258] The electronic device (100) can be set so that the item damage score increases as the damage parameter increases. This reflects the fact that physical damage, such as corner damage, scratches, and bending, degrades the preservation state of the item. On the other hand, since the impact of the same damage parameter on the relative value of the item may decrease as the number of issues increases, the electronic device (100) can be set so that the damage score increases gradually as the number of issues increases.

[0259] That is, the electronic device (100) calculates an item damage score using a relationship that is directly proportional to the damage parameter and inversely proportional to the number of issues. At this time, the damage score does not depend only on a single type of damage, but is generated as a result value that comprehensively reflects multiple types of damage.

[0260] The electronic device (100) can internally manage the item damage score as a single continuous numerical value, and this value can be directly used for determining the grade or adjusting the trading price in a later stage. This damage score has technical significance in that it indicates “how severe the damage is” while simultaneously reflecting “how rare the item is that the damage occurred.”

[0261] According to one embodiment, the processor (120) can determine the grade of the target item among a plurality of grades based on the item damage score (S440).

[0262] The electronic device (100) is configured to determine the condition grade of a target item among a plurality of grades based on the calculated item damage score. Here, the condition grade is a classification system for intuitively expressing the overall preservation state of the item, and is designed to be easily understood by a user or a transaction participant.

[0263] The electronic device (100) determines which range the item's damage score belongs to by referring to the mapping relationship between the predefined damage score ranges and the grades. For example, a range with a low damage score corresponds to the highest grade, and as the damage score increases, it may be classified into lower grades. These grade ranges can be set to be adjustable according to the service operation policy.

[0264] The electronic device (100) may not only rely on scores in the grading process, but may also additionally consider combinations of damage types or the presence of specific damage. For example, it may be set to exclude from a specific higher grade if even one bend is present, which may be a setting that reflects the practices of the collecting item market or user expectations.

[0265] The determined condition grade is used as a standard for calculating the tradable price range of the target item, calculating the estimated sales amount, or displaying information provided to the user. That is, the electronic device (100) provides complex damage analysis results as user-friendly information by converting the item damage score into an intuitive expression called a grade.

[0267] The above method for managing item information is,

[0268] The above processor includes an operation to calculate the item damage score based on the following [Mathematical Formula 1], and

[0269]

[0270] S corresponds to the item damage score, k corresponds to the score scaling constant, N_issue corresponds to the number of issues of the same item as the target item, e corresponds to the natural constant, w_i corresponds to the weight per damage item, and d_i corresponds to the damage value of the i-th damage item among a total of n damage items.

[0271] The Item Damage Score S is a comprehensive indicator that simultaneously reflects the physical damage status and scarcity of a target physical collectible item. Rather than simply determining the presence of damage, the Item Damage Score numerically expresses how an item's value degrades non-linearly when multiple damage factors accumulate. In other words, S functions as a fundamental metric for determining condition grades and adjusting digital value.

[0272] The score scaling constant k is a scaling constant for adjusting the overall range of item damage scores. Mathematically, k serves to convert the relative degree of damage into a score range available in the actual service. For example, the electronic device (100) can adjust the item damage score to be suitable for an integer-based scoring system or grade range mapping by setting k to 1,000 or 10,000. k does not affect the relative relationship of damage judgment and can be selectively adjusted according to the service policy.

[0273] The issuance count N_issue represents the total quantity of the target item issued to the market. This is a key variable for numerically expressing the scarcity of physical collectible items. In the mathematical formula, the issuance count is used as the input for a logarithmic function, which designs the system so that the item's damage score decreases gradually as the issuance count increases. Conversely, for items with a very low issuance count, the decrease in the damage score is reflected relatively more significantly even at the same level of damage.

[0274] The natural constant e is an operation stabilization term added to the input of the logarithmic function. This prevents the logarithmic term from becoming zero even for items with a supply quantity of 1 or a very small number, thereby preventing operation errors where the denominator becomes zero in the entire mathematical expression. Technically, this is an essential correction factor to ensure the continuity and computability of the formula.

[0275] The damage value d_i represents a quantitative damage value for each type of damage. For example, d_1 can be defined as the edge damage parameter, d_2 as the number of surface scratches, d_3 as the number of warpings, and d_4 as the degree of discoloration.

[0276] The weight w_i is a weighting factor that reflects the relative impact each damage item has on the item's value. For example, since bending or corner damage can have a greater impact on value reduction than scratches, the corresponding w_i can be set to a relatively large value compared to other weights. Mathematically, the weight w_i is a factor used to reflect the asymmetric importance between damage items in the formula.

[0277] The electronic device (100) can analyze target image data received from a user terminal to calculate a damage value d_i corresponding to each damage item. As previously described, this process can be performed through edge damage cluster analysis based on the outer contour, identification of candidate bending regions based on reflection line patterns, linear damage cluster analysis within the surface region, and calculation of the degree of discoloration based on color distribution change. The electronic device (100) obtains a quantified damage value for each damage item through these analysis results.

[0278] The electronic device (100) can check the number of issues N_issue by querying the item database using the identification information of the target item. The number of issues can be confirmed based on data provided by the manufacturer, official issuance information, or internal platform data, or obtained through a request to an external server (220), and the electronic device (100) treats this as a scarcity indicator and reflects it in a mathematical formula.

[0279] The weight w_i and score scaling constant k are parameters pre-set in the electronic device (100) or can be updated based on statistical analysis or learning results during the service operation process. For example, if it is determined that a specific damage item has a significant impact on the price by analyzing past transaction history and actual transaction price fluctuation data, the electronic device (100) can increase the weight w_i of the item.

[0280] Conventional technology has estimated the degree of item damage based simply on the extent of the damage. However, this method fails to adequately reflect the actual characteristics of the collecting market, where value collapses rapidly as damage accumulates.

[0281] Mathematical formula 1 adopts a double nonlinear structure that applies a logarithmic function to the damage item and then wraps it with an exponential function, so that the score decrease is gradual in the early stages of damage, but decreases exponentially as damage accumulates.

[0282] Existing inventions have failed to accurately reflect the scarcity of items in depreciation. On the other hand, Equation 1 is structurally designed to apply a logarithmic function to the number of issues N_issue, so that rarer items respond more sensitively to the same damage. This mathematically implements the collecting market practice that “perfection is more important for rare items.”

[0283] Equation 1 is not a list of specific functions, but rather has a structure in which elements reflecting the non-linearity of damage accumulation, the diminishing effect of scarcity, and the relative importance among damage items are logically combined. As a result, it can be clearly understood that Equation 1 is not a combination of arbitrary choices.

[0284] Since the electronic device (100) always produces the same damage value and the same item damage score S for the same image input, consistent damage evaluation is possible without user subjectivity or appraiser deviation.

[0285] Mathematical Formula 1 is designed to be more sensitive to damage the rarer the item, thereby reasonably reflecting the impact of minor damage on the value of high-value items. This can improve transaction reliability in the high-value collecting market.

[0286] The item damage score S can be used as a direct input for determining the condition grade and calculating the tradable price range. This automates the entire process from condition assessment to digital value linkage, enabling the maintenance of consistent standards even in large-scale item processing environments.

[0287] The damage score based on mathematical formula 1 has a clear and reproducible calculation logic, which provides the effect of reducing condition assessment disputes that may occur in transactions between users. This has a technical effect of substantially improving the reliability of the entire collecting platform.

[0289] The operation of extracting the state feature information including the surface scratch mentioned above is,

[0290] The above processor identifies a surface area excluding the corner area based on the outer contour of the above target item;

[0291] The above processor identifies a linear pixel change pattern within the surface region that contrasts with a surrounding region as a scratch candidate;

[0292] The above processor performs the operation of clustering the above scratch candidate pixels, which are spatially adjacent to each other and have continuous directionality, into a single scratch candidate line segment; and

[0293] The processor includes an operation of calculating the number of separated scratch candidate line segments as the number of surface scratches present on the target item and including this in the state feature information.

[0294] The electronic device (100) is configured to identify a surface area of ​​a target item from target image data, and then analyze linear damage traces within the surface area to calculate the number of surface scratches.

[0295] Here, the “surface area” refers to the central or flat part of the target item, excluding the corner areas, and is the area where damage primarily occurs due to scratches or friction.

[0296] The electronic device (100) can analyze changes in brightness, color, or texture continuity of pixels within a surface area to identify linear patterns that contrast with surrounding areas as scratch candidates. Generally, surface scratches have a continuous shape in the longitudinal direction and are characterized by a narrow width and a relatively long length. The electronic device (100) distinguishes them from dot-shaped noise or stains based on these morphological characteristics.

[0297] The electronic device (100) spatially connects the identified scratch candidate pixels to form a single scratch candidate line segment. At this time, by recognizing only a set of pixels that are continuously connected for a certain length or longer as a valid scratch candidate, it is possible to prevent misidentification caused by minute shooting noise or printing patterns. That is, a scratch candidate line segment is determined to be an actual scratch only if it satisfies at least one of the minimum length criterion, the minimum contrast criterion, or the direction consistency criterion.

[0298] The electronic device (100) treats separated scratch candidate line segments as independent scratches and calculates the number of such segments as the number of surface scratches. For example, if there are three separate scratch marks on the surface of a target item, the electronic device (100) determines them as three scratches. On the other hand, if a single scratch maintains continuity even if it is curved or its direction changes slightly, it may be counted as a single scratch.

[0299] As such, the number of surface scratches is defined as the “number of results obtained by clustering linear damage candidates into physical damage units,” which provides repeatable results despite changes in shooting resolution or lighting conditions. The calculated number of surface scratches is subsequently used as a quantitative input value in the item damage score calculation step.

[0301] The operation of extracting the state feature information including the above discoloration is,

[0302] The above processor performs the operation of obtaining reference color information corresponding to the target item from an item database;

[0303] The above processor performs the operation of dividing the surface area of ​​the target item included in the target image data into a plurality of sub-regions;

[0304] The above processor performs the operation of calculating a color deviation between an actual color value and the reference color information for each of the plurality of sub-regions; and

[0305] The processor calculates the degree of discoloration by synthesizing the distribution and deviation magnitudes of sub-regions where the color deviation satisfies preset discoloration judgment criteria, and includes the degree of discoloration in the state feature information.

[0306] The electronic device (100) is configured to calculate the degree of discoloration by analyzing the difference between the reference color state of the target item and the actual captured color state from the target image data. Here, the reference color state refers to the color distribution expected when the target item is in a normal state without damage, and this can be defined based on at least one of a reference image stored in an item database, color information at the time of manufacture, or average color information of the same item.

[0307] The electronic device (100) divides the surface area of ​​the target item into a plurality of sub-regions and calculates an average color value or a representative color value for each sub-region. Subsequently, the difference between the color value of each sub-region and the reference color value is calculated to quantitatively express the color deviation. At this time, the color deviation can be defined as a distance value in a specific color space or a combination of brightness and hue changes.

[0308] The electronic device (100) does not immediately determine a fine color difference in a single sub-area as discoloration, but determines it as a discolored area only when a color deviation appears continuously over an area larger than a certain size. For example, it can distinguish and process cases where the color fades locally in a specific area and cases where the entire item is uniformly discolored.

[0309] The electronic device (100) calculates the degree of discoloration by combining the size of the color deviation and the distribution area of ​​the discolored regions identified in this way. The degree of discoloration can be expressed as a single continuous numerical value and can be divided into stages, for example, such as a state with almost no discoloration, mild discoloration, moderate discoloration, and severe discoloration. At this time, each stage can be defined based on the average value, maximum value of the color deviation, or the ratio of the area of ​​the discolored region.

[0310] The electronic device (100) includes the calculated degree of discoloration in the state feature information and uses the value as a weighted input value in the item damage score calculation step. That is, it can be configured to increase the damage score by determining that the visual quality of the item has deteriorated as the degree of discoloration increases.

[0312] The operation of calculating the degree of discoloration among the above state feature information is,

[0313] The operation of the processor controlling the target item to be illuminated through the display of the user terminal with a specific color light source that is in a complementary relationship with a reference color previously registered for the target item; and

[0314] The above processor includes an operation of analyzing pixel value fluctuations within an image captured under conditions where the specific color light source is irradiated, and determining an area where the chroma deviation from the surroundings is greater than or equal to a set threshold as a discolored area.

[0315] The operation of calculating the degree of discoloration among the state feature information is performed not by simply comparing the color distribution of captured images, but by artificially creating optical conditions that actively stimulate the color characteristics that the target item should inherently possess, and then analyzing the response.

[0316] Specifically, the electronic device (100) controls the display of the user terminal to output a specific color light source that is in a complementary relationship with a reference color previously registered for the target item.

[0317] Here, “standard color” may be manufacturer information, color data at the time of release, or standard color values ​​stored in the item database.

[0318] The electronic device (100) selects a complementary color corresponding to this reference color, thereby inducing that color absorption and reflection characteristics appear consistently on the surface of the item in a normal state.

[0319] An image captured under conditions where such a specific color light source is irradiated maximizes the difference in chromaticity response between an area without discoloration and an area where discoloration occurs. The electronic device (100) analyzes the variation in pixel values ​​within the image to identify areas where the chromaticity deviation relative to the periphery under the same light source conditions exceeds a set threshold. This chromaticity deviation is determined to be due to a change in the color component of the material itself, rather than a simple change in lighting or shadows.

[0320] Based on these judgment results, the electronic device (100) determines the area where the color deviation exceeds a threshold as the discolored area and includes the area, distribution, or intensity of the discolored area as state feature information indicating the degree of discoloration. Unlike a simple RGB average comparison method, this method provides a technical effect that can precisely reflect whether the color of the actual item has deteriorated while minimizing errors due to the shooting environment.

[0322] The operation of extracting surface scratches from the above state feature information is,

[0323] The above processor receives a plurality of angle-specific captured images synchronized with tilt information obtained from the gyro sensor of the user terminal; and

[0324] The above processor includes an operation of tracking the rate of change of light intensity of specular reflection between the plurality of angle-based captured images in pixel units, and identifying points where discontinuous luminance peaks occur only at specific angles as fine scratches.

[0325] The operation of extracting surface scratches from state feature information is performed not by judging solely based on changes in brightness appearing in a single image, but by utilizing the angle dependence of specular reflection characteristics occurring on the item surface.

[0326] The electronic device (100) uses tilt information obtained from a gyroscope sensor provided in the user terminal (210) to induce the user to photograph a target item while tilting it at different angles, or receives multiple angle-specific images synchronized with the tilt information. At this time, each photograph corresponds to the result of photographing the same surface area under different angles of incidence conditions.

[0327] The electronic device (100) tracks changes in specular reflection light intensity on a pixel-by-pixel basis among multiple angle-by-angle captured images received in this way. In the case of a normal flat surface, the specular reflection light intensity changes in a continuous and predictable manner according to changes in angle. On the other hand, if fine scratches are present, discontinuous luminance peaks occur where reflected light is locally concentrated or rapidly decreases only at specific angles.

[0328] The electronic device (100) utilizes these characteristics to identify pixel points where luminance peaks are detected only at specific angles among multiple images as fine scratches. This allows for clear distinction between normal surface elements, such as printing patterns or material patterns, and actual scratches, and enables reliable calculation of the number and distribution of scratches.

[0330] The above method for managing item information is,

[0331] The above processor performs the operation of checking the shooting time included in the target image data received from the user terminal; and

[0332] The above processor includes an operation to block the calculation of a status grade for the target item when the shooting time exceeds a preset valid time interval.

[0333] The item information management method sets the appropriateness of the shooting time itself as an evaluation condition in the stage prior to state analysis. The electronic device (100) checks the shooting time information included in the target image data received from the user terminal.

[0334] The electronic device (100) stores a preset effective time interval, which can be defined, for example, as a time period when natural light is stably secured or a time period when indoor lighting conditions are relatively uniform. If the shooting time falls outside this effective time interval, the electronic device (100) determines that the image is highly likely to be significantly affected by lighting deviations or environmental noise.

[0335] Accordingly, the electronic device (100) blocks the calculation of the status grade for the target item itself when the shooting time falls outside the valid time interval. This serves as a technical means to prevent the status grade from being distorted due to an image generated under incorrect shooting conditions.

[0337] The above method for managing item information is,

[0338] The above processor performs the operation of receiving location information along with the target image data from the user terminal; and

[0339] The above processor includes an operation of extracting state feature information only when the location information is included in a pre-registered list of allowed evaluation locations.

[0340] The electronic device (100) receives location information from the user terminal (210) along with target image data. This location information may be GPS coordinates, network-based location information, or a pre-registered place identifier.

[0341] The electronic device (100) stores a list of locations allowed for evaluation, and the list may include, for example, locations where indoor shooting is possible, locations of certified shooting booths, or user registration addresses. The electronic device (100) determines whether the received location information is included in this list of locations allowed for evaluation.

[0342] The electronic device (100) performs state feature information extraction only when the location information is included in the allow list. Conversely, for images taken at unallowed locations, the extraction of state feature information is restricted because it is determined that there is a high degree of uncertainty regarding external environmental factors or lighting conditions. Through this, the reliability of the evaluation can be managed based on location.

[0344] The above method for managing item information is,

[0345] The above processor performs the operation of analyzing the luminance distribution included in the target image data and calculating an average luminance value; and

[0346] The above processor includes an operation in which, if the average luminance value deviates from a preset luminance range, it does not perform a state grade calculation for the target item.

[0347] The electronic device (100) analyzes the total luminance distribution included in the target image data and calculates an average luminance value. This average luminance value can be calculated based on the entire image or the area where the target item is located.

[0348] The electronic device (100) stores a preset appropriate brightness range, which is a reference value for excluding overexposure or underexposure conditions during shooting. If the average brightness value falls outside this range, the electronic device (100) determines that detailed damage features within the image are likely to be excessively lost or exaggerated.

[0349] Accordingly, the electronic device (100) does not perform a status grade calculation for the target item when the average luminance value falls outside the set range. This method provides the effect of structurally blocking evaluations that are performed when image quality is not secured.

[0351] The operation of extracting the above state feature information is,

[0352] The above processor controls the display of the user terminal to output a white light source of maximum brightness, and acquires an image captured while the target item is placed on the display;

[0353] The above processor analyzes the transmittance distribution of a light source from the captured image and identifies linear regions with discontinuously high light transmittance compared to the periphery as interlayer separation or microcracks within the item; and

[0354] The above processor includes an operation of determining the state grade of the target item to the lowest grade in response to the detection of interlayer separation or microcracks within the item.

[0355] The operation of extracting state feature information can be extended to determine whether there are abnormalities in the internal structure of the item as well as on the surface of the item. The electronic device (100) controls the display of the user terminal to output a white light source of maximum brightness and acquires an image taken with the target item placed on the display.

[0356] Under these imaging conditions, normal items exhibit a continuous distribution of light transmittance due to their uniform internal material structure. In contrast, if interlayer separation or microcracks are present, light transmittance increases locally or a discontinuous pattern appears in the affected areas.

[0357] The electronic device (100) analyzes the transmittance distribution of the light source from the captured image and identifies linear regions where the amount of light transmittance is discontinuously higher compared to the periphery. These linear regions are determined to be interlayer separation or microcracks within the item.

[0358] When an electronic device (100) detects interlayer separation or micro-cracks within an item, it determines that such damage is irreparable and fundamentally impairs the structural stability of the item, unlike external damage, and determines the condition grade of the target item to the lowest grade. This provides a technical effect of clearly distinguishing between external damage and internal damage when evaluating them.

[0360] The various thresholds, reference values, judgment criteria, and setting values ​​used in the present invention are not selected arbitrarily, but are reasonably set based on the results of a prior analysis of physical collecting items in a normal state. The electronic device (100) can calculate the average value, variance value, or distribution range for each state feature information by referring to normal item samples stored in the item database or image data of items for which evaluation was completed in the past.

[0361] For example, the threshold value for the rate of change relative to pixels used to determine edge damage can be set by multiplying the average value of the rate of change relative to pixels measured in the edge area of ​​a normal item by a preset correction factor. The threshold value for the rate of change in specular light intensity used to determine surface scratches can be set based on the maximum value of light intensity change observed on a surface where no scratches exist. The chromaticity deviation criterion used to determine discoloration can be set based on the statistical distribution of the amount of chromaticity change relative to the reference color of a normal item.

[0362] In addition, the threshold for spacing change or curvature change used for warping judgment is set based on the average spacing or average curvature of a reference straight line pattern reflected from a flat item surface, and warping may be determined to exist only when a deviation of a certain ratio or more occurs. Critical conditions related to the shooting time, luminance range, and location information may also be set within a predefined range based on environmental conditions where the reliability of the state analysis is ensured.

[0363] As such, since the threshold and reference values ​​in the present invention are set based on the results of prior data analysis regarding the characteristics of items in a normal state, those skilled in the art can reproduce the extraction of state feature information and the calculation of the number of damages using the same criteria. Accordingly, the present invention effectively prevents problems where implementation is difficult or judgment criteria become unclear due to the arbitrariness of setting thresholds.

[0365] The above description is merely an illustrative explanation of the technical concept of the present invention, and those skilled in the art to which the present invention pertains will be able to make various modifications and variations within the scope of the essential characteristics of the present invention.

[0366] Accordingly, the embodiments disclosed in this invention are intended to illustrate, not limit, the technical concept of the invention, and the scope of the technical concept of the invention is not limited by these embodiments. The scope of protection of this invention shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of this invention.

Claims

Claim 1 A method for managing item information, which is performed by an electronic device including a processor and classifies the condition of an AI-based physical collectible item to link with its digital value, comprises: the processor receiving target image data including a captured image of a target item from a user terminal; the processor extracting condition feature information from the target image data, including at least one of corner damage, surface scratches, discoloration, or warping of the target item; the processor calculating a condition grade corresponding to the target item according to a predefined condition determination criterion based on the condition feature information; the processor adjusting a tradable price range and an estimated sale amount of a digital item linked to the target item based on the condition grade; and the processor linking the condition grade, the tradable price range, and the estimated sale amount to target item information corresponding to the target item and providing them to the user terminal. The operation of extracting the condition feature information from the target image data comprises the processor detecting an outer contour of the target item within the target image data and then identifying a corner area based on the outer contour. and the processor includes an operation of determining the number of points where the rate of change relative to a pixel set within the corner area exceeds a reference value as the number of corner damages and including this in the state feature information, and the operation of extracting the state feature information from the target image data includes the processor controlling the output of a plurality of mutually parallel reference straight line patterns through the screen of the user terminal before the shooting of the target item is performed, and acquiring the target image data to include an image in which the surface of the target item is shot with the reference straight line patterns reflected.A method for managing item information, comprising: an operation in which the processor detects a plurality of reflected lines corresponding to a reflected image of a reference straight line pattern within the target image data and calculates a change in spacing and a change in curvature between adjacent reflected lines; and an operation in which the processor identifies a location where the change in spacing or the change in curvature satisfies a preset bending judgment criterion as a bending candidate region, and determines the number of the bending candidate regions that are spatially separated from each other as the number of bends existing in the target item and includes this in the state feature information. Claim 2 delete Claim 3 delete Claim 4 In claim 1, the operation of extracting state feature information from the target image data comprises: an operation in which, if the change in spacing or the change in curvature does not satisfy a preset bending judgment criterion, the processor generates a luminance deviation matrix by comparing a virtual reference plane defined by connecting the four corner vertex coordinates of the target item with the distribution of actual received pixel-by-pixel luminance data; an operation in which the processor generates a brightness contour line by linearly connecting pixels having the same luminance deviation within the luminance deviation matrix; and an operation in which, after identifying a closed loop shape among the brightness contour lines, the number of target closed loop shapes among the closed loop shapes whose internal area exceeds 15% of the total area of ​​the target item is determined as the number of bends existing in the target item and included in the state feature information. Claim 5 In claim 4, the item information management method comprises: an operation in which the processor identifies damage parameters including the number of corner damages, the number of surface scratches, the degree of discoloration, and the number of warping based on the state feature information; an operation in which the processor identifies the number of issuances of an item identical to the target item in an item database; an operation in which the processor confirms an item damage score inversely proportional to the damage parameters and the number of issuances; and an operation in which the processor determines the grade of the target item among a plurality of grades based on the item damage score.

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