Information processing device, information processing method, and information processing program for the security management of commodity transactions.

Abstract

We provide an information processing device, an information processing method, and an information processing program for securely conducting transactions of precious metals such as gold and platinum, brand-name goods, luxury watches, and jewelry. [Solution] The information processing device for managing the security of commodity transactions includes a server 10 which comprises: an XRF acquisition unit 1041 that acquires spectral data of fluorescent X-rays irradiated onto a specific product; a material analysis AI unit 1042 that generates extracted data from the spectral data including the peak position, peak intensity, peak width and trace element distribution of each element, estimates the content of precious metals including gold, platinum, palladium, or silver based on the extracted data, and detects hollow structures, foreign material contamination, plating, or the use of counterfeit metals based on abnormal values ​​in the spectral pattern of the specific product; and a material authenticity determination unit 1043 that uses the material analysis results, which are the estimation results from the material analysis AI unit, to determine the authenticity of the specific product.

Description

[Technical Field]

[0001] This invention relates to an information processing device, an information processing method, and an information processing program for securely conducting transactions of precious metals such as gold and platinum, brand-name goods, luxury watches, jewelry, and the like. [Background technology]

[0002] In recent years, trading methods for precious metals such as gold and platinum, as well as branded goods, luxury watches, jewelry, etc. (hereinafter referred to as "specific goods"; details will be described later), have diversified to include over-the-counter transactions, mail-order transactions, on-site transactions, and online transactions. Accordingly, server- and client-type information processing devices have been proposed to manage transactions that are not face-to-face transactions at over-the-counter locations. For example, Japanese Patent Publication No. 2012-155639 describes an information processing device for managing precious metal transactions. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2012-155639 [Overview of the project] [Problems that the invention aims to solve]

[0004] However, the following serious problems exist. (1) Distribution of counterfeit goods, plated goods, and goods containing foreign materials Numerous counterfeit items have been discovered that are gold or platinum mixed with tungsten and other materials. (2) The increasing sophistication of counterfeit branded goods The technology for counterfeiting brand-name bags and watches has become so advanced that it is difficult to distinguish them from genuine items using traditional visual inspection methods. (3) Misuse for anti-social forces and profiting from crime Because precious metals are easily converted into cash, they are misused for laundering funds from organized crime and criminal activities. (4) Substitution, breaking of seals, and misrepresentation of weight during mailing Fraudulent practices such as sending items different from the images, breaking seals, and falsifying weight are becoming frequent. (5) Problems of cross - border counterfeiting and money laundering due to the increase in international transactions For example, in the aforementioned Japanese Patent Application Laid - Open No. 2012 - 155639, only limited solutions for only limited products in a very limited field are disclosed, and it has been difficult to comprehensively and integrally solve various serious problems associated with specific commodity transactions.

[0005] In view of the above problems, the object of the present invention is 1. Enhancement of authenticity determination (materials, images, brands, damages) 2. Integration of scientific analysis and AI analysis, 3. Minimization of comprehensive risks including anti - social forces and crimes, 4. Response to double KYC (identity verification) and anti - money laundering (AML), 5. Securing of delivery and logistics, 6. Market soundness, 7. API, AI fail - safe, self - recovery, 8. Ensuring the safety, convenience and transparency for both users and franchisees etc., and to solve these problems with a single integrated platform having safety, continuity and explainability based on material analysis such as XRF and others.

Means for Solving the Problems

[0006] The information processing apparatus of the present disclosure is an information processing apparatus for commodity transaction safety management including determining the authenticity of materials of specific commodities including precious metals, jewelry, or branded products which are objects of trading transactions, an XRF acquisition unit that acquires spectrum data of fluorescent X - rays irradiated on the specific commodity, generates extraction data including peak positions, peak intensities, peak widths, and trace element distributions of each element from the spectrum data, estimates the content rate of precious metals including gold, platinum, palladium, or silver based on the extraction data, and a material analysis AI unit that detects a hollow structure, mixing of different materials, plating treatment, or use of counterfeit metals based on abnormal values of the spectrum pattern of the specific commodity. A commodity trading safety management information processing apparatus includes a material authenticity determination unit that uses a material analysis result, which is an estimation result by the material analysis AI unit, for determining the authenticity of the specific commodity.

[0007] Also, the information processing apparatus of the present disclosure includes a specific gravity measurement unit that calculates a specific gravity value based on the weight and volume of an object, a magnetic inspection unit that detects the presence or absence of magnetic field response, the mixing of different materials based on the magnetic field strength, or tungsten substitution, and an ultrasonic analysis unit that detects internal cavities, layered structures, or density unevenness based on the reflection waveform of ultrasonic pulses. The above-described commodity trading safety management information processing apparatus may further include a composite material analysis unit that inputs the specific gravity value, magnetic response, or ultrasonic reflection characteristics to the material analysis AI unit to perform a composite material determination.

[0008] Also, the information processing apparatus of the present disclosure uses, as inputs, three or more of the integrated feature quantities including the engraving features, damage features, or appearance features obtained from the image analysis AI unit, the sewing features, logo features, or internal structure features obtained from the brand authenticity AI unit, or the element content rate, the specific gravity value, magnetic response, or ultrasonic reflection pattern obtained from the material analysis AI unit. Based on the integrated feature quantity, it calculates a comprehensive authenticity index indicating the authenticity of the transaction object. The above-described commodity trading safety management information processing apparatus may further include an integrated authenticity AI unit. <000014>

[0009] Also, the information processing apparatus of the present disclosure extracts damage features including scratches, defects, dents, polishing marks, and wear marks from a commodity image, analyzes the relevance between the damage features, the material analysis result, and the comprehensive authenticity index, and may further include a damage value correction AI unit that calculates a rating correction value according to the type, position, depth, and range of the damage.

[0010] Furthermore, the information processing device of this disclosure takes as input three or more of the following: the overall authenticity index, the anti-social activity index, the anti-money laundering score, the behavioral abnormality index, or the delivery risk index which includes at least one of the following: deviation from the delivery route, abnormal sealing tag, or weight difference. The above-mentioned information processing device for commodity trading security management may further include a risk integration AI unit that calculates a comprehensive risk index indicating the overall risk level of the transaction based on the aforementioned input.

[0011] Furthermore, if the overall risk index exceeds a predetermined threshold, the information processing device of this disclosure will... (1) Prohibition of postal transactions, (2) Limitation to over-the-counter transactions, (3) Additional identity verification request, (4) Transaction suspension linked to banks, (5) The above-mentioned information processing device for managing the security of commodity transactions may further include a transaction restriction control unit that automatically controls at least one of the following: sealing reinforcement, dual weight measurement, or the application of a delivery reinforcement mode including surveillance camera recording.

[0012] Furthermore, the information processing device disclosed herein acquires at least two of the following from a customer's information processing terminal, such as a trading terminal: GPS positioning information, surrounding WiFi access point information, or IP address-derived location estimation information. By analyzing the degree of consistency of these location data, temporal consistency, and the rationality of the movement trajectory, The aforementioned information processing device for managing the security of commodity transactions may further include a multi-location verification unit that calculates the reliability of the customer's current location.

[0013] Furthermore, the information processing device disclosed herein uses customer information processing terminal behavior data obtained from at least one of an acceleration sensor, a gyroscope, a barometric pressure sensor, or a tilt sensor, By comparing changes in location information, movement speed, and radio wave intensity, The aforementioned information processing device for managing the security of commodity transactions may further include a location spoofing detection unit that calculates a location spoofing index indicating the possibility that the location of the customer's information processing terminal has been falsified, including GPS spoofing.

[0014] Furthermore, the information processing device disclosed herein will, based on at least one of the following: the OS version of the customer's information processing terminal, such as a trading terminal, the security patch application status, the rooted or jailbroken status, the installed application status, the history of past fraudulent behavior, or the location spoofing index, The aforementioned information processing device for managing the security of commodity transactions may further include a terminal evaluation unit that calculates a device reliability score indicating the security and reliability of the customer's information processing terminal.

[0015] Furthermore, the information processing device of this disclosure refers to at least one of the following for each delivery company: past accident rate, loss rate, delay rate, damage rate, or risk index of the delivery route. The aforementioned information processing device for managing the security of commodity transactions may further include a delivery risk assessment unit that calculates a risk index for each delivery company and a risk index for delivery routes.

[0016] Furthermore, the information processing device of the present disclosure may further include an emergency stop control unit that immediately suspends a transaction, delivery process, or fund transfer process if three or more of the following exceed a predetermined risk threshold: an anti-social activity index, an anti-money laundering score, a delivery anomaly index, a location falsification index, or a device reliability score.

[0017] Furthermore, the information processing device of this disclosure, when it detects a response delay, error response, no response, or inconsistent response of the anti-social matching API, banking API, delivery API, or insurance API, The aforementioned information processing device for managing commodity transaction security may further include a fail-safe switching unit that maintains the continuity of transaction processing by automatically switching to at least one of an alternative API, cached data, or local estimation logic.

[0018] Furthermore, the information processing device of this disclosure vectorizes at least one of the following: product image, engraving features, material analysis features, or brand features. By calculating the degree of deviation from past product features recorded in the vector database, The aforementioned information processing device for managing the security of product transactions may further include a similarity feature search AI unit that extracts candidates that match patterns of similar products, related products, or counterfeit products.

[0019] Furthermore, the information processing device of this disclosure has a minimum requirement for at least one of the following: marking features, brand features, material features, damage features, or delivery features. Based on the difference from known genuine data, statistical outliers, anomalous correlation patterns, or the degree of deviance in the trained feature space, The aforementioned information processing device for managing the security of commodity transactions may further include a counterfeit anomaly detection AI unit that calculates a counterfeit anomaly index indicating the suspicion of unknown counterfeit goods.

[0020] Furthermore, the information processing device of this disclosure integrates at least two of the following: delivery tracking data including location, time, and dwell time; seal tag image difference; weight difference; IoT sensor data; or delivery company risk information. The aforementioned information processing device for managing the security of commodity transactions may further include a delivery anomaly analysis unit that calculates a delivery anomaly index indicating deviations, impacts, substitutions, seal breakage, or internal fraud during the delivery process.

[0021] Furthermore, the information processing device of this disclosure is based on at least one of the following: the overall authenticity index, the delivery anomaly index, the seal tag detection result, the weight difference result, the damage analysis result, or the delivery company log. Estimate the possibility of a delivery accident or damage occurring. The aforementioned information processing device for managing the security of commodity transactions may further include an automated insurance claim AI unit that automatically extracts items requested by insurance companies, including the cause of the accident, evidence data, time of occurrence, delivery route, or product information, and generates insurance claim data.

[0022] Furthermore, the information processing device disclosed herein uses machine learning to acquire at least one of the following data from member stores: appraisal results, authenticity matching rate, damage detection accuracy, price deviation rate, claim occurrence rate, processing speed, or counterfeit detection history for each member store. We will calculate an appraisal quality index for each member store, The aforementioned information processing device for managing the security of commodity transactions may further include an AI unit for franchisee training that presents assessment procedures, points to note, or areas for improvement based on the aforementioned assessment quality indicators.

[0023] Furthermore, the information processing device disclosed herein extracts recommendation features from at least one of the following: customer transaction history, product category, brand, material authenticity index, price range, or market demand index. The aforementioned information processing device for managing the security of commodity transactions may further include a transaction recommendation AI unit that presents a suitable transaction method, recommended merchant, or appropriate price range to the trader based on the aforementioned recommendation features.

[0024] Furthermore, the information processing device disclosed herein takes at least three of the following as input: counterfeit product detection rate, anti-social activity rate, anti-money laundering activity rate, delivery accident rate, assessment discrepancy rate, regional security index, or legitimate transaction rate. The aforementioned information processing device for commodity trading security management may further include a market health analysis unit that calculates a market health index indicating the overall health of the market.

[0025] Furthermore, the information processing device disclosed herein is based on at least one of the following: the customer's age group, user experience, the customer's information processing terminal performance, screen size, operation history, or language settings. The aforementioned information processing device for managing the security of commodity transactions may further include an adaptive UI control unit that automatically adjusts the display items, layout, explanatory content, and operation guidance.

[0026] Furthermore, the information processing device disclosed herein may handle at least a portion of the following: name, address, biometric information, customer information processing terminal ID and other transaction identification information, financial information, location information, or delivery information. To ensure that personal identification becomes impossible after the retention period has expired, the data is converted into anonymized data by performing at least one of the following processes: deletion, masking, random number substitution, or hashing. The anonymized data can be used for authenticity detection model training, risk analysis, or market statistics. The aforementioned information processing device for managing the security of commodity transactions may further include a data anonymization processing unit.

[0027] Furthermore, the information processing device disclosed herein includes at least one of the following: authenticity determination AI, behavioral abnormality AI, anti-social group analysis AI, anti-money laundering scoring AI, or delivery abnormality AI. Analyze statistical deviations, inter-group imbalances, or excessive influence on specific attributes caused by input features. In addition to calculating an equity index that shows the degree of bias, The aforementioned information processing device for managing the security of commodity transactions may further include an AI fairness correction unit that automatically applies at least one of the following processes: adjustment of feature weights, retraining, feature exclusion, or correction processing, if the fairness index deviates from an acceptable range.

[0028] Furthermore, if the information processing device of this disclosure detects a response delay, error response, no response, or content inconsistency with at least one of the following APIs: anti-social API, financial API, delivery API, insurance API, or market data API, Automatically switches to at least one of the following: alternative API, cached data, or local estimation model. The aforementioned information processing device for commodity transaction security management may further include an API anomaly control unit that maintains continuous transaction processing.

[0029] Furthermore, the information processing device disclosed herein analyzes at least one of the following: differences in markings, abnormal material composition, abnormal weight patterns, abnormal delivery patterns, behavioral inconsistencies, or tendencies to indicate suspected anti-social activities or anti-money laundering, which are characteristic of counterfeit goods. Automatically generate fraud templates representing new forgery techniques or fraudulent behavior patterns. The aforementioned information processing device for managing the security of commodity transactions may further include a fraudulent template learning unit that causes each AI model to continuously learn the aforementioned fraudulent templates.

[0030] Furthermore, if a failure, abnormal load, error, or functional degradation is detected in at least one of the following modules of the information processing device disclosed herein: AI inference module, material analysis module, delivery analysis module, anti-social matching module, or financial API integration module, The aforementioned information processing device for managing the security of commodity transactions may further include an autonomous recovery engine that autonomously performs at least one of the following to recover: cause determination, activation of an alternative module, reconfiguration, or cache restoration.

[0031] Furthermore, this disclosure can be viewed from the perspective of information processing methods. That is, the information processing methods of this disclosure A method for managing the security of commodity transactions, which includes determining the authenticity of the materials of specific goods, including precious metals, jewelry, or branded goods, that are the subject of trading, An XRF acquisition step to acquire spectral data of fluorescent X-rays irradiated onto the specified product, Extracted data including the peak position, peak intensity, peak width, and trace element distribution of each element is generated from the spectral data. Based on the extracted data, the content of precious metals including gold, platinum, palladium, or silver is estimated, A material analysis AI processing step that detects a hollow structure, foreign material inclusion, plating treatment, or use of counterfeit metal based on anomalies in the spectral pattern of the specified product, This is an information processing method for managing the security of product transactions, which includes an information processing method for material analysis, and a material authenticity determination step, which includes using the material analysis results, which are estimation results from the material analysis AI processing step, to determine the authenticity of the specified product.

[0032] Furthermore, this disclosure can be viewed from the perspective of an information processing program. That is, the information processing program disclosed herein is an information processing program for managing the security of commodity transactions, which includes determining the authenticity of the materials of specific goods, including precious metals, jewelry, or branded goods that are the subject of trading. On the computer, An XRF acquisition step to acquire spectral data of fluorescent X-rays irradiated onto the specified product, Extracted data including the peak position, peak intensity, peak width, and trace element distribution of each element is generated from the spectral data. Based on the extracted data, the content of precious metals including gold, platinum, palladium, or silver is estimated, A material analysis AI processing step that detects a hollow structure, foreign material inclusion, plating treatment, or use of counterfeit metal based on anomalies in the spectral pattern of the specified product, This is an information processing program for managing the security of product transactions, which executes a material authenticity determination step in which the material analysis results, which are estimation results from the material analysis AI processing step, are used to determine the authenticity of the specified product. [Effects of the Invention]

[0033] According to this disclosure, by constructing a single, integrated security platform with security, continuity, and transparency based on material analysis using XRF and other methods, it is possible to provide a commodity transaction security management information processing device, information processing method, and information processing program that ensure the authenticity, transaction security, and fairness of specific goods, including precious metals, jewelry, watches, or branded goods, while executing transactions. [Brief explanation of the drawing]

[0034] [Figure 1] This is a conceptual diagram illustrating the configuration of the information processing device 1 for managing the security of commodity transactions in one embodiment of the present disclosure. [Figure 2] This is a block diagram showing the functional configuration of a server 10 that enables the operation of a commodity transaction security management information processing device 1 in one embodiment of the present disclosure. [Figure 3]This diagram illustrates the data structure of the data table 1010 stored in the server 10. [Figure 4] This is an example flowchart illustrating the operation of automatically determining the authenticity of a specific product using an XRF acquisition unit, a material analysis AI unit, and a material authenticity determination unit. [Figure 5] This is a block diagram showing the basic hardware configuration of a typical computer 90 that constitutes an information processing device. [Modes for carrying out the invention]

[0035] The terms used herein are defined as follows:

[0036] In this disclosure, "specified goods" refers to precious metals such as gold and platinum, branded goods, luxury watches, jewelry, etc. However, even general industrial products may be considered specified goods if this disclosure is partially effective.

[0037] In this disclosure, AI refers to the functions of a computer system equipped with intelligent functions such as reasoning and judgment. It has a data storage and reasoning unit, but is not necessarily required to be a generative AI.

[0038] Preferred embodiments of the present disclosure are described below. In all the figures illustrating the embodiments, common components are denoted by the same reference numerals, and repeated descriptions are omitted. The following embodiments are not intended to unduly limit the content of the present disclosure as described in the claims. Not all components shown in the embodiments are necessarily essential components of the present disclosure. Also, each figure in the drawings is a schematic diagram and is not necessarily a strict illustration.

[0039] <Configuration of information processing device for commodity transaction security management> The following description will be based on reference to diagrams related to the information processing equipment.

[0040] As shown in Figure 1, the information processing device 1 for managing the security of commodity transactions in this disclosure consists of a connected server 10 and a management information processing terminal 15. In addition, an information processing terminal 17 to which an XRF device 18 is connected, and any information processing terminals 20, 30 such as those of customers can be connected. However, the connection of the XRF device 18, information processing terminal 17, and any information processing terminals 20, 30 such as those of customers is just an example. For example, the XRF device 18 may be connected to the management information processing terminal 15, and data measured by an XRF device not shown in the figure may be acquired externally.

[0041] Some or all of them may be connected by network N. Network N is disclosed as being the internet or a mobile phone carrier's line, but is not limited to these; for example, it may be a dedicated line. Similarly, server 10 is disclosed as being connected to management information processing terminal 15 by network N, but for example, some or all of it may be stored within management information processing terminal 15.

[0042] Server 10 may be configured as a microservices system using a cloud environment (AWS, GCP, etc.), in which case each AI module can scale independently. Information processing terminals 20, 30, etc. may consist of smartphones, tablets, PCs, etc., and their built-in sensors (camera, GPS, accelerometer) can also be used. One of the customer's information processing terminals, the merchant terminal, may be connectable to OCR, NFC, and even XRF devices, enabling advanced material analysis to be performed on the merchant side.

[0043] The entire system can communicate with external services (banking APIs, anti-social APIs, delivery APIs) through a secure API gateway. The communication may be encrypted using TLS, and the private key may be stored in the HSM to prevent unauthorized access.

[0044] <Configuration of the information processing device> Figure 2 is a block diagram showing the functional configuration of server 10.

[0045] Each information processing device consists of a computer equipped with an arithmetic unit and memory for control and calculations. The basic hardware configuration of the computer and the basic functional configuration of the computer realized by said hardware configuration will be described later. For each of the following, explanations that overlap with the basic hardware configuration and basic functional configuration of the computer described later will be omitted. In addition, any type or model of XRF device is acceptable as long as it can output spectral data of fluorescent X-rays.

[0046] <Server 10 Configuration> Server 10 is an information processing device that acquires, stores, and calculates and provides information related to the information processing device 1 for commodity transaction security management. Server 10 comprises a storage unit 101 and a control unit 104.

[0047] <Configuration of the storage unit 101 of server 10> The storage unit 101 of the server 10 includes a data table 1010 and an application program 1011.

[0048] <Configuration of the control unit 104 of server 10> The control unit 104 of server 10 includes an XRF acquisition unit 1041, a material analysis AI unit 1042, a material authenticity determination unit 1043, a specific gravity measurement unit 1044, a magnetic inspection unit 1045, an ultrasonic analysis unit 1046, a composite material analysis unit 1047, an authenticity integration AI unit 1048, a damage value correction AI unit 1049, a risk integration AI unit 104A, a transaction restriction control unit 104B, a multiple location matching unit 104C, a location spoofing detection unit 104D, a terminal evaluation unit 104E, a delivery risk evaluation unit 104F, and an emergency stop control unit 104G. It comprises a fail-safe switching unit 104H, a similar feature search AI unit 104I, a forgery anomaly detection AI unit 104J, a delivery anomaly analysis unit 104K, an automatic insurance claim AI unit 104L, a merchant training AI unit 104M, a transaction recommendation AI unit 104N, a market health analysis unit 104O, an adaptive UI control unit 104P, a data anonymization processing unit 104Q, an AI fairness correction unit 104R, an API anomaly control unit 104S, a fraudulent template learning unit 104T, and an autonomous recovery engine 104U.

[0049] <Basic Computer Hardware Configuration> Figure 5 is a block diagram showing the basic hardware configuration of a typical computer 90. The computer 90 includes at least a processor 901, main memory 902, auxiliary memory 903, and a communication interface 991. These are electrically connected to each other by a communication bus 921.

[0050] The processor 901 is hardware for executing the instruction set written in the program. The processor 901 consists of an arithmetic unit, registers, peripheral circuits, etc. Furthermore, for GPU acceleration, it may be equipped with a GPU (Graphics Processing Unit), which is an arithmetic unit specialized for image processing.

[0051] Main memory 902 is used to temporarily store programs and data processed by programs, etc. For example, it is a volatile memory such as DRAM (Dynamic Random Access Memory).

[0052] Auxiliary storage device 903 is a storage device for saving data and programs. Examples include flash memory, HDD (Hard Disk Drive), magneto-optical disk, CD-ROM, DVD-ROM, and semiconductor memory. Alternatively, storage space may be secured in the cloud to add or replace auxiliary storage device 903.

[0053] The IF991 communication interface is an interface for inputting and outputting signals for communication with other computers via a network using wired or wireless communication standards.

[0054] A network consists of various mobile communication systems, such as the internet, LANs, and wireless base stations. For example, a network includes 3G, 4G, and 5G mobile communication systems, LTE (Long Term Evolution), and wireless networks (e.g., Wi-Fi®) that can connect to the internet via designated access points. When connecting wirelessly, communication protocols include, for example, Z-Wave®, ZigBee®, and Bluetooth®. When connecting via a wired connection, the network also includes connections made directly via USB (Universal Serial Bus) cables and other means.

[0055] Furthermore, it is possible to virtually realize a computer 90 by distributing all or part of each hardware configuration across multiple computers 90 and connecting them to each other via a network. Thus, the concept of computer 90 includes not only a computer 90 housed in a single enclosure, but also a virtualized computer system.

[0056] <Basic Functional Configuration of Computer 90> The functional configuration of the computer realized by the basic hardware configuration of computer 90 is described below. The computer comprises at least one functional unit: a control unit, a memory unit, and a communication unit.

[0057] The functional units of computer 90 can also be realized by distributing all or part of each functional unit across multiple computers 90 interconnected via a network. The concept of computer 90 includes not only a single computer 90 but also a virtualized computer system.

[0058] The control unit reads various programs stored in the auxiliary storage device 903 by the processor 901, loads them into the main memory 902, and executes processing according to the program. The control unit can realize the functions of various information processing units depending on the type of program. In this way, the computer becomes an information processing device that performs information processing.

[0059] The memory unit is implemented by the main memory 902 and the auxiliary memory 903. The memory unit stores data, various programs, and various databases. The processor 901 can also reserve memory areas corresponding to the memory unit in the main memory 902 or the auxiliary memory 903 according to the program. The control unit can also cause the processor 901 to perform operations such as adding, updating, and deleting data stored in the memory unit according to the various programs.

[0060] Memory management can be performed independently for each AI model, and unnecessary models can be processed using an on-demand loading method, contributing to a lighter overall system.

[0061] Furthermore, data encryption is possible, and to prevent decryption by future quantum computers, it can be made compatible with quantum-resistant encryption algorithms (such as CRYSTALS-Kyber).

[0062] The program adopts a cloud-first structure, prioritizing the use of programs located in the cloud. Furthermore, it employs a cloud-native structure that optimizes the applications being executed for the cloud, allowing each program's functions to run independently as microservices. This eliminates the need for service downtime when updating AI models, facilitating continuous operation. External integrations are integrated via an API gateway, enabling secure and high-speed data processing. It can also run on multiple platforms, including web browsers, iOS apps, and Android apps, maximizing the use of each device's capabilities (camera, NFC, GPS, accelerometer, etc.). The smartphone app offers high camera quality, making it suitable for acquiring high-resolution data necessary for material analysis and authenticity verification. The PC browser version provides drivers as needed for stores to connect with external devices such as XRF equipment, hydrometers, NFC readers, and ultrasonic flaw detectors. Finally, it provides a unified UI / UX across all platforms, allowing users to access equivalent functionality regardless of the device.

[0063] A database, specifically a relational database, is a system for managing and relating tabular data sets called masters, which are structurally defined by rows and columns. In a database, tables are called tables, masters are called masters, the columns of tables are called columns, and the rows of tables are called records. In a relational database, relationships can be established and linked between tables and masters.

[0064] Typically, each table and master has a primary key column to uniquely identify records, but setting a primary key column is not mandatory. The control unit can instruct the processor 901 to add, delete, or update records in specific tables and masters stored in the memory unit, according to various programs.

[0065] Furthermore, each table, database, and master in this disclosure may include any data structure (lists, dictionaries, associative arrays, objects, etc.) in which information is structurally defined. Data structures also include data that can be considered a data structure by combining data with functions, classes, methods, etc., written in any programming language.

[0066] The communication unit is implemented by the communication IF991. The communication unit provides the functionality to communicate with other computers 90 via the network. The communication unit can receive information transmitted from other computers 90 and input it to the control unit. The control unit can cause the processor 901 to perform information processing on the received information according to various programs. The communication unit can also transmit information output from the control unit to other computers 90.

[0067] This disclosure allows the use of APIs (Application Programming Interfaces). APIs are interfaces that connect software, programs, and web services. By using APIs, it is possible to link functions and data between different applications, which can lead to improved development efficiency, cost reduction, and enhanced user convenience.

[0068] This disclosure utilizes an API gateway to centrally manage communication with multiple external services, including user terminals, merchant terminals, material analysis equipment, bank APIs, anti-social APIs, delivery APIs, and insurance APIs.

[0069] API gateways are equipped with access control lists (ACLs), rate limiting, and multiple authentication to prevent excessive requests and unauthorized communication to external APIs. Furthermore, in the event of communication anomalies (such as delayed responses, 500 errors, or no response), the gateway can automatically select alternative routes, retry processes, and cache utilization.

[0070] Access to external APIs can utilize OAuth 2.0, API keys, and HTTPS (TLS encryption) as the communication protocol, and may be securely established using authentication methods such as digital certificates.

[0071] Furthermore, this disclosure allows for the use of AI models, and Explainable AI (XAI) technology can be introduced to ensure transparency regarding the judgment results of the AI ​​models. An "AI explanation function" may be provided in response to user questions, in which the AI ​​can explain (why the price was determined that way).

[0072] By adopting microservices, AI model updates, retraining, and modifications can be performed without affecting other functions, ensuring the overall stability of the service.

[0073] The AI ​​model runs in a Docker container and can automatically scale out according to the load, maintaining inference speed even under high load conditions.

[0074] Here, we will explain the operation flow of the information processing device 1 for managing the security of commodity transactions in this embodiment.

[0075] <Functions of information processing equipment for managing the security of commodity transactions> The present invention is a multi-layered integrated transaction management system for ensuring authenticity, safety, fairness, transparency, and continuity in transactions of mainly high-value goods such as precious metals, jewelry, watches, and branded goods.

[0076] In other words, a single integrated platform with safety, continuity, and transparency, comprising a material authenticity determination unit that uses material analysis results, which are estimation results from an XRF acquisition unit that acquires spectral data of fluorescent X-rays irradiated onto a specific product, for authenticity determination, A composite material analysis unit that inputs the aforementioned specific gravity value, magnetic response, or ultrasonic reflection characteristics into the material analysis AI unit to perform composite material determination; a genuineness integrated AI unit that calculates a genuineness comprehensive index indicating the authenticity of the object being traded based on integrated features; Damage Value Correction AI Unit calculates an assessed value correction value according to the type, location, depth, and extent of the damage. The Risk Integration AI Department calculates a comprehensive risk index that indicates the overall risk level of the transaction. A trading restriction control unit that automatically controls the transaction when the overall risk index exceeds a predetermined threshold. A multi-location matching unit that calculates the reliability of the customer's current location. A location spoofing detection unit calculates a location spoofing index that indicates the possibility that the location of a customer's information processing terminal is being falsified, including GPS spoofing. A terminal evaluation unit calculates a device reliability score that indicates the security and reliability of the customer's information processing terminal. The Delivery Risk Assessment Department calculates the risk index for each delivery company and the risk index for delivery routes. An emergency stop control unit that immediately halts transactions, delivery processing, or fund transfer processing if a predetermined risk threshold is exceeded. A fail-safe switching unit that maintains the continuity of transaction processing by automatically switching to at least one of the following: an alternative API, cached data, or local estimation logic. Similarity feature search AI unit that extracts candidates that match patterns of similar products, related products, or counterfeit products. The AI ​​unit for detecting counterfeit anomalies calculates a counterfeit anomaly index that indicates the likelihood of an unknown counterfeit product. A delivery anomaly analysis unit calculates a delivery anomaly index that indicates deviations, impacts, substitutions, seal breakage, or internal fraud during the delivery process. An automated insurance claim AI unit automatically extracts items requested by insurance companies, including accident cause, evidence data, time of occurrence, delivery route, or product information, and generates insurance claim data. The AI ​​training department for franchisees presents assessment procedures, points to note, or areas for improvement based on assessment quality indicators. A trading recommendation AI unit that presents trading methods, recommended merchants, or appropriate price ranges to traders based on recommendation features. The Market Health Analysis Department calculates the Market Health Index, which indicates the overall health of the market. Adaptive UI control unit that automatically adjusts display items, layout, explanatory content, and operation guidance. A data anonymization processing unit that makes anonymized data available for use in authenticity model training, risk analysis, or market statistics. An AI fairness correction unit that automatically applies at least one of the following processes: feature weight adjustment, retraining, feature exclusion, or correction. API error control unit that maintains continuous transaction processing. A fraudulent template learning unit that automatically generates fraud templates representing new forgery methods or fraudulent behavior patterns, and continuously trains each AI model with said fraud templates. Alternatively, the present invention provides an information processing device for commodity trading security management, an information processing method for commodity trading security management, and an information processing program for commodity trading security management, further comprising an autonomous recovery engine that autonomously performs at least one of the following to recover: cause determination, activation of an alternative module, configuration rearrangement, or cache restoration.

[0077] The above-mentioned functions are comprised of the following individual functions.

[0078] 1 XRF material analysis AI 2. AI for material analysis using specific gravity, magnetism, and ultrasound. 3. Authenticity / Counterfeiting Index (Fusion Model) 4. Damage Value Correction AI 5. Risk Integration AI 6. Trading Restriction Control 7 GPS × WiFi × IP Location Matching 8. Location spoofing detection 9. Device reliability score 10. Risk Index by Delivery Company 11. Kill Switch (Emergency Stop Control) 12 Failsafe Switch 13 Similar Feature Search AI 14. AI for detecting forgery and anomalies 15 Delivery abnormality analysis AI 16. AI for automated insurance claims 17 Franchisee training AI 18. Transaction Recommendation AI 19 Market Health Index 20 Adaptive UI Control 21. Data anonymization process 22 AI Fairness Correction 23. Automatic API Anomaly Switching 24. Automated learning of fraudulent templates 25. Autonomous Recovery (DR)

[0079] Each of the individual functions described above is realized by the various parts (Figure 2) of the control unit 104 located inside the server 10 of the commodity transaction security management information processing device 1. For example, the XRF acquisition unit 1041 acquires fluorescence X-ray spectral data from the information processing terminal 17, and then the material analysis AI unit 1042 generates extracted data from the acquired spectral data, including the peak position, peak intensity, peak width, and trace element distribution of each element. Based on the extracted data, the AI ​​estimates the content of precious metals, including gold, platinum, palladium, or silver, and detects hollow structures, foreign material inclusions, plating, or the use of counterfeit metals based on anomalies in the spectral patterns of these specific products. The material analysis AI unit 1042 stores the data from each stage in the data table 1010. Once all available material analysis results have been stored in the data table 1010, the material analysis AI unit 1042 terminates its operation. Finally, the material authenticity determination unit 1043 reads the data stored in the data table 1010, determines whether a transaction is possible and the transaction conditions, and stores the results in the data table 1010. The results can then be read and displayed on the management information processing terminal 15, as well as on information processing terminals 17, 20, 30, and others.

[0080] The Commodity Transaction Security Management Information Processing Device 1 implements the functions described below in this manner. Each of these functions will be described in detail below. The internal coordination of the Commodity Transaction Security Management Information Processing Device 1 described above is the same as described below, so it will not be described further.

[0081] The details of each of the aforementioned individual functions will be described in detail below.

[0082] <1 XRF Material Analysis AI> High-precision estimation of elemental composition, hollow structure, and counterfeit metal inclusion.

[0083] In general, it involves the following steps.

[0084] 1. XRF is irradiated onto precious metals, jewelry, and branded goods, and their spectra are obtained. 2. The AI ​​analyzes the peak position, intensity, width, and trace components to estimate the elemental ratio. 3. Abnormal spectral patterns were used to detect hollow structures, foreign material inclusions, plating, and counterfeit metals. 4. The material authenticity determination unit then determines the result and outputs whether the material is genuine or suspected of being counterfeit.

[0085] In short, it is a specialized engine that uses XRF (X-ray fluorescence) alone to determine the authenticity of materials, deciding whether they are genuine at the elemental level. It is a material analysis system that determines the authenticity of materials such as precious metals, jewelry, or branded goods. (1) An XRF acquisition unit that acquires spectral data of fluorescent X-rays irradiated onto the object, (2) From the spectrum, the AI ​​material analysis unit extracts the peak position, peak intensity, peak width, and trace element distribution of each element, estimates the content of Au, Pt, Pd, Ag, etc. based on this, and detects hollow structures, foreign material inclusions, plating treatments, and the use of counterfeit metals from outliers in the spectral pattern. (3) A material analysis system equipped with a material authenticity determination unit that uses the results of this material analysis AI unit to determine the authenticity of the material, Input: XRF spectrum (intensity data for each wavelength) Processing: Peak analysis + concentration estimation + anomaly pattern detection (AI) Output: Determination of material authenticity (genuine / suspected counterfeit) It is an independent material analysis system that performs this task.

[0086] Further detailed instructions are as follows.

[0087] 1.Element ratio (content rate C i ) Estimate From the XRF spectrum X(λ), the proportion of element i(Au / Pt / Pd / Ag…) is:

[0088]

number

[0089] 2. Detecting forgery from "outliers" in the spectrum In addition to estimating the content, abnormalities in the spectral pattern, such as shifts in peak position, abnormally broad peak widths, elemental peaks that shouldn't be present, and unnatural distributions of trace elements, can be used to detect hollow structures, contamination with foreign materials (e.g., tungsten), surface-only plating, and the use of counterfeit metals (brass, nickel alloys, etc.).

[0090] 3. X-ray fluorescence properties of atoms (unique to each element) Each element emits X-rays of a specific energy (wavelength) as a unique peak. This "peak position" is a physical constant and therefore does not change. In other words, Au, Pt, Pd, Ag, Cu, Ni, etc. will always have peaks in different locations in their X-ray spectra, so no matter how much you try to disguise them, you "cannot falsify the elemental composition."

[0091] The invariance, density, reflectivity, and magnetism of atomic fluorescence properties follow physical laws, so these properties will fail in the case of counterfeit materials (gold-plated, hollowed out). Therefore, even just measuring the surface with XRF, if it's plated, for example, "the surface is Au, but signals of Cu or Ni appear in the deeper layers," and when combined with auxiliary information such as the hollow structure, it can be determined that it is a "counterfeit" because "the weight, internal structure, and elemental ratios contradict each other." It is physically impossible for a gold bar to have the same weight but be hollow inside; therefore, it can be concluded that there is a hollow space or a different material inside somewhere. If you rely solely on markings and engravings to determine the material, it's difficult to spot cases where the markings themselves are forged or have been added later. Furthermore, specific gravity measurement alone can overlook hollow structures, high-density metals, and complex alloys. Additionally, using XRF only at a "meter reading" level (often used in the field with the feeling that "it looks like 18K, so it's OK") can lead to incorrect judgments.

[0092] Because XRF has been elevated from "visual numerical checks" to "AI analysis that vectorizes peak position, intensity, width, and trace components," and furthermore, because it is a material-specific authenticity engine that automatically performs purity determination (content) and abnormal pattern detection (hollows, different materials, plating, counterfeit metals), 1. Regardless of the markings, the authenticity of the material can be determined from its elemental composition itself. 2. "Removing the core from genuine metal," "mixing high-density different materials," etc. It can detect deception that might be overlooked if we rely solely on human observation. As a result, users will have a reduced risk of being deceived in transactions, merchants will have a reduced risk of being sold counterfeit goods at inflated prices, and society as a whole will benefit from a healthier precious metals and brand market, creating a market structure where it is impossible to deceive about the quality of materials. Furthermore, this invention serves as a single platform that integrates the entire invention of this disclosure, as well as a bridgehead to the inventions described later. For example, this XRF material analysis AI is connected as a material layer to judgment based on specific gravity, magnetism, and ultrasonic measurement, an integrated authenticity AI, a damage value correction AI, and so on.

[0093] The processing flow in the above-mentioned information processing terminal will be explained below with reference to Figure 4. The information processing device disclosed herein is an information processing device for managing the security of commodity transactions, which includes determining the authenticity of the materials of specific goods, including precious metals, jewelry, or branded goods that are the subject of trading. The control unit 104 of the server 10 of the information processing device 1 for secure management of commodity transactions operates the XRF acquisition unit 1041, the material analysis AI unit 1042, and the material authenticity determination unit 1043 as follows. The XRF acquisition unit acquires spectral data of the fluorescent X-rays irradiated onto the specific product (S101), The material analysis AI unit generates extracted data from the spectral data, including the peak position, peak intensity, peak width, and trace element distribution of each element (S102), Based on the extracted data, the content of precious metals including gold, platinum, palladium, or silver is estimated (S103), Based on the abnormal values ​​of the spectral pattern of the specified product, hollow structure, foreign material inclusion, plating treatment, or use of counterfeit metal is detected (S104), and these are stored in data table 1010 as material analysis results. The material authenticity determination unit reads the material analysis results, which are the estimation results from the material analysis AI unit (S105), and makes a determination of the authenticity of the specified product (S110). If the determination result is Yes, the material authenticity determination unit determines that the transaction is OK; if it is No, it determines that the transaction is to be suspended; and in other cases, including inability to determine, it offers a transaction with conditions and restrictions.

[0094] The subsequent processes follow the same pattern as described above, therefore, flowcharts and process numbers will be omitted from now on.

[0095] <2. AI for Material Analysis using Specific Gravity, Magnetism, and Ultrasound> AI for Composite Material Analysis using Specific Gravity, Magnetism, and Ultrasound The information processing device disclosed herein includes a specific gravity measuring unit that calculates a specific gravity value based on the weight and volume of an object, A magnetic inspection unit that detects the presence or absence of a magnetic field response, the presence of foreign materials based on magnetic field strength, or tungsten substitution, It comprises an ultrasonic analysis unit that detects internal cavities, layered structures, or density unevenness based on the reflected waveform of ultrasonic pulses, The invention further comprises a composite material analysis unit that inputs the aforementioned specific gravity value, magnetic response, or ultrasonic reflection characteristics into the material analysis AI unit to perform composite material determination.

[0096] In addition to XRF, the analysis of specific gravity, magnetism, and ultrasound is integrated, extending the XRF material analysis AI to include "specific gravity, magnetism, and ultrasound." This is a composite material analysis AI technology that "uncovers all materials that defy the laws of nature." By adding specific gravity + magnetism + ultrasound to XRF spectral analysis, it is upgraded from simple "elemental composition determination" to comprehensive material determination including "density, magnetism, and internal structure." While conventional methods used XRF alone, ultrasound alone, or magnetic checkers alone, this disclosure integrates the results of XRF, specific gravity, magnetism, and ultrasound inspections to calculate the degree of abnormality.

[0097] for example, 1. When measuring the surface with XRF, the "Au ratio is reasonably high," 2. Inconsistent with density calculated from weight and volume. In some cases, there is a physical interpretation behind it that "this shape should be heavier." When we combine the specific gravity, magnetism, and ultrasound of this invention, the XRF shows an Au peak, the specific gravity is clearly lighter than pure gold, and the ultrasound reflects from the internal cavity, leading to the conclusion that it is a "hollow structure + thin gold plating." In other words, it has a structure of XRF acquisition unit (input) → spectral analysis AI (processing) → material authenticity determination unit (output).

[0098] [Principles / Principles] Nuclear physics (XRF): → Each element has its own unique spectrum → The presence or absence of alloys or camouflage can be determined from the spectrum. Magnetic / Ultrasonic: → If a metal that is normally non-magnetic becomes magnetic, it indicates foreign matter contamination. → Internal cavities and sandwich structures always exhibit abnormal patterns in ultrasonic reflection.

[0099] This can be explained using mathematical formulas as follows:

[0100] 1. Specific gravity measuring section

[0101]

number

[0102] 2. Magnetic Inspection Department

[0103]

number

[0104] Gold (Au) is inherently "almost nonmagnetic." When tungsten or iron-based alloys are present, the magnetic field response (Hout) changes, and M increases. Therefore, "magnetic testing can detect the magnetic response indicating the presence of tungsten or other metals." 3. Ultrasonic analysis department

[0105]

number

[0106] 4. Overall "Composite Material Score"

[0107]

number

[0108] Traditionally, XRF was used alone (only for elemental composition). Ultrasound was used only in limited areas of the watch industry. Even when examining cavities with ultrasound, it is difficult to determine purity, making judgment difficult. Magnetics were only used as a simple checker. However, this system integrates these methods with AI and has a structure that evaluates them as a unified material authenticity index. As a result, it is possible to reduce the risk of overlooking things like hollow cores, layered structures, and tungsten inclusions.

[0109] This invention combines a "specific gravity measurement unit + magnetic inspection unit + ultrasonic analysis unit" as formal inputs to a material analysis AI unit, consolidating them into a "composite material analysis unit." This completes a sensor fusion that allows for the observation of surface composition using XRF, checking density inconsistencies with specific gravity, detecting the presence of tungsten and other materials with magnetism, and detecting internal cavities and layer structure with ultrasound. AI can comprehensively detect camouflage that is difficult to detect using XRF alone, specific gravity alone, magnetism alone, or ultrasound alone. Furthermore, it allows for the same "physical-based material assessment" at any store, regardless of the store's or appraiser's experience.

[0110] <3. Fusion Model> Generation of a Fusion Score, an integrated index of authenticity, combining image analysis AI, brand authenticity AI, and material analysis AI. The information processing device of the present invention uses marking features, scratch features, or appearance features obtained from the image analysis AI unit, Sewing characteristics, logo characteristics, or internal structure characteristics obtained from the brand authenticity AI department, or, The integrated feature quantity, which includes the elemental content, specific gravity, magnetic response, or ultrasonic reflection pattern obtained from the aforementioned material analysis AI unit, is used as input. Based on the aforementioned integrated features, a comprehensive authenticity index is calculated to indicate the genuineness of the traded item. This invention is intended to be the aforementioned information processing device for managing the security of commodity transactions, further equipped with an integrated AI unit for authenticity detection.

[0111] Here, the marking features, scratch features, or external appearance features, as well as the stitching features, logo features, or internal structure features, may be extracted and judged by image analysis AI × brand authenticity AI. In other words, authenticity determination is achieved through a tripartite structure of image analysis AI × brand authenticity AI × material analysis AI. It allows us to logically explain to users not only the markings and colors, but also the "authenticity of the material, including its contents." This AI combines image analysis AI (for markings, scratches, and appearance), brand authenticity AI (for stitching, logos, and internal structure), and material analysis AI (for elemental content, specific gravity, magnetism, and ultrasound) to create a single Fusion Authenticity Score.

[0112] 1) Marking features, scratch features, or appearance features obtained from the image analysis AI unit, 2) Sewing characteristics, logo characteristics, or internal structure characteristics obtained from the brand authenticity AI department, 3) At least three of the following, obtained from the material analysis AI unit, are used as integrated inputs: elemental content, specific gravity, magnetic response, or ultrasonic reflection pattern. Based on these integrated features, a Fusion Authenticity Score is calculated to indicate the authenticity of the traded object.

[0113] Here, (1) The Image Analysis AI Department is Image scores are obtained from engraving characteristics (depth, line width, font, placement, etc.), scratch characteristics (scratches, dents, defects, polishing marks), and appearance characteristics (color, gloss, shape balance, etc.). (2) The Brand Authenticity AI Department, The brand authenticity score is determined from factors such as stitching pitch / stitch variations, logo proportions, curves, letter spacing, and internal structure (pocket placement, movement layout, etc.). (3) As described above, the material analysis AI unit obtains a material authenticity score, By using weighted averages, Bayesian estimation models, stacking / metaclassifiers, etc., it is possible to obtain a Fusion Authenticity Score, a continuous value from 0 to 1.0, which integrates the degree of agreement to estimate "total truthfulness."

[0114] Using completely different judgment methods such as optics (markings, scratches, appearance (incident light → reflected light)), industrial statistics (stitching, logo, internal structure (factory tolerance range)), and physics / chemistry (XRF (atomic fluorescence), specific gravity (law of conservation of mass), magnetism (magnetic field response), and ultrasound (wave equation), we will see if they converge to the same answer (genuine / fake). This is because, no matter how well-made a counterfeit is, it is virtually impossible to simultaneously deceive all aspects of light reflection, industrial tolerances, atomic composition, density, magnetism, and internal structure. "Statistically, integrating multiple independent variables reduces the error, and this is the most theoretically robust structure for fake detection, as can be said from the principles of statistics." Combining scores with different properties is more effective than using AI based solely on images or solely on materials, as it brings us closer to the "true label (authenticity)." This is the "Ensemble = Fusion Model," which translates image score + brand score + material score into a comprehensive authenticity index.

[0115] The following formula can be used as a specific calculation method.

[0116] Fusion Authenticity Score Integrating three types of AI scores:

[0117]

number

[0118]

number

[0119] Normally, it's 0.33–0.33–0.33, or if you prioritize the quality of the ingredients, 0.2–0.5–0.3.

[0120] Threshold determination A≧0.9 → Genuine work 0.8≦A<0.9→ Check required A < 0.8 → Suspicion of forgery As a concrete example of calculation, consider a watch where the materials are genuine, but only the case has been replaced. If the movement inside is genuine, but the case and bracelet are aftermarket, the input score would be... Image analysis AI: Appearance is slightly different from normal → 0.6 Brand Authenticity AI: Internal structure (movement) is OK, but the exterior structure does not match → 0.7 Material Analysis AI: The 18K ratio is correct → 0.95 When combined, the overall authenticity index is approximately 0.75, which is interpreted as being in the "gray area - caution advised" range. This allows us to express in a single number a situation where the material has value, but "it cannot be said to be completely authentic as a branded product." Buyers can then limit the appraisal amount based on the material value and explain that "it is not covered by the brand guarantee because it is an aftermarket case."

[0121] If, Authenticity Index ≈ 0.5 → Strong suspicion of being a fake. Therefore, even with sophisticated forgery, Fusion can pick up inconsistencies with other elements, and overall, it can be judged as "dangerous." Even counterfeit, modified, or replaced items that are difficult to spot at first glance can be comprehensively assessed for inconsistencies from three angles: image, brand, and material. This minimizes variations between appraisers, allowing the use of the same authenticity index across all stores and countries. As it unifies the subsequent risk management, insurance, and finance metrics, it becomes a core engine supporting the "safety layer of goods" in precious metals and brand-name transactions.

[0122] Technical points include 1. The input is clearly defined as "engraving, scratches, appearance, stitching, logo, internal structure, elements, specific gravity, magnetism, and ultrasound," 2. The output is a Fusion Authenticity Score, which is a continuous score from 0 to 1. 3. While existing technologies include AI for "images only," "materials only," and "brands only," this approach integrates them into a "trinity." Such a clear model has never existed before.

[0123] Further details regarding the Image Analysis AI Department and the Brand (Authenticity) AI Department are provided below.

[0124] [Image Analysis AI Department] The image analysis AI unit (S105) extracts the shape of the engraving, scratches, and external features from the captured image. For example, the AI ​​can quantify the thickness, depth, and spacing of the engraving lines, surface scratches, dents, polishing marks, and discoloration, as well as the overall shape and balance, and evaluate the "authenticity."

[0125] For example, the marking and image features can be formulated as follows:

[0126]

number

[0127] The similarity to the brand standard data Bimg is

[0128]

number

[0129] In contrast to the conventional approach of judging authenticity based solely on the "feel" of the engraving, which is vulnerable to high-precision counterfeit engravings, and where only "coarse features" of the engraving are used as input for integrated authenticity detection AI, this new approach treats the engraving as a multidimensional physical quantity vector consisting of "line width, depth, edge, placement, and font," and measures the distance from the genuine template.

[0130]

number

[0131] By quantifying the degree of similarity and using that as powerful input for Fusion Score (an integrated AI for authenticity detection), it can quantitatively evaluate "how close it is to the real thing" based solely on the engraving, without relying on human perception. Even with high-precision copies, it can comprehensively detect slight differences in depth and line width, unnatural jagged edges, and millimeter-level misalignments.

[0132] As a specific example, even if a genuine product has a K18 stamp (line width 0.12mm, depth 0.05mm) while a counterfeit product has a thinly laser-copied K18 stamp (line width 0.09mm, depth 0.02mm), the AI ​​image analysis can extract the stamped area from the product image, estimate the depth d from the length and density of the shadow using optical feature extraction, estimate w from the line width, and then F img = Calculate (d, w, c, r, s) and brand template B img By comparing it to the genuine product, the |Fimg-Bimg|| is smaller. S img ≈ 0.98 However, the distance between counterfeit goods and the real thing increases. S img ≈ 0.6 Therefore, even a difference that might seem "a little faint" to the human eye can be clearly shown by AI as a numerical value indicating the "deviation from the real thing," allowing for more precise judgment of authenticity. As a result, the reliability of the overall authenticity judgment (Fusion Authenticity Score) is increased, leading to a more satisfactory authenticity judgment for merchants, users, and the government.

[0133] [Brand (Authenticity) AI Department] The brand (authenticity) AI department converts the "stitching, logo, hardware, interior, material texture, engraving font, and deterioration over time" of branded goods into "multidimensional numerical vectors," compares them against a standard template for each brand, and generates a brand authenticity score. It will be abbreviated as Brand AI Department or Brand AI. In other words, at least one of the brand's unique features—such as sewing patterns, logo shapes, hardware shapes, internal structures, material textures, engraved fonts, and aging patterns—is multidimensionally matched against a "brand-specific feature template" to determine the brand's authenticity characteristics (brand authenticity score).

[0134] More specifically, genuine products, thanks to factory equipment and craftsman training, fall within a "certain tolerance range," while counterfeit products have stitching patterns that tend to deviate from that range, resulting in differences in stitch pitch (seam spacing), thread angle, and stitching pattern (consistent / variable). Furthermore, regarding logo shapes, which are strictly defined as industrial design and statistically result in very similar shapes for genuine products, we assess the proportions (height and width), letter spacing (kerning), curve radius, and edge rounding of the brand logo. Regarding hardware shapes, which have little variation in shape and engraving depth because they are mass-produced on metal processing lines, we assess the outer shape, thickness, edge treatment, and engraving depth of zippers, buckles, and hardware.

[0135] Furthermore, genuine products have a fixed assembly order and design, resulting in consistent structural patterns. Regarding internal structures, this includes the interior of bags (pocket location, number, sewing order, tag location), the interior of watches (movement structure, component arrangement, bridge shape, etc.), and genuine leather and canvas, where the material and processing process are consistent, resulting in statistically similar texture distributions when viewed macroscopically. However, differences exist in material texture (texture), such as the fiber pattern on the leather surface, the size and direction of the grain, and the weave density and direction of the canvas. Moreover, as mentioned above, the embossed font and the way genuine materials and processing age are somewhat patterned, but counterfeit products have different material quality, making the gloss, color, and rate of deterioration unnatural. Regarding aging patterns, the fading and shine of leather, scratches and oxidation of metal, and subtle changes in paint that occur over time are collectively treated as a "brand-specific feature vector," and the distance and similarity to the template are calculated.

[0136] As a specific example, if the exterior stitching and logo are quite neat, but the interior pocket structure and tag placement are slightly different from the original model, the brand authenticity AI will score highly for the exterior features (nearly 0.9), but the internal structure features (number of pockets, position, stitching pattern) deviate from the template, resulting in a score of only about 0.4 for this dimension alone. Therefore, the combined score would be... Brand authenticity score ≈ 0.6 It is determined that it falls into this category. By combining this with separate material analysis AI and image analysis AI, it is also possible to make a comprehensive judgment and determine that it is "strongly suspected of being a counterfeit."

[0137] Even highly sophisticated counterfeits, such as A-grade and S-grade copies, can be detected through a "comprehensive pattern" of stitching, logos, hardware, interior, materials, and aging. This reduces "differences in expertise" between stores and appraisers, ensuring a unified brand authenticity standard across all stores. It also makes it easier to explain to insurance, financial, and government agencies "how each feature differs from the template," thus ensuring transparency and accountability in authenticity judgments.

[0138] The captured data may be automatically processed with shadow correction, brightness correction, white balance adjustment, distortion correction, noise reduction, etc., so that the AI ​​can analyze it with optimal quality. Background noise can also be removed using background removal AI, contributing to improved accuracy in mark identification and material analysis. A depth camera or LiDAR-compatible terminal may also be used to acquire three-dimensional data of surface irregularities, mark depth, and edge shape. The captured image will include the effects of lighting conditions, shadows, color temperature, and background noise, so these may be automatically corrected. Lighting condition correction includes automatic correction of underexposure and overexposure, and correction of light source color temperature. Background removal AI removes backgrounds other than the product, improving the accuracy of shape, color, and marking identification. Changes in shadow shape may be used for material estimation, and if the shadow intensity is unnatural, the system may prompt for reshooting. AI camera guidance can evaluate the shooting position, angle, and distance in real time, guiding the user to take optimal shots. The AI ​​can automatically point out shooting errors such as the subject being too small, blurry, or the engraving being cut off. Shooting assistance significantly improves the quality of input to the authenticity detection AI. Furthermore, the image analysis AI may employ a multi-view analysis method that integrates and analyzes images from multiple angles and under multiple conditions, rather than just a single image. Multi-view analysis allows for highly accurate acquisition of information that is difficult to obtain from a single image, such as the depth of the engraving, changes in shadows, material reflectivity, and distortion of fine details. The AI ​​can compare the consistency, differences, and unnatural deformations between multiple images to evaluate the possibility of forgery.

[0139] Image segmentation models (such as U-Net and Mask R-CNN) can be used for scratch detection, quantifying the location, length, depth, and shape of the scratches. These scratch parameters are reflected in the appraisal value, and the type of scratch (linear scratches, dents, impact marks, polishing marks) is also identified. Age-related scratches and fabricated scratches can also be distinguished through pattern analysis.

[0140] As a future extension, the present invention may be extended to material analysis using a hyperspectral camera. Hyperspectral data can accurately identify minute differences in material composition, traces of surface processing, and the degree of deterioration over time. This significantly improves the accuracy of detecting subtle counterfeiting, which was difficult with conventional analysis. Furthermore, AI detects image manipulation, erasure, and processing traces using Photoshop® or similar software by analyzing image pixel statistics, waveform analysis, edge mismatch, and noise distribution. If there is a high suspicion of manipulation, the submission of a video recording or real-time recording may be required.

[0141] For international transactions, the system may include features such as multilingual translation, automatic regional law detection, and export / import regulation checks. Integration with official brand APIs (Louis Vuitton, Rolex, etc.) allows for verification of model numbers, manufacturing years, and official authenticity information. For high-value items, security features such as GPS tracking of on-site appraisers, entry / exit logs, and alerts for dangerous areas may be provided. Brand-specific models may utilize Siamese Networks or Metric Learning to capture subtle differences unique to counterfeit goods.

[0142] For luxury watches, the movement of the hands, acoustic data (tick sounds), case back engravings, weight, and the conductivity of the materials are also used in the learning process. If official brand data is unavailable, the feature model can be enhanced using a large amount of image data collected from users and affiliated stores. To improve the accuracy of counterfeit detection, it is also possible to optimize different models for each brand. For highly accurate authentication of branded goods, AI can extract a variety of features, including logo characteristics, stitching patterns, engraved fonts, material textures, hardware shapes, and internal tag information. For watches, AI can also analyze the periodicity of hand movement, acoustic patterns, weight and specific gravity, case back engravings, and movement characteristics, and incorporate this information into the authentication process. Certain brands have consistent combinations and structures of internal components, and AI can learn these characteristic patterns to detect even slight discrepancies. AI can also detect counterfeit-specific features such as "overly regular stitching," "differences in logo embossing depth," "unnatural placement of model numbers," and "variations in leather material patterns."

[0143] For high-value items, additional security features such as GPS tracking of on-site appraisers, hazardous area alerts, and customer location verification can be provided. For international transactions, automated checks of import and export regulations, digital customs verification, and restrictions imposed by sanctioned countries may be applied.

[0144] This disclosure allows for authenticity determination across categories, including not only precious metals such as gold and platinum, but also bags, watches, jewelry, apparel, shoes, works of art, antiques, and electronic devices. Since different features (sewing, markings, internal structure, electrical properties, acoustic data, weight distribution, etc.) are extracted depending on the category and input into a category-specific AI model, a "Category-specific Evaluation Pipeline" can be automatically constructed according to the category.

[0145] By collaborating with international brand protection organizations (e.g., REACT, ICC-BASCAP), counterfeit brand monitoring data can be incorporated into the AI, contributing to international anti-counterfeiting efforts. The latest counterfeit characteristics of monitored brands (engraving shapes, stitching patterns, model number spoofing, internal mechanisms, etc.) can be automatically updated as AI training data. The authenticity database can be automatically expanded through transactions, with continuous learning of engraving characteristics, scratch patterns, material characteristics, brand characteristics, and counterfeit characteristics. When new counterfeits are discovered, the AI ​​can generate counterfeit characteristic templates through differential learning and immediately reflect them in future judgments. The authenticity database can be hierarchically organized by category, brand, and region to optimize search efficiency.

[0146] <4 Damage Value Correction AI> Value correction AI based on damage feature analysis The information processing device disclosed herein extracts damage features, including scratches, defects, dents, polishing marks, and wear marks, from product images. The relationship between the aforementioned damage characteristics, the material analysis results, and the overall authenticity index was analyzed. The invention is a product information processing device for managing the security of commodity transactions described above, which may further include a damage value correction AI unit that calculates an assessed value correction value according to the type, location, depth, and extent of the damage.

[0147] This is a correlation analysis between damage characteristics and material analysis results.

[0148] This "damage value correction AI" uses AI to quantify "scratches, dents, defects, polishing marks, and wear," and combines this with a material analysis AI and a comprehensive authenticity index to automatically calculate how much the value of the product will decrease. By integrating "damage characteristics + material analysis results + overall authenticity index," the system more precisely calculates how much the damage negatively impacts the "economic value" of the product.

[0149] Referring to whether it is genuine (Fusion Authenticity Score) and its value as a material (material analysis results), "Even with the same scratches, a genuine rare model and scrap gold can be different." It can even reflect the real-world "way of value being assigned," such as "the magnitude of the negative impact is completely different." The steps are as follows.

[0150] 1. Product image acquisition (damage value correction AI) →Retrieves images taken by the user or store. 2. Region extraction process → Extract the "product area," "edges," and "high-risk areas (corners, rugs, etc.)" from the image. 3. Damage analysis processing (Damage value correction AI) →Detects scratches, dents, wear, polishing marks, defects, etc. 4. Is there damage? (Assessment) →If YES, proceed to "Damage Location / Degree of Damage Determination Process". →If the answer is NO, the process will terminate. 5. Damage location / degree of damage determination process → Which part (location), how deep (depth), and how wide (range) is it? Quantify it. 6. Output of analysis results →This damage feature vector is combined with the material analysis results and the overall authenticity index to calculate the appraisal value adjustment value.

[0151] The changes in scratches are considered "age-related wear," where wear and abrasion occur according to the mechanics of friction and impact. Scratches that increase over time represent a continuous change. Damage caused by accidents or drops represents a large change in a short period of time = discontinuous. Damage analysis AI can read whether it is "natural deterioration or accidental damage" from the shape, depth, and distribution of the scratches, and reflect that difference in value adjustment.

[0152] While gold and platinum bullion can be bought by weight after melting, making visible damage less impactful on their value, rare vintage watches and designer bags are significantly affected by even minor scratches, so by combining damage characteristics, material analysis results, and an overall authenticity index, the AI ​​reproduces a "natural sense of value" where scratches carry less weight for scrap gold and more weight for rare, genuine brand items.

[0153] Traditionally, 1. The way people and stores interpret "damage" varies. There are subjective differences, such as "I don't mind this scratch" or "No, it's a big negative." 2. If you look at scratches separately from the material and authenticity, even if it's scrap gold, scratches can significantly reduce its value. Conversely, scratches can be overlooked on a rare, genuine, limited edition model. 3. There is inconsistency in valuation across insurance, finance, and resale sectors, making it difficult for insurance companies to distinguish between damage after an accident and "pre-existing damage." To improve this, we can use AI to quantify the type, location, depth, and extent of damage characteristics, and combine this with the material analysis results (metal value and material composition) and the overall authenticity index (degree of authenticity) to calculate an appraisal value adjustment. 1. Eliminate "perception differences" from store to store, 2. It is possible to make adjustments that take into account both the material value and the collector value. 3. The same logic can be applied to insurance, finance, and resale, resulting in a more effective outcome.

[0154] Specific examples will be described later.

[0155] By eliminating "perception differences" between stores and appraisers, and enabling the application of unified damage reduction rules nationwide and worldwide, the same correction logic can be used in insurance, finance, and resale. This increases the consistency between the amount of damage, collateral value, and resale price in the event of an accident, resulting in appraisals that are highly satisfactory to users, businesses, insurance companies, and financial institutions alike.

[0156] <5. Integrated Risk AI> Generation of a comprehensive risk index (Total Risk Score) integrating authenticity, anti-social activities, finance, and distribution. The assessment is based on a Total Risk Score that integrates the product (authenticity), the people involved (anti-social elements / AML = anti-money laundering), and the process (delivery). This fundamentally overhauls conventional assessment and KYC (Know Your Customer) technologies.

[0157] The present invention is an information processing device for managing the security of commodity transactions described above, which may further include a risk integration AI unit that takes three or more of the following as inputs: a comprehensive authenticity index, an anti-social activity index, an anti-money laundering score, a behavioral abnormality index, or a delivery risk index that includes at least one of the following: deviation from the delivery route, abnormal sealing tag, or weight difference, and calculates a comprehensive risk index that indicates the risk level of the entire transaction based on the inputs.

[0158] The "Risk Integration Engine" is calculated by combining the factors of "product (authenticity)", "people (anti-social elements, AML, behavior)", and "process (delivery)" into a single Total Risk Score. The Fusion Authenticity Score (A), AS-index / AML score / R-index, and delivery risk index (delivery anomalies) are ultimately combined from the perspective of "Is the transaction safe?".

[0159] The inputs are the Authenticity Index (AJI) for assessing the safety of goods, the AS-index for assessing the safety of people, the AML score, or the R-index, and the Delivery Risk / Delivery Anomaly Index for assessing the safety of processes. These are integrated in a multivariate model to calculate a Total Risk Score ranging from 0 to 1.0. It acts as the "coordinator" of the tripartite model. It can also calculate the Total Risk Score and automatically control whether to proceed with, restrict, or stop a transaction.

[0160]

number

[0161] Meaning of the formula 1 - A: Risk of counterfeit side (reciprocal of authenticity) The others are "more dangerous as they become larger" as they are. In this formula, things (A) + people (AS, AML, R) + process (D) are converted into one "total risk score".

[0162] Meaning of parameter λ For financial institutions wanting to strengthen AML → Increase λ3 For companies giving top priority to anti-social risk → Increase λ2 When combined with logistics with frequent delivery accidents → Increase λ5 → By changing λi, the "safety policy" can be numerically reflected.

[0163] Function and threshold determination Function: It can be a simple linear combination, or compressed with a sigmoid function or non-linear transformation as needed.

[0164] Threshold:

[0165]

Number

[0166] [What is the representative formula of abnormal behavior: R-index] The R-index is the formula of the principle of behavior: "Normal behavior follows a certain distribution, and abnormal behavior is outside that". It will be explained below in the typical formula: For example, for one feature quantity (number of transactions per day x):

[0167]

Mathematics

[0168] [[ID=(15)]]

Mathematics

[0169] Similarly, calculate the degree of deviation for each feature quantity such as time zone, amount, moving distance, etc., and integrate them with weights to obtain the R-index.

[0170] The parameters are μ, σ: Estimated parameters of the normal distribution (learned from the log).

[0171] Regarding the weights of each feature quantity, If you want to particularly emphasize "late-night high-amount transactions", increase the weight of that component.

[0172] As an image of the effect, 1 to 2 transactions per day, small-amount transactions during the day → Low R-index 10 consecutive transactions of 500,000 yen or more in a short period, only at night → High R-index → Reflected in Total Risk → Restricted and monitored.

[0173] Specific example The output is R total is output as a comprehensive risk value from 0 to 1.0, and a threshold is set (e.g., 0.6 or more), and it is possible to judge like normal transactions, restricted transactions, and complete stops.

[0174] Unlike the conventional approach of only evaluating the item itself (for example, "it's OK because an appraiser confirmed it's genuine," without considering whether the seller is involved with organized crime or if there was any fraud during shipping), or relying solely on KYC (identity verification) or AML (even if identity verification or AML is performed, it's not linked to information about authenticity or delivery), or lacking a concept of total risk (items / people / processes are operated separately, and there's no mechanism to numerically measure the "risk level of the entire transaction"), this system combines the safety of the item (A), the safety of people (AS / AML / R), and the safety of the process (D) into a single Total Risk Score, allowing for the mechanical execution of the decision to "stop the transaction even if the item is genuine if the people or process are risky." This changes the traditional situation of "I'll buy it if it's genuine" to a structure where purchases are only made after "total safety" is ensured.

[0175] Specific examples will be described later.

[0176] In this way, disparate risks such as counterfeiting, anti-social activities, AML, and delivery accidents can be visualized as a single numerical value, shifting the structure from "I'll buy it because it's genuine" to "I'll buy it because the total safety is above a certain level." This eliminates differences between stores and individual staff, allowing transactions to be stopped or allowed based on the same criteria everywhere. In other words, it integrates authenticity (A), anti-social activities (AS), AML, behavioral abnormalities (R), and delivery abnormalities (D) into a trinity of "product x person x process," realizing a "comprehensive risk meter" for determining the GO / STOP of a transaction. With this Total Risk Score, a transaction infrastructure can function that handles complex combinations of risks such as counterfeiting, anti-social activities, fraud, and delivery accidents intuitively and rigorously.

[0177] The formulas for calculating the Fusion Authenticity Score (A), AS-index / AML score, and delivery anomaly index will be explained later.

[0178] [Dual identity verification via bank API (Financial KYC)] It double-checks the "personal information already confirmed by the bank side" and the "personal information obtained by the company itself", and calculates the degree of consistency as the Financial KYC score (KYC: Know Your Customer / personal verification). For example, the financial income and expenditure management department can do this using the bank API.

[0179] It is a unique configuration that links with the bank's KYC / anti-money laundering (AML) infrastructure and API to produce a "dual KYC score". By double-verifying independent information sources, it can reduce the probability of misidentification (probability theory), detect outliers in human behavior (R-index), and integrate with the anti-money laundering (AML) score, statistically explaining and suppressing financial fraud. From the bank API, it obtains account name information (name in kana and kanji), address, date of birth, transaction status (normal / suspended / attention required, etc.), usage restriction information (frozen / limited account, etc.), and compares it with the name, address, date of birth, personal verification document information, etc. obtained in the personal verification process to output "how much they match" as a dual KYC score (a continuous score such as from 0 to 1.0 or from 0 to 100). If the match rate is low, additional personal verification and transaction restrictions are automatically imposed. Account status (frozen / transaction restriction), internal suspicious transaction records, and anti-money laundering (AML) warnings may also be obtained.

[0180] More specifically, after normalizing full-width and half-width, old fonts, space differences, etc. for the name, calculate the degree of consistency using the Levenshtein distance (edit distance) or Jaro-Winkler, etc. After normalizing the fluctuations in the notation of blocks and house numbers for the address, consider the consistency at the prefecture / city level + the similarity of the detailed address, and for the date of birth, absorb the differences in date formats and determine whether it is a perfect match to calculate the above score. In cases such as "account frozen", "anti-social suspicion flag", "with anti-money laundering (AML) warning", etc., add a negative weight to the score. For example, a score of 0.9 or above is highly reliable, 0.6 to 0.9: attention required (additional confirmation required), and less than 0.6 is dangerous (transaction restriction / suspension). Furthermore, by monitoring account balances, deposit / withdrawal history, transaction suspension flags, and fraud warning information retrieved from bank APIs, it's possible to automatically control the deposit / withdrawal process itself, including whether to allow the transaction and how to do so. In this case, it's an invention that defines a "financial deposit / withdrawal OS." It controls whether it's safe to actually move money with this person and in what form that would be secure.

[0181] The "Financial Deposit and Withdrawal Management Department," which takes account balances, deposit and withdrawal history, transaction suspension flags, and fraud warning information as inputs, automatically executes deposit / withdrawal processing for transactions such as buybacks, pawning, and sales based on account balances, deposit and withdrawal history, transaction suspension flags, and fraud warning information (anti-money laundering (AML) warnings, suspected fraudulent use, etc.) entered from the bank API. It also controls whether to allow transactions and how to allow them (over-the-counter only / bank only, etc.) depending on the bank's restriction information (frozen or restricted accounts).

[0182] If an account is frozen or restricted, the transfer method can be changed or canceled accordingly. High-value transactions can be prohibited if the account status has a "transaction suspended" or "restricted" flag. Withdrawal methods other than banking can be blocked. The entire account can be put on hold or frozen. There is a normal mode for normal cases, a restricted mode for warnings, and a Kill Switch (transaction suspension) linked for frozen or high-level warnings. This system achieves both security and convenience by automating "deposits and withdrawals for legitimate users" while preventing transfers to suspicious accounts in the first place. Furthermore, it effectively prevents attempts to cash out using other people's names or fraudulent accounts, and the platform avoids channeling money to "accounts that have already been deemed risky by banks."

[0183] The Fusion Authenticity Score can be more complex, potentially employing metaclassifiers (random forests, lightweight neural networks) and Bayesian inference (integrating each piece of evidence as a conditional probability).

[0184] <6. Risk Limitation Control> Automated trading limit control based on the overall risk index If the information processing device of this disclosure exceeds a predetermined threshold, (1) Prohibition of postal transactions, (2) Limitation to over-the-counter transactions, (3) Additional identity verification request, (4) Transaction suspension linked to banks, (5) The invention may further include a transaction restriction control unit that automatically controls at least one of the following: sealing reinforcement, dual weight measurement, or the application of a delivery reinforcement mode including surveillance camera recording. AI automatically controls trading restrictions.

[0185] Based on the aforementioned "Total Risk Score," this is an AI-powered engine that automatically switches between specific transaction restrictions such as prohibiting mail delivery, limiting transactions to in-store only, requiring additional KYC (Know Your Customer) verification, bank hold, and enhanced delivery mode.

[0186] When the Total Risk Score exceeds a predetermined threshold, (1) Prohibition of postal transactions, (2) Limitation to over-the-counter transactions, (3) Additional identity verification request, (4) Transaction suspension linked to banks, (5) Automatically control the application of at least one of the enhanced delivery modes.

[0187] When the Total Risk Score is calculated from the aforementioned Authenticity Index A, AS-index, AML score, R-index, and Delivery Risk Index D, this Total Risk Score is monitored, and if it exceeds a threshold, the trading mode is automatically switched to the "safe side." In other words, after measuring how risky something is, the system decides "how to behave" according to that risk, thus establishing a Control relationship between Scoring.

[0188] The details of the five controls are as follows:

[0189] (1) The ban on postal transactions completely eliminates the possibility of transactions via "postal mail" for high-risk users and high-risk transactions. By only allowing in-store and face-to-face channels, the risks of product substitution, impersonation, and anonymity are reduced. (2) Limiting transactions to in-store transactions means not allowing online, mail, or agent-based transactions, and only permitting transactions at physical stores where identity verification is easy. This allows for controls such as switching high-value transactions or transactions with high-risk countries to "face-to-face only". (3) Additional identity verification requests will be made for those in the "gray zone" that slightly exceed the threshold. Instead of immediately suspending service, it's possible to require additional strong KYC (Know Your Customer) procedures, such as submitting My Number / resident registration / additional documents, or verifying identity via video call. (4) Bank-linked transaction suspension can temporarily stop suspicious fund movements by linking with the financial institution's account status and AML warnings, suspending transfers and withdrawals, and delaying the completion of deposit and withdrawal processing. (5) The enhanced delivery mode applies by making sealing, weight measurement, and surveillance cameras stricter than usual. For example, it involves doubling the sealing tags, double-measuring the weight before dispatch and after arrival, and always recording the handling of packages with cameras. By applying this mode when D is moderate (higher delivery risk), it can increase the deterrent against substitution, theft, and accidents.

[0190] Traditionally, 1. A "putting it on hold" based on intuition, meaning that appraisers or store staff make subjective judgments such as "it seems a little suspicious, so let's pass this time," which varies from store to store. 2. Authenticity verification and KYC (Know Your Customer) / AML are disconnected; that is, the authenticity 담당 (person in charge of authenticity verification) says "it's genuine, so it's OK," while the AML 담당 (person in charge of AML) says "it's financially risky," but no one has a method for deciding "what to do overall." 3. The rules are implemented solely through text and are not linked to AI; in other words, the company manual simply states, "If something seems suspicious, the store manager should decide to stop it." By using the aforementioned Total Risk Score as a trigger, and linking it to a specific set of actions such as prohibiting mail delivery, limiting sales to in-store only, requiring additional KYC (identity verification), holding bank accounts, and strengthening delivery, 1. The criteria for judgment are quantified, eliminating differences between stores and staff members. 2. Consistent safety controls are possible, taking into account risks related to materials, people, and processes. 3. Explanations to internal control and supervisory bodies can be logically explained as "this limit applies above this score."

[0191] Specific examples will be described later.

[0192] Risk level determination (threshold comparison) example R total This is compared with multiple thresholds T1, T2, and T3. "usually" "Note" "Caution" "Dangerous (Kill Switch candidate)" Determine which zone you are in.

[0193] for example: R total < 0.3 → normal 0.3 ≤ R total <0.6 → Caution 0.6 ≤ R total <0.8 → Caution 0.8 ≤ R total → Danger (Kill Switch) Normal mode (low risk) Transactions will proceed without special restrictions using pre-defined standard flows, including mail-in, in-store, and on-site purchase services. However, AI systems for checking seals, weight differences, and authenticity will function as usual.

[0194] Caution mode (medium risk) For example, the following restrictions are automatically applied: 1. Additional identity verification requirements (enhanced eKYC) Resubmission of identity verification documents Re-implementation of the Lifeness Check Banks strengthen KYC verification 2. Lowering of transaction limits Divide high-value transactions into smaller parts. Suppressing large, one-off orders While it may require slightly more effort from the user, this mode offers enhanced security.

[0195] Alert mode (high risk) Even stricter restrictions are automatically applied here: 1. Prohibition of postal transactions In-store only On-site purchases and transactions through agents are also prohibited / restricted. 2. Suspension of bank deposits and withdrawals Stop instant transfers Hold for a set period + manual review Blocking large money transfers 3. Enhanced Delivery Mode Double sealing tag Double weighing before shipping and after arrival. Camera recording is mandatory during handling. In some cases, the delivery person's identity may be verified. Users will be informed in advance through the terms of service and on-screen displays that "there are restrictions based on the level of risk involved."

[0196] Danger Mode (Kill Switch Candidate) R total If it exceeds the upper threshold T3, Five particularly dangerous indicators: 1. AS-index 2. AML score 3. Delivery anomaly index 4. Location spoofing index 5. Device Trust Score If three or more of these factors exceed the danger threshold, Kill Switch is activated.

[0197] Processing when Kill Switch is activated: Immediate suspension of transactions (buying, selling, auctions, pawning, etc.) Cancel / Hold delivery processing Cancellation / Pending Bank Transfer Instructions Log preservation and notification to government / police / financial institutions The Total Risk Score allows for the "automatic switching" of control logic for mail, in-store, KYC (identity verification), bank, and delivery modes. This enables the incorporation of specific business rules into the AI ​​control unit to create a secure transaction flow, resulting in a system that not only "displays a score" but also changes the transaction flow itself based on the score.

[0198] <7 GPS x WiFi x IP Location Matching> Multi-purpose location matching AI using GPS, WiFi, and IP information The information processing device disclosed herein acquires at least two of the following from a customer's information processing terminal: GPS positioning information, surrounding WiFi access point information, or IP address-derived location estimation information. By analyzing the degree of consistency of these location data, temporal consistency, and the rationality of the movement trajectory, The invention is that the aforementioned information processing device for managing the security of commodity transactions may further include a multi-location verification unit that calculates the reliability of the customer's current location.

[0199] It integrates GPS, WiFi, and IP to convert location information into a reliability score. This is a multi-location matching AI (Location Trust Score engine) that scores "how reliable the current location is" based on "three types of location information": GPS, WiFi, and IP.

[0200] The system takes GPS location (latitude and longitude), surrounding WiFi AP information (SSID, BSSID, signal strength), and location estimation information derived from IP addresses as input, and analyzes the degree of accuracy of the location information, temporal consistency (whether the flow of time and change in location are natural), and the rationality of the movement trajectory (whether it is physically possible for a person or delivered object). It then outputs a Location Trust Score that indicates "how reliable the current location of the device is."

[0201] The specific input details are as follows:

[0202] 1. GPS positioning information →Latitude and longitude based on signals from satellites. This is a basic concept, but errors, occlusion, and spoofing (falsification) can occur inside buildings or in urban areas. 2. Information on nearby Wi-Fi access points →By comparing the MAC address (BSSID) and RSSI (signal strength indicator) of nearby visible WiFi APs with a database of known AP locations, it is estimated that "if this group of WiFi is visible, then the location is approximately here." 3. IP address origin location estimation information →The IP address of mobile and fixed-line connections is used to estimate the location from the GeoIP database. Typically, GPS, WiFi, and IP are used, but in some cases, GPS + WiFi, GPS + IP, etc., may also be acceptable. The specific analysis points are as follows:

[0203] 1. Degree of location information matching → We quantify whether GPS coordinates, WiFi estimated location, and IP estimated location are close at the same city / town level, or whether they are off by hundreds of kilometers or even across countries, and create a "matching score." 2. Time consistency →Based on the most recent location log and time, we check: "Is the distance traveled possible from one minute ago until now?" and "Are there any sudden changes like teleportation?" For example, if someone travels from Tokyo to Osaka in 10 seconds, it's considered unnatural; if they travel several hundred meters in 30 minutes, it's considered natural. If a Japanese mobile IP address changes to an IP address of an overseas data center within seconds, it suggests that the user is not traveling normally and may be using a VPN, proxy, or location spoofing tool. 3. Rationality of the movement trajectory →Compare the data with map information (roads, railways, sea, etc.) to determine if the person is walking on the sea, moving unusually fast in mountainous areas, and whether the movement is "plausible" for a person or delivery vehicle. By combining these, we can determine the confidence level (from 0 to 1) of the user's current location.

[0204] GPS uses the time it takes for radio waves to arrive from satellites, WiFi uses signal strength and location, and IP uses network topology, so they are based on engineering laws such as radio wave propagation, geometry, and network structure. The system scores whether these three elements "naturally coincide" or "unnaturally contradictory."

[0205] Traditionally, 1. It relies solely on GPS, and smartphones and location-based services tend to trust "GPS only." GPS spoofing apps can easily falsify location. 2. IP-based location data is coarse; IP geolocation often only provides rough information at the prefectural or national level, making it inaccurate for tasks such as "selecting the nearest store" or "assessing delivery risk." 3. The tendency is to overlook inconsistencies in location information, and to make a judgment based on only one of the GPS, WiFi, and IP addresses, even if they point to different routes. By comparing at least two types of data (GPS, WiFi, and IP), and analyzing the degree of agreement, temporal consistency, and rationality of the movement trajectory, a confidence score for the current location is calculated. 1. Not relying solely on GPS, 2. VPN and WiFi spoofing are also easily detected. 3. This has the effect of improving the accuracy of subsequent R-index, delivery anomaly, Kill Switch, and nearby merchant notifications.

[0206] Specific examples will be described later.

[0207] Furthermore, when notifying customers of nearby affiliated stores with "safe affiliated stores in this vicinity," it becomes possible to control the system by not issuing notifications or prompting additional verification at the store if the reliability of the multi-location matching unit does not meet a certain standard. Additionally, the reliability of the multi-location matching unit can be useful when calculating the R-index (behavioral abnormality index) by incorporating the "temporal consistency" and "rationality of the trajectory" of the location log as part of the behavior, and when examining the relationship between the delivery tracking location and the customer's information processing terminal location in the delivery abnormality (analysis) AI and delivery risk assessment.

[0208] <8 Location Spoofing Detection> AI for detecting location spoofing (GPS spoofing) using terminal sensors and behavior patterns The information processing device disclosed herein uses customer information processing terminal behavior data obtained from at least one of an acceleration sensor, a gyroscope, a barometric pressure sensor, or a tilt sensor, By comparing changes in location information, movement speed, and radio wave intensity, The invention further includes a location spoofing detection unit that calculates a location spoofing index indicating the possibility that the location of the customer's information processing terminal has been falsified, including GPS spoofing.

[0209] This is an AI for detecting GPS spoofing, which combines terminal sensors, location information, and radio wave conditions. As mentioned earlier, instead of relying solely on GPS, WiFi, and IP, this engine uses AI to compare the relationship between the device's "accelerometer, gyroscope, barometric pressure, and tilt" with its location, speed, and signal strength, and then generates a "location spoofing index" indicating whether the location information is suspicious (suspected GPS spoofing).

[0210] "Terminal behavior data" from at least one of the following sensors—accelerometer, gyroscope, barometer, and tilt sensor—is input. The system then analyzes changes in location information (GPS / WiFi / IP location movement), movement speed (location change ÷ time), and signal strength changes (WiFi / mobile RSSI, etc.) to calculate a Spoofing Index, which indicates the degree of possibility of location spoofing (GPS spoofing) on ​​a scale from 0 to 1.0.

[0211] The overall processing flow is as follows: 1. GPS location acquisition 2. Acquisition of terminal sensor data (accelerometer, gyroscope, tilt) 3. WiFi location acquisition 4. Get IP location 5. AI detects inconsistencies between "location information" and "sensor behavior". 6. Is there a possibility of location spoofing? If the answer is YES, a Spoofing Index is generated; otherwise, the process terminates. In other words, location reliability is not determined solely by the "consistency of GPS, WiFi, and IP," but rather by comparing it with "the movement of the device itself."

[0212] The information emitted by the terminal sensors includes the accelerometer, which shows nearly zero when stationary on a desk, a regular periodic pattern when walking, and a different periodic / vibration pattern when in a car or train. The gyroscope / tilt sensor can determine the orientation and rotation of the terminal (e.g., whether it's in a pocket or held in the hand). The barometric pressure sensor can detect changes in height (floor number) and enable elevator / stair movement. The location spoofing detection unit can detect unusual conditions such as "the sensor is moving, but the GPS location is fixed," or conversely, "the GPS is showing high-speed movement, but the sensor is almost stationary," "the acceleration and gyroscope patterns do not match the movement of hundreds of kilometers in a short time," or "the GPS jumps significantly while the WiFi signal strength and type do not change," thereby increasing the location spoofing index.

[0213] Since an object's position can be said to be the result of integrating the acceleration with respect to time (velocity) and then integrating that again, if the device is truly moving, corresponding changes should appear in the accelerometer and gyroscope. However, if "the acceleration is almost zero, but the GPS warps 10km ahead," this behavior contradicts the equations of motion and is likely to be suspected of spoofing. Furthermore, while Wi-Fi signal strength and the type of access point visible naturally change with location, if the Wi-Fi information remains completely unchanged while only the GPS location is being transmitted, it's highly likely that only the location information is being rewritten by the app.

[0214] Since it is physically impossible for a person or smartphone to travel hundreds of kilometers in a few seconds, multiple location matching can roughly match the location information, and by multiplying this with the terminal's acceleration log and time, it is possible to evaluate whether the movement is physically plausible. When the "physical sensations" reported by the device's sensors and the "movements on the map" indicated by GPS, WiFi, and IP do not match according to natural laws, the AI ​​determines that "location spoofing" has occurred. This can be quantified as the Spoofing Index.

[0215] Traditionally, 1. With GPS-only checks, typical apps only look at GPS coordinates, making it easy to "make it appear as if you are overseas" using GPS spoofing apps. 2. It simply looks at WiFi / IP and GPS separately, and even if there is a slight discrepancy, it is dismissed with a "well, that's just how it is." 3. While terminal sensors are not used for "location authenticity," and sensors such as accelerometers, gyroscopes, and barometers are used for AR, pedometers, etc., there was almost no structure used for detecting GPS spoofing. The innovative aspect of this system is that it simultaneously analyzes multidimensional data from GPS, WiFi, IP, and terminal sensors (accelerometer, gyroscope, tilt, and barometric pressure) to determine whether movement is natural or if it's an unnatural jump in position. The results are then used as a location spoofing index, which can be input into the R-index (behavioral abnormality), device confidence score, and Kill Switch.

[0216] Specific examples will be described later.

[0217] As described above, the core of the technology lies in modeling behavior that contradicts the following as "location spoofing": the Spoofing Index, the relationship between acceleration, velocity, and position, the range of radio waves, and the relationship between geographical distance and time.

[0218] In other words, as an "AI that can detect location spoofing physically and statistically," it can significantly improve the security of the human (behavior) and process (delivery) layers.

[0219] <9 Device Trust Score> Device Trust Score generation AI that evaluates the authenticity of a device. The information processing device disclosed herein, based on at least one of the following: the OS version of the customer's information processing terminal, the security patch application status, the rooted or jailbroken status, the installed application status, the history of past fraudulent behavior, or the location spoofing index, The invention is that the aforementioned information processing device for managing the security of commodity transactions may further include a terminal evaluation unit that calculates a device reliability score indicating the security and reliability of the customer's information processing terminal. The security of a device is indexed as a Device Trust Score.

[0220] We provide a Device Trust Score AI that takes into account the OS, security status, modification status (root / jailbreak), suspicious apps, past fraud history, and location spoofing index, and assigns a score from 0 to 1 indicating "how trustworthy this device is."

[0221] 1. OS version, 2. Security patch application status, 3. Rooted / jailbroken state 4. Status of installed applications (malware, malicious apps, etc.) 5. History of past fraudulent activity, 6. Enter the location spoofing index, Outputs the Device Trust Score, which indicates the security and reliability of the device.

[0222] Device security is assessed based on device ID, SIM number, device certificate, OS version, and root / jailbreak status. High-risk devices (those with heavy VPN use, suspicious IP addresses, or signs of location spoofing) will have their transactions restricted. A Device Trust Score is generated for each device and integrated into the R-index and AS-index.

[0223] Specifically, the following decisions will be made.

[0224] 1. OS version and security patches →Devices with older operating systems and those that haven't received security patches for a long time are at high risk of malware infection and vulnerability exploitation. Therefore, the Device Trust Score evaluates devices with the latest or most recent stable OS versions and the latest patches as high-rated, and older OS versions with no patches applied as low-rated. 2. Root / Jailbreak status →Rooted / jailbroken devices are more susceptible to tampering with app behavior, data, and location information using system privileges. Therefore, including "Rooted / Jailbroken Status" in the input will significantly lower the score if Root / Jailbreak=YES. 3. Installed App Status / Malware →The system lists known malicious apps and malware, detects apps requesting suspicious permissions, and increases the device risk level depending on the number and type of apps. 4. History of past fraudulent activity →If the device has been used in the past for location spoofing, multiple accounts, or fraudulent transactions, this will be considered a factor that reduces the device's reliability. 5. Location spoofing index →The aforementioned "GPS spoofing index" is used as input, and devices with a tendency to spoof their location will have their Device Trust Score lowered. In other words, the more a device deviates from "natural OS security design + natural sensor behavior," the more dangerous it is, and the Device Trust Score aims to bring that device down to a score of 0 or 1.

[0225] Traditionally, 1. They only trusted the user ID and account, meaning that even if impersonation or multiple accounts were being used from the "same dangerous device," they only looked at each account separately. 2. The security status of the device is not used in risk assessment; in other words, even if rooting or jailbreaking is detected, it often ends with just a "warning." 3. While the Anti-Social / AML / R-index does not incorporate device risks, it aggregates the device's OS, patches, modification status, malicious apps, location spoofing, and fraud history into a single Device Trust Score. This score is then input into the R-index (behavioral abnormalities), AS-index (anti-social suspicion index), and Kill Switch, creating a structure that enables risk control at the device level.

[0226] Specific examples will be described later.

[0227] In terms of technical features, it treats the OS / patches / modification status / apps / location spoofing index / past fraud history as a "device-specific integrated trust score," and in relation to the overall structure, it has significance in naturally adding a "device risk level" layer to AS-index, R-index, and Kill Switch. While "device risk" is becoming increasingly important in the world of KYC (Know Your Customer) and AML, what is unprecedented is that it is integrated as an indicator on par with anti-social elements, abnormal behavior, and abnormal delivery.

[0228] Using the service from a secure device is smooth and free from unnecessary restrictions. However, using a risky device (rooted, location spoofing, malicious apps) will result in a low Device Trust Score, automatically triggering controls such as prohibiting high-value transactions, prohibiting postal transactions, requiring additional KYC (identity verification), and in some cases, immediate suspension (Kill Switch). As a result, an infrastructure is created that guarantees the security of precious metals and brand-name goods transactions by considering not only "who is using it" but also "what kind of device is being used."

[0229] Furthermore, the R-index may be calculated using a hybrid model that combines Isolation Forest, LOF, LSTM time series models, etc.

[0230] <10 Risk Index by Delivery Company> AI-powered risk assessment by delivery company and delivery route The information processing device of this disclosure refers to at least one of the following for each delivery company: past accident rate, loss rate, delay rate, damage rate, or risk index of the delivery route. The invention relates to the aforementioned information processing device for managing the security of commodity transactions, which may further include a delivery risk assessment unit that calculates a risk index for each delivery company and a risk index for delivery routes.

[0231] This is an AI-powered delivery risk assessment tool that combines delivery company-specific risks, route risks, and past accident data. This engine uses AI to score "which delivery company to use / which route to take" based on past accident rates, loss rates, delay rates, and damage rates (= historical data for each company), as well as risk indices such as the safety of the route and accident-prone locations (delivery company risk index + delivery route risk index).

[0232] The "Risk Index by Delivery Company" and the "Risk Index by Delivery Route" are calculated by referring to the past accident rate, loss rate, delay rate, damage rate for each delivery company, and the risk index of the delivery route (such as public safety, accident-prone areas, weather, and theft-prone areas). The decision is made based on the "statistical frequency law (accident rate, loss rate, delay rate)."

[0233] A risk indicator, expressed as frequency (probability), can be created from the number of past losses, damages, delays, and accidents experienced by each delivery company divided by the total number of deliveries. Since it can be assumed that "accidents will continue to occur with a similar frequency" according to the assumptions of statistical models such as the Poisson distribution and binomial distribution, future risk can also be estimated from past data. Areas with a high incidence of traffic accidents, thefts, mountainous regions, and areas prone to severe weather tend to have a higher likelihood of accidents and losses due to geographical, public safety, and weather conditions. A "route risk index" is defined based on accident history, crime statistics (car break-ins, robberies), and road types (highways, local roads, narrow roads), and this index is combined with company-specific risks and supplied to the delivery risk assessment AI.

[0234] Integrating three perspectives. The AI ​​for delivery risk assessment is, 1. Delivery company risks (accident rate, loss rate, damage rate), 2. Route risks (security, stops, detours), 3. (+IoT sensors: shock, temperature, humidity) We comprehensively analyze these factors and score the "delivery risk if this company is entrusted with this route."

[0235] Traditionally, 1. Choosing a delivery company based solely on "price and speed," that is, selecting based only on factors like cheapness / fastness, without considering the high number of past losses, damages, and delays, 2. The level of risk for each route is not quantified; for example, the risk of routes through major city centers versus areas with high crime rates, or highways passing through accident-prone areas, are not managed using an index. 3. Separation from delivery anomaly (analysis) AI (GPS, seal, weight), that is, while it could detect "anomalies that occurred along the way," it did not reflect the risk in the initial company / route selection. By establishing a "Delivery Risk Assessment Department" that uses AI to score the route risk index based on past accident rates for each delivery company, 1. Before departure, the system automatically selects a "safety-oriented company and route." 2. This score will also be reflected in the Total Risk Score and trading restriction controls. For high-risk companies and high-risk routes, it becomes possible to implement measures such as enhanced delivery mode, mandatory insurance, or even outright refusal of acceptance. This enables AI-driven risk management that is highly relevant to the field, such as "selecting routes where accidents are less likely to occur in advance."

[0236] Specific examples will be described later.

[0237] Beyond just being cheap and fast, the AI ​​can automatically select "safe companies and safe routes," and even when selecting high-risk companies and routes, risk mitigation measures such as enhanced insurance, double sealing, double weight measurement, and mandatory IoT sensors can be automatically set. As a result, delivery risks such as loss, damage, delay, and theft are statistically reduced, and "delivery anxiety" among users, merchants, insurance companies, and the platform is greatly reduced.

[0238] <11 Kill Switch> A Kill Switch (emergency stop control) that activates in the event of a combination of anomalies including anti-social activities, AML (Anti-Monetary Security), delivery errors, and location spoofing. The present invention is an information processing device for managing the security of commodity transactions, further comprising an emergency stop control unit that immediately suspends a transaction, delivery process, or fund transfer process if three or more of the following exceed a predetermined risk threshold: an anti-social activity index, an anti-money laundering score, a delivery anomaly index, a location falsification index, or a device reliability score.

[0239] This Kill Switch was triggered by a combination of conditions: AS-index, AML, delivery anomaly, location spoofing, and device reliability. Kill Switch is an emergency stop device that instantly halts transactions, deliveries, and fund transfers the moment three or more of the "five most dangerous" indicators—anti-social elements, AML, delivery anomalies, location spoofing, and terminal risk—reach a dangerous level. It's not a "safety brake that switches to a 'restricted mode' depending on the overall risk," but rather an "emergency stop button."

[0240] The five metrics used for input are, namely, 1. AS-index (anti-social suspicion index), 2. AML score (Money Laundering and Financial Anomaly Score) 3. Delivery abnormality index (calculated from seal, weight, route, and impact), 4. Location spoofing index (degree of GPS spoofing), 5. Device Confidence Score (Device risk level; lower score indicates higher risk) If at least three of the following conditions exceed their respective set "risk thresholds" (i.e., a very dangerous situation), transaction processing (buying, selling, auctions, etc.), delivery processing (shipping, receiving, redelivery), and fund transfer processing (bank transfers, withdrawals, refunds, etc.) will be immediately stopped. The "Emergency Stop Control Unit (Kill Switch)" will activate. This is a complete shutdown (all transactions, deliveries, and fund transfers will stop). This applies the Fail-Safe principle of safety engineering to the world of finance and logistics.

[0241] To give an example of an actual judgment, each score ranges from 0 to 1.0, but if three or more of the following five scores are evaluated as "dangerous"—AS-index > 0.8, AML > 0.8, delivery anomaly > 0.7, location spoofing index > 0.7, and Device Trust Score < 0.3 (the lower the score, the more dangerous it is, so we look at it as 1 - Score)—the Kill Switch flag is turned ON, the transaction control unit rolls back the transaction, cancels the execution of the transfer / withdrawal, stops or suspends the delivery instruction, leaves a log / notification, and sends it for human review.

[0242] The five scores that Kill Switch monitors all quantify the degree of "deviation," with the AS-index / AML score indicating statistical outliers in behavior and financial movement (R-index, AML), and consistency with anti-social databases, PEPs, and FATFs; the delivery anomaly index indicating abnormalities in weight differences, routes, distances, and times, as well as impacts; the location spoofing index indicating the relationship between acceleration, velocity, and location, as well as the range of radio waves (WiFi) and the temporal continuity of location information; the Device Trust Score indicating system security anomalies based on OS security patches and modification status, and the accumulation of unnatural behavior such as location spoofing history.

[0243] In safety engineering, when the level of risk exceeds a certain threshold, stopping the system itself is considered safe (Fail-Safe). When deviations are detected at multiple layers—anti-social elements, AML, delivery, location, and terminals—the Kill Switch is determined to be "too dangerous to proceed any further" and is always switched to the "zero-risk side" (not doing the transaction).

[0244] Traditionally, 1. The "If it's genuine, we'll buy it" problem: In other words, when an appraiser or system determines something is "genuine," the transaction proceeds while ignoring the risks to people and processes. 2. Overreacting to or missing a single indicator—that is, focusing only on AML, delivery anomalies, or anti-social activities—increases the number of false positives (overreaction) or false negatives (missed cases). 3. The "method of stopping" relies on on-site discretion, meaning there is no consistent "emergency stop rule" as a system, leading to inconsistencies such as employee A stopping the train while employee B lets it through, and making it susceptible to human biases such as "I'll let it through because I'll get yelled at if I stop it." By triggering the Kill Switch based on a "combined condition" where "three or more" of the five risk scores exceed the threshold, it becomes possible to: 1. be less susceptible to false positives from a single score (reducing false stops), and 2. reliably stop truly dangerous patterns (multivariate high risk). Since trades that meet the Kill Switch conditions are stopped immediately without human discretion, it eliminates inconsistencies in judgment caused by "leniency" or "pressure" on the ground.

[0245] Specific examples will be described later.

[0246] If the seller is mostly clean, but the process (delivery) + location spoofing + device malfunction creates three or more risks, Kill Switch will stop the delivery in question. However, the customer will only see a "Under Investigation" message (no assessment or payment will be made), allowing you to pinpoint and stop only the fraud within the delivery process without raising unnecessary suspicions in the customer. It strikes a balance between security and practical usability. As a result, it can function as the final line of defense, reliably eliminating only "truly dangerous transactions" that involve multiple factors such as counterfeiting, anti-social activities, AML, internal delivery fraud, and cyberattacks.

[0247] In other words, it achieves a safety engineering-based emergency shutdown that "stops transactions, deliveries, and fund transfers without question" the moment it detects the "worst-case scenario overlap" of AS-index × AML × delivery anomaly × location spoofing × terminal risk.

[0248] <12 Fail-safe switching> Automatic fail-safe switching control in the event of an external API failure If the information processing device of this disclosure detects a response delay, error response, no response, or inconsistent response from the anti-social matching API, banking API, delivery API, or insurance API, The aforementioned information processing device for managing commodity transaction security may further include a fail-safe switching unit that maintains the continuity of transaction processing by automatically switching to at least one of an alternative API, cached data, or local estimation logic. This is the invention.

[0249] The system implements fail-safe functionality through AI integration, automatically switching to an alternative AI logic in the event of an API failure. It has a fail-safe structure that automatically switches to an alternative API, cache, or local estimation upon detecting an external API failure, maintaining the continuity of transaction processing. This is not merely an error notification, but a switching control that ensures continuity without compromising security. It serves as an operational infrastructure layer supporting security and continuity, ensuring safe continuity even in the event of an external API failure. The system includes a fail-safe switching mechanism that automatically switches to at least one of the following—an alternative API, cached data, or local estimation logic—to maintain the continuity of transaction processing if it detects a response delay, error response, no response, or inconsistent response to any of the anti-social group matching APIs, banking APIs, delivery APIs, or insurance APIs. It's not just a simple "communication error countermeasure," but an architecture that balances continuity control and safety control, ensuring that even if an externally dependent module stops, the entire system continues to operate while shifting to a safer mode.

[0250] It has the following three components.

[0251] 1. Fault detection unit This section monitors the API response time, response code, whether a response was received, and the consistency of the response content. It detects issues such as "slow response," "no response," and "inconsistent content." 2. Candidate group for switching At least one of the following could be a candidate: an alternative API, cached data, or local estimation logic. 3. Fail-safe switching section This is the part that automatically transitions to one of the above candidates based on the fault detection result, and instead of stopping processing, it proceeds to continue under safe conditions.

[0252] For example, if a bank API becomes temporarily unresponsive, the transfer approval / failure determination would normally stop, and the entire transaction would likely halt. However, as the first option, an alternative bank API is used. As a second option, we can switch to the most recently matched cache, and as a third option, to the local estimation logic, thus avoiding a complete shutdown while suppressing only the dangerous processes.

[0253] Previously, when an external API failed, the entire system would shut down, the system would be left to manual decisions by the person in charge, and dangerous temporary operations would occur. However, according to this disclosure, intermediate control becomes possible, where processes that should be stopped are stopped, while processes that do not need to be stopped are safely continued.

[0254] In other words, the present invention defines a control technology that, in safety management processing that relies on an external API, does not simply notify an error and stop processing, but rather transitions to an alternative path after detecting an anomaly, thereby keeping the entire system safe and enabling continuous operation.

[0255] Specific examples will be described later.

[0256] The challenge lies in avoiding a double whammy: when an external API fails, either the entire transaction is excessively halted due to the suspension of anti-social group verification, financial verification, delivery tracking, or insurance integration, or conversely, risky transactions continue without sufficient verification. External APIs are prone to communication delays, momentary interruptions, response anomalies, or content inconsistencies, making them vulnerable to real-world operating conditions if they rely on a single system. This invention assumes the occurrence of such failures and prevents both processing stoppages and continued risk by providing alternative APIs, cached data, and local estimation logic as switching candidates.

[0257] In this context, "alternative API" refers to an external linkage via a different route that has the same or equivalent functionality; "cached data" refers to safe data that has been acquired and verified for consistency up to the most recent time; and "local estimation logic" refers to an estimation process that performs a provisional assessment while biasing towards risk based on internally held information and recent history, even when external linkage is unavailable. This allows for a gradual and safe continuation of operations, for example, by continuing only low-value transactions with additional verification conditions while suspending high-value transactions if the anti-social API is unresponsive, or by transitioning to an enhanced delivery mode based on recent tracking cache and internal risk estimation if the delivery API is down.

[0258] The benefits are, firstly, that it reduces the rate of system downtime during external connection failures and maintains the continuity of transaction processing. Secondly, because the switching destinations are designed not merely as detours but as a group of candidates to maintain security, it is possible to achieve both processing continuity and security assurance. Thirdly, the existence of a fail-safe switching unit ensures that even in the event of an external API failure, anti-social group verification, financial matching, delivery monitoring, and insurance processing are not completely interrupted, improving the reliability, availability, and operational stability of the entire antique goods transaction security management system.

[0259] For example, if a bank API becomes unresponsive, normally deposit and withdrawal confirmation would be impossible and transactions would be suspended. However, with the configuration of this invention, while utilizing the most recently verified cache data, only the withdrawal process is stopped if a high-risk flag is set, while preliminary processes such as identity verification and authenticity determination can continue. Furthermore, if the delivery API returns an error, the acquisition of the latest delivery tracking information is suspended, and the system switches to an internal estimation based on the most recent tracking point and the delivery company-specific risk index, allowing monitoring to continue without stopping the update of the delivery anomaly index. Thus, this invention has technical significance in enabling an implementation that "does not compromise security and does not stop too much" even in the event of a failure.

[0260] <13 Similar Feature Search AI> Similar Feature Search AI (Siamese Network / Vector Search) The information processing device of this disclosure vectorizes at least one of the following: product image, engraving features, material analysis features, or brand features. By calculating the degree of deviation from past product features recorded in the vector database, The invention is a product transaction security management information processing device that further comprises a similarity feature search AI unit that extracts candidates matching similar products, related products, or counterfeit product patterns.

[0261] By "vectorizing" various features such as markings, materials, and brands, counterfeit patterns are identified through high-speed similarity searches. This is a similarity feature search AI (Vector Search / Siamese Network) that combines "multiple types of features" such as engraving, material, and brand into a single vector (embedded), and quickly retrieves "similar past examples" from a vector database.

[0262] As features, at least one of the following is vectorized (embedded): product image, engraving features, material analysis features (XRF + specific gravity + magnetism + ultrasound), and brand features (sewing, logo, internal structure, etc.). The distance between these features and all past feature vectors recorded in the vector database is calculated, and those with similar distances (similar vectors) are extracted as "similar products," "related products," and "counterfeit pattern candidates." The similarity feature search AI unit returns a group of candidates such as similar genuine products, similar counterfeit patterns, and patterns originating from the same lot and factory. In other words, the image analysis AI, brand authenticity AI, and material analysis AI extract their respective feature vectors (e.g., 512 dimensions) as features, vectorize them (embed), and then integrate or select them to calculate a single integrated feature vector (embed). This allows for a vector database search (Vector Search), which uses an ANN / Siamese-based database containing vectors for all past products. By calculating distances (cosine similarity, Euclidean distance, etc.), the system quickly searches for similar items and extracts a list of similar products, returning the top N items with the closest distances as "similar products," "related products," and "counterfeit pattern candidates."

[0263] More specifically, each product is first represented (embedded) as a point in a high-dimensional vector space. For example, v = [engraving features, material features, brand features, color features, …] (similar items tend to cluster closer together in this vector space, resulting in smaller / larger cosine similarity or Euclidean distance (this can be reversed depending on which metric is used)). This is based on the natural geometric property that "similar things are close together in a vector space," which is assumed by Siamese networks and embedding learning. Next, statistical clustering (grouping of patterns) is performed. Items made in the same factory, by the same counterfeiting organization, and from the same batch tend to form clusters in the vector space because they have similar errors in markings, materials, and brand structure. Similarity feature search AI assists in inference, such as "this belongs to the pattern of this counterfeiting group," based on this "closeness of clusters." Then, high-dimensional ANN (Approximate Nearest Neighbor) search is performed. As we accumulate the transaction history of precious metals and brands worldwide, it is computationally impossible to compare all of the millions or tens of millions of feature vectors each time. Therefore, graph-based indexes such as ANN (Approximate Nearest Neighbor Search), hash-based (LSH), and HNSW are used to speed up the nearest neighbor search in high-dimensional space.

[0264] Traditionally, 1. Each time, the judgment was made "from scratch," meaning that even if there were similar forgery patterns in the past, that knowledge was not "automatically" utilized in the next case. 2. Only text / model number-based searches are possible; that is, using only product model numbers or brand names, it is not possible to identify "counterfeit groups with the same marking errors" or "counterfeit groups with the same material defects." 3. Image similarity search alone is insufficient; in other words, similarity search based only on images (appearance) cannot capture physical characteristics such as material, markings, and internal structure. By integrating various physical, statistical, and image-based features such as markings, materials, and brands into a single vector, and performing high-speed vector searches across the entire historical database, 1. Past forgery patterns can be reused in order of similarity. 2. Although they are not connected by name or model number, they are similar in terms of feature vectors, so you can uncover hidden patterns / sequences. 3. It can calculate truly meaningful similarity, including not only the image but also the materials and markings.

[0265] Specific examples will be described later.

[0266] Beyond simply combating individual counterfeit goods, this system can lead to the detection and deterrence of organized crime groups. Depending on how it's used, it could even provide recommendations for "buyback, consignment sales, or auctions" based on market prices of similar products and the behavior of similar users.

[0267] By "vectorizing" various characteristics such as markings, materials, and brands, and using high-speed similarity searches to identify counterfeit patterns, from a technical standpoint, 1. The input is multimodal (including not only product images, but also features such as engraving, material, and brand characteristics). 2. The structure of vectorization (embedding) + vector database search is explicitly stated, and it delves into specific technical methods such as ANN and Siamese Networks. 3. Not only is its purpose clearly linked to "identifying counterfeit patterns," but it also features a similarity search function that finds related items and series of counterfeit goods, rather than simply providing recommendations.

[0268] Even when appraising genuine products, consistent pricing and judgments based on past similar cases become possible.

[0269] <14 Counterfeit Anomaly Detection AI> Unknown Counterfeit Product Detection (Anomaly-based Counterfeit Detection AI) The information processing device of this disclosure has respect to at least one of the following: marking features, brand features, material features, damage features, or delivery features. Based on the difference from known genuine data, statistical outliers, anomalous correlation patterns, or the degree of deviance in the trained feature space, The invention is that the aforementioned information processing device for managing the security of commodity transactions may further be equipped with a counterfeit anomaly detection AI unit that calculates a counterfeit anomaly index indicating the suspicion of unknown counterfeit goods.

[0270] Unknown counterfeit goods (unregistered patterns) are detected through anomaly detection. This surpasses conventional authentication methods that rely on comparison with known patterns. This AI evaluates all features such as markings, brand, material, scratches, and shipping based on how far they deviate from the "world of genuine products," and uses anomaly detection to capture "new types of fakes" that have never been seen before. While the aforementioned similarity feature search AI performs "similarity searches with known patterns," this is a dedicated engine that detects "unknown forgeries" that do not resemble known patterns.

[0271] The input features include at least one of the following: marking features, brand features, material features, damage features, and delivery features. Anomalies are defined as the difference from known genuine data, statistical outliers, anomaly correlation patterns, and deviance in the trained feature space. The output is a counterfeit anomaly index that quantifies the "suspicion of an unknown counterfeit product." "Detecting unknown counterfeit goods (unregistered patterns) through anomaly detection is a technical matter that goes beyond conventional authentication methods that rely on known comparisons, and surpasses conventional authenticity authentication methods that only rely on known samples."

[0272] The following five types of characteristics can be entered as input.

[0273] (1) Features of the engraving This includes line width, depth, edge precision, engraving angle, and engraving variation. Engravings that deviate significantly from the distribution of genuine engravings (mean + standard deviation) by 2 to 3 σ are treated as "abnormal engravings." (2) Brand characteristics Stitching pitch, logo shape, hardware shape, internal structure, material texture, and aging deterioration patterns. Individuals that clearly deviate from the "authentic product cluster," which has been learned for each brand and era, are detected as anomalies in the feature space. (3) Material characteristics XRF spectrum, specific gravity, magnetism, and ultrasound. When compared with the elemental ratio, density, and internal structure of a genuine object, anomalies such as "impossible elemental composition," "inconsistency between density and elemental ratio," and "unnatural layered structure inside" are detected. (4) Damage characteristics Depth, length, location, and shape (scratches / dents / impact marks / polishing marks). Abnormalities include unnatural directions that differ from the "natural pattern of wear over time," and concentrated polishing in one area. (5) Delivery features Route, dwell time, weight difference, seal tag difference, impact log (delivery anomaly analysis AI).

[0274] Compared to the "regular distribution pattern for genuine works," it follows an unusually long and roundabout route. A strong impact or similar event at a specific location will be treated as an anomaly.

[0275] For each of these characteristics, the degree of anomaly is calculated using the distance from the genuine cluster, the statistical z-score, and anomaly correlations (e.g., high-quality materials but cheap engraving / logo), and these are compiled into a counterfeit anomaly index.

[0276] The underlying principle is anomaly detection / outlier theory, which assumes that "the world of genuine works has a certain natural distribution," that "differences in marking features are quantified as statistical indicators (Z-score, anomaly index)," that "the counterfeit anomaly detection AI has a hierarchical model by brand, category, era, and workshop, and can handle unknown counterfeit patterns and advanced counterfeiting techniques," and that "feature arrangements, spectral shapes, and image patterns that are completely different from the norm are treated as singularities." Therefore, if the distribution of genuine work data is learned, samples that deviate significantly from that distribution can be treated as "unknown counterfeits" or "singularities."

[0277] Errors in industrial manufacturing (sewing, markings, structure) follow a normal distribution, and elemental ratios, density, waveform, and aging deterioration also follow natural laws. Therefore, anything that deviates statistically from this distribution can be considered an anomaly.

[0278] Traditionally, 1. Because it relies on known patterns, that is, on "Are there any known counterfeits that look similar to this?" and "Is this a legitimate part number listed in a known catalog?", it is vulnerable to newly appearing counterfeiting techniques, new materials, and new methods. 2. If the check is limited to only a part of the item, meaning only the material, image, or markings are examined, it may overlook cases where the item is genuine at that level, but the overall combination is unnatural. 3. Cases where "something is wrong but it doesn't fit into known patterns" cannot be stopped; in other words, even if the appraiser feels something is off, if there is no concrete precedent, they will judge it as "insufficient evidence" and proceed with the appraisal. Instead of evaluating "similarity to known patterns," it directly assesses "deviation from the genuine cluster." For example, if there is a strong anomaly in any one of the following: markings, brand, material, damage, or delivery, the counterfeit anomaly index will be set high. It can also be used as input for the Fusion Authenticity Index, the Overall Risk Index, and Kill Switch. This allows, 1. Capable of handling unknown forgery, new materials, and new methods (detected as "singularities"), 2. We can provide a safety net that quantifies the appraiser's intuition, such as statistically quantifying a vague feeling of suspicion.

[0279] Specific examples will be described later.

[0280] Previously, forgeries that were overlooked because they didn't fit known patterns will now be stopped as "singularities" from the very first instance. The appraiser's "vague feeling of unease" can be quantified as a statistical forgery anomaly index, and even as forgery techniques evolve and materials and methods change, a genuine authentication platform will be realized that constantly stays one step ahead and continues to capture "unknown forgery patterns."

[0281] In other words, it's an "AI forgery anomaly detection system" that finds new types of forgery and unknown methods by asking "How much does it deviate from the 'natural distribution' of genuine items?" rather than "Does it resemble a known pattern?". As a result, the judgment of authenticity becomes a "two-pronged approach that covers both known and unknown forgeries," fundamentally surpassing the conventional "appraisal that relies solely on comparison with known items."

[0282] <15 Delivery Anomaly Analysis AI> Delivery anomaly analysis AI that integrates and analyzes delivery route, seal, weight, and time-series data. The present invention is an information processing device for managing the security of commodity transactions described above, which may further include a delivery anomaly analysis unit that integrates at least two of the following: delivery tracking data including location, time, and dwell time; seal tag image difference; weight difference; IoT sensor data; or delivery company risk information; and calculates a delivery anomaly index indicating deviations, impacts, substitutions, seal breakage, or internal fraud in the delivery process. The delivery safety AI integrates multiple inputs (seal + weight + time series + corporate risk), specifically "delivery tracking logs (GPS x time x stop) + seal tag image differences + weight differences + IoT sensors such as impact + delivery company risk information, all combined into one delivery anomaly analysis AI (delivery anomaly index D) that numerically determines whether 'something unusual is happening during this delivery.'"

[0283] The system inputs delivery tracking data, location (GPS), time, dwell time, seal tag image difference (before dispatch vs. upon arrival), weight difference (declared weight vs. actual weight), IoT sensor data (impact, temperature, humidity, etc.), and delivery company risk information. By integrating this data, it evaluates the presence and degree of deviations (abnormalities in routes and dwell times), impacts, substitutions, seal tampering, and internal delivery fraud, and outputs a "Delivery Anomaly Index (D)".

[0284] For more details, (1) Seal tag matching degree T Based on the difference between the pre-shipment image and the image upon arrival,

[0285]

number

[0286]

number

[0287] (3) Delivery route anomaly score P

[0288]

number

[0289] Quantifying "suspicious activity that differs from normal delivery" Because they use a "nearly fixed route" based on cost, time, and road structure, any significant deviation or unusually long delays strongly suggest artificial detours, concealment, or substitution.

[0290] (4) Delivery Anomaly Index D (Final output of the delivery AI)

[0291]

number

[0292] The more the seal is broken (the greater the 1-T) The greater the weight difference (the larger the 1-W value) The route, stopping point, and impact are unnatural (P is large). A large increase in D strongly suggests a delivery error. Traditionally, 1. It's often unclear "where and what happened," meaning that even in cases of loss, substitution, or damage, it's difficult to determine whether the sender, delivery company, or recipient is at fault, leading to arguments where one party is primarily responsible. 2. Delivery anomalies are only detected by "human intuition," meaning that the route, time, weight, seal, and impact are not integrated and monitored as a system, and anything other than obvious anomalies is overlooked. 3. It is a "logistics black box" separate from authenticity verification, anti-social activities, and AML, and while the risks to people and goods are assessed, the logistics process itself is not evaluated numerically. By integrating GPS logs, stoppages, impacts, weight, seals, and corporate risks, "unnatural behavior" during the delivery process can be visualized as a single index, D. Furthermore, D serves as input for the automated insurance claim AI, risk integration AI, and Kill Switch, enabling the realization of the process safety concept of "stopping payment, delivery, and fund transfer if the delivery is suspicious, even if the item is genuine."

[0293] In short, it can accurately detect substitution, theft, seal rupture, accidental drops, and internal fraud during transit, automatically assisting with transaction suspension, insurance claims, and liability demarcation, while simultaneously proving that "the delivery was undamaged." This enables fair and transparent trouble resolution among users, merchants, delivery companies, insurance companies, and government agencies.

[0294] Specific examples will be described later.

[0295] <16 Automated Insurance Claim AI> Automated insurance claim generation AI based on delivery anomalies, damage, and authenticity index The information processing device of this disclosure, based on at least one of the following: the overall authenticity index, the delivery anomaly index, the seal tag detection result, the weight difference result, the damage analysis result, or the delivery company log, Estimate the possibility of a delivery accident or damage occurring. The invention further includes an AI unit for automated insurance claims that automatically extracts items requested by insurance companies, including accident cause, evidence data, time of occurrence, delivery route, or product information, and generates insurance claim data. This AI automatically compiles necessary information, extracts the "accident cause, evidence, time, route, and product information" requested by insurance companies, and automatically generates insurance claim data (insurance claim forms). The AI ​​automatically generates insurance claims (accident estimation × evidence extraction).

[0296] Based on inputs such as the overall authenticity index, delivery anomaly index, seal tag detection results, weight difference results, damage analysis results, and delivery company logs (at least one, usually multiple), the system estimates the "possibility of a delivery accident or damage occurring." It then automatically extracts the accident cause, evidence data, time of occurrence, delivery route, and product information required by insurance companies, and automatically generates insurance claim data (invoice / application data). It can also generate insurance claim data in a format that can be sent directly to insurance companies. In other words, insurance claim processing can be automatically generated based on delivery tracking data, seal tag images, weight difference data, and AI-generated anomaly detection logs, thereby reducing the burden on customers and merchants.

[0297] Traditionally, 1. Insurance claims rely on "paper and manual processes," meaning customers and affiliates collect photos themselves, fill out documents, explain the date, location, and cause of the incident, and submit them to their insurance representatives or agents. This results in insufficient / subjective information and a heavy burden on insurance companies for investigation. 2. It is difficult to detect fraudulent charges, meaning it is hard to determine whether "it was really a delivery accident" or "was it broken from the start?", which often leads to arguments and a structure where only malicious users benefit. 3. AI judgment and insurance operations are not connected. Even if AI is used for authenticity verification or delivery, insurance claims are processed through a separate system and workflow, and the AI ​​results are not directly used for insurance decisions. The AI ​​classifies the cause and causal relationship of damage from damage analysis, delivery anomaly index, and authenticity index. Then, it automatically extracts accident cause, evidence data (images, logs, scores), time of occurrence, delivery route, product information, and assessed value in accordance with the insurance company's API, and creates insurance claim data (equivalent to an invoice). This allows, 1. The burden of insurance administration on customers and affiliated stores will be significantly reduced. 2. Because it is based on AI-driven causal identification, legitimate accidents will be paid quickly, and fraudulent claims will be easier to scientifically deny.

[0298] Specific examples will be described later.

[0299] Thus, users and affiliated stores only need to report that "an accident occurred." This system minimizes tedious paperwork, allows insurance companies to quickly pay only legitimate claims based on AI logs (materials, delivery, damage), and reject fraudulent claims with scientific evidence. For the platform, it integrates a secure transaction infrastructure with an insurance and compensation infrastructure, creating an ecosystem that doesn't disrupt the user experience even in the event of an accident.

[0300] <17 Franchisee Training AI> "Franchisee Training AI" that learns and provides feedback on the appraisal quality of franchisees. The information processing device disclosed herein uses machine learning to acquire at least one of the following data from a merchant: appraisal results, authenticity matching rate, damage detection accuracy, price deviation rate, claim occurrence rate, processing speed, or counterfeit detection history for each merchant. We will calculate an appraisal quality index for each member store, The invention is a product transaction security management information processing device described above, which may further include an AI unit for franchisee training that presents assessment procedures, points to note, or items for improvement based on the aforementioned assessment quality indicators.

[0301] It's not just about education; it learns about authenticity matching rates, forgery detection, appraisal history, and market deviations, all backed by technical expertise. This "franchise training AI" learns from the appraisal result data of each franchise store, calculates an "appraisal quality score" for each store, and automatically provides feedback on specific areas for improvement based on that score.

[0302] Assuming that a "Store Report Card" exists, the AI ​​learns from at least one of the following as input data: appraisal results at the store (actual appraisal amount and judgment), authenticity agreement rate (agreement rate between AI authenticity result and store judgment), damage detection accuracy (how accurately scratches and defects were found), price deviation rate (difference between AI recommended price / market price and store price), complaint rate (frequency of complaints or troubles later), processing speed (time taken for appraisal), and store-specific counterfeit detection history (how well counterfeit items were detected / missed). The AI ​​then calculates an appraisal quality index (similar to a Store Assessor Quality Score) for each store and provides the store with suggestions for improving the appraisal procedure, points to watch out for that are often overlooked, and specific areas for improvement (e.g., "This brand is weak in authenticating, so additional training is recommended"). "The dashboard for member stores visualizes appraisal quality, price fairness index, counterfeit detection rate, complaint rate, and customer satisfaction in real time, and the AI ​​also suggests 'points for improvement,' 'factors of risky behavior,' and 'optimal appraisal guides.'" The system scores "how accurately this store (this appraiser) can authenticate and appraise items with the same precision as AI" and "how much they deviate from market prices and safety standards," and this can be fed back into education, training, and internal controls. It can also be used to evaluate the accuracy of individual appraisers and to detect fraud at franchise stores.

[0303] For more details, (1) Authenticity match rate, that is, how well the "AI authenticity result" and the "merchant's judgment" match, is categorized as TP (correctly identified as genuine), TN (correctly identified as counterfeit), FP (misidentifying a counterfeit as genuine: risky), and FN (misidentifying a genuine as counterfeit: opportunity cost). Statistically evaluated using methods such as, (2) Evaluate the accuracy of damage detection, i.e., the difference between AI damage analysis and human observation, and score whether there are many missed defects or whether the damage is being overestimated. (3) The price deviation rate, that is, the difference between the "price range recommended by AI" or "market price" and the appraisal amount actually offered by the member store, is statistically measured (mean deviation, standard deviation, etc.). (4) The complaint rate, i.e., the number of complaints / total number of transactions, is calculated for each member store, and the content of the complaints (undervaluation, overvaluation, purchase of counterfeit goods, insufficient explanation, etc.) is further classified and a coefficient is applied. (5) From the processing speed, i.e., the time distribution taken for assessment, measure whether it is abnormally fast and therefore sloppy, or conversely, too slow and therefore inefficient. (6) The "counterfeiting response capability" is evaluated based on the counterfeit detection history, that is, how often counterfeit goods are detected and the number of cases where counterfeiting was not detected and was discovered later. Combining these,

[0304]

number

[0305] Traditionally, 1. Appraisal quality is entirely dependent on the "experience of the person and the store," meaning that the accuracy of authenticity determination, how damage is assessed, and pricing methods vary greatly from store to store and from appraiser to appraiser. 2. The training is one-off and not based on data; that is, it consists only of lectures and manual distribution, and does not delve into "who" is weak and "where". 3. Detecting internal fraud and arbitrary assessments is difficult; that is, even if assessments deviate significantly from market value over a long period, they tend to be overlooked as "the individual's personality." By learning from the AI's "closest-to-correct labels," market prices, and complaint history, it becomes possible to quantify the accuracy of authenticity detection, price fairness, service quality, and potential for fraud for each franchisee and appraiser as "appraisal quality indicators." Furthermore, based on these indicators, it becomes possible to implement "continuous training + internal controls," including visualization on a dashboard, automated improvement suggestions, and audits and suspension of qualifications in the event of serious internal fraud.

[0306] As a specific example, if a franchisee is good at authenticating but always offers excessively low prices, Store X may have a high authenticity rate (almost identical to the AI's judgment) and a good history of detecting counterfeits (it also accurately identifies fakes). However, if the price deviation rate from the AI-recommended price / comparison with other stores is consistently between -10% and -20% (tendency to buy at low prices), the franchisee training AI's feedback would include "Authenticity Accuracy: ◎" and "Price Appropriateness: △" as breakdowns of the appraisal quality indicators. The dashboard would then display "Consistently lower prices than the market average. Price review recommended for some brands and categories." By reviewing Store X's pricing policy and incentives, it is possible to prevent unfairly low purchase prices and the accumulation of customer dissatisfaction.

[0307] Furthermore, by disseminating the know-how of top-performing franchisees throughout the entire system, and visualizing the "tacit knowledge of top-performing stores" as concrete checklists and guides via AI, it may be possible to unify and raise the overall assessment level, ultimately improving the health of the market.

[0308] In short, this reduces variations in assessment quality among member stores, making it easier to achieve "the same level of safety and quality at every store," enabling early detection and correction of internal fraud and arbitrary assessments, and directly contributing to improved reliability and soundness of the entire market. Furthermore, because training and education become "data-driven rather than uniform," it can function as a training system that significantly increases effectiveness without increasing the burden on employees.

[0309] <18 Trading Recommendation AI> Trading recommendation AI based on user and product characteristics The information processing device disclosed herein extracts recommendation features from at least one of the following: customer transaction history, product category, brand, material authenticity index, price range, or market demand index. The invention further comprises a transaction recommendation AI unit that presents a suitable transaction method, recommended merchant, or appropriate price range to the trader based on the aforementioned recommendation features, and is an information processing device for managing the security of commodity transactions as described above.

[0310] The "Recommendation Feature" integrates product characteristics, authenticity index, and market index. It is an AI that recommends the optimal channel / option for transactions to the user, taking into account "all information" such as authenticity, price, risk, merchant score, and delivery risk.

[0311] By inputting user attributes and behavioral history, product characteristics (brand, material, price range, rarity), merchant score, delivery risk, overall risk, and Kill Switch judgment, the recommendation model calculates which merchant, which channel (mail order / in-store / consignment / auction, etc.), and under what conditions (price range, delivery conditions, insurance conditions) is best for "this user x this product." It can provide specific recommendations to users such as "bring it to store A in-store," "mail order with insurance under these conditions," or "selling it at auction is likely to yield a higher price," and recommendations to merchants such as "this is the explanation you should give this user" or "the conversion rate is high within this price range," providing a "matching + recommendation function."

[0312] For more details, first 1. User information includes past sales and purchase history, preferences (brand, category, price range), and convenience orientation (speed-focused / price-focused / safety-focused, etc.). 2. Product information includes Fusion Authenticity Index, brand, model, material, condition (damage score), rarity, market value, and price range. 3. As information for member stores, we request appraisal quality indicators, specialty brands / categories, complaint rate, and customer satisfaction. 4. Enter the following as process risk information: delivery risk, delivery anomaly index, insurance conditions, and overall risk / transaction restrictions. The AI ​​can provide transaction recommendation output to users such as, "For you, bringing this watch to store A (10-minute walk) + this price range seems to offer the best balance of price, safety, and speed," or, "Since it's a rare vintage item, we recommend putting it up for auction with a minimum bid price of XX million yen rather than immediate purchase." It can also provide affiliated stores with recommendations such as, "Since this user has purchased from this brand multiple times in the past, suggesting XX and YY from the same brand's inventory will increase the chances of a successful transaction."

[0313] Through statistical learning, Pareto learns patterns from vast amounts of past transaction data, such as "under what conditions did it sell for the highest price," "which combinations resulted in fewer complaints," and "which stores are a good match." It then performs multi-objective optimization to find and recommend Pareto's optimal recommendations, simultaneously satisfying the user's objectives (price, speed, and safety) and the platform's objectives (channels with low accident and fraud risks). However, recommendations may be made within compliance constraints, such as routes prohibited by Kill Switch, prohibition of postal delivery to high-risk users, and restrictions on transactions with specific countries.

[0314] Traditionally, 1. It is difficult for users to independently determine "where and how to sell best," meaning that with numerous stores and channels (in-store, mail order, auction, consignment, etc.), it is difficult to determine which option is best for them. 2. Safety, compliance, and pricing are managed separately; in other words, if you only look at the "price," this store may seem good, but it doesn't consider anti-social risk, delivery risk, or franchisee quality together. 3. Determining the "optimal way to distribute" the data from the system's perspective is also difficult; in other words, it might be safer to concentrate the data at stores with thin margins from an anti-social / AML ​​standpoint, but this would be too disadvantageous to users in terms of price, and it is impossible to adjust everything manually. By integrating authenticity, price, risk, and merchant score into a single scoring space, and considering individual user "weights" (safety > price, price > speed, etc.), the platform can recommend the best route, store, and channel for "this user × this product × this timing." As a result, users can more easily sell and buy "at a high price, safely and smoothly" in a way that aligns with their values, and the platform can naturally guide traffic to routes that minimize the risk of accidents, complaints, and fraud.

[0315] Specific examples will be described later.

[0316] In short, it is a "transaction recommendation AI" that integrates users, products (authenticity, materials, condition), merchants (quality indicators), and risks (anti-social elements, AML, delivery risks), providing the "brain that suggests the optimal route" that seamlessly connects users, merchants, platforms, insurance, and security.

[0317] <19 Market Health Index> Generation of Market Health Index The information processing device disclosed herein takes at least three of the following as input: counterfeit detection rate, anti-social activity rate, anti-money laundering activity rate, delivery accident rate, assessment discrepancy rate, regional security index, or legitimate transaction rate. The invention is that the aforementioned information processing device for managing the security of commodity transactions may further include a market health analysis unit that calculates a market health index indicating the overall health of the market. It integrates multiple security indicators such as counterfeit rates, anti-social activity rates, and AML rates, along with market indices. It serves as the operational infrastructure layer supporting safety, continuity, and transparency, and provides high-level monitoring of the overall health of the market.

[0318] This is a meta-indicator that monitors the entire market from a high-level perspective, rather than making individual trade judgments. In other words, it numerically captures and judges whether the market, region, or period itself is healthy, not just the risks of individual products or individuals. This is an analysis unit that integrates multiple market anomaly indicators to generate a top index that shows the overall health of the market. This technology measures the state of the market itself, rather than individual risks. Traditionally, countermeasures against counterfeiting, AML (Anti-Money Laundering), delivery accidents, and discrepancies in assessments were handled separately. This new approach integrates these into a single market health index, enabling dynamic monitoring of the market environment. For example, if the counterfeit detection rate, delivery accident rate, and suspected anti-social activities are all rising in a particular region, simply looking at individual transactions won't reveal the overall trend. The Market Health Analysis Department can integrate these factors and determine that "the market in this region and during this period is unhealthy."

[0319] In a healthy market, the rate of legitimate transactions is usually high, counterfeiting, accidents, and doubts are low, and valuation discrepancies are stable. However, when the market becomes volatile, these abnormal indicators tend to become skewed simultaneously, and this skew in multivariate statistics is treated as a single index. Traditionally, problems were detected retrospectively, with people only noticing "something's been off in this area lately" after a problem occurred. This disclosure will allow for the quantitative and early identification of signs of market deterioration, making it easier to tighten trading conditions, strengthen merchant screening, change delivery routes, and create objective data for reporting to authorities.

[0320] The following formula can be used as a specific calculation method.

[0321] [Market Health Index (MHI)] The Market Health Index includes "counterfeiting rates, AML cases, accident rates, price discrepancies, and public safety index." It is a macroeconomic indicator that integrates various factors.

[0322]

number

[0323] Brand headquarters are sensitive to counterfeiting of their own brand → Make the α1 larger. Government prioritizes AML → Expand Alpha 2 By changing the parameters, we can express "whose health is being considered."

[0324] Algorithm of the Market Health Index 1. Counterfeiting ratio 2. Ratio of anti-social elements 3. Number of AML cases 4. Delivery accident rate 5. Price discrepancy → Integrate and index principle Markets are stochastic processes, and the accumulation of anomalies increases the "risk of market collapse."

[0325] → Utilize the fundamentals of economic engineering. Automatic filing of STRs (Suspicious Transaction Notifications) is also possible.

[0326] Specific examples will be given later.

[0327] The technical feature of this invention is that it includes a market health analysis unit that calculates a market health index indicating the overall health of the market, taking at least three of the following as inputs: counterfeit detection rate, anti-social activity rate, anti-money laundering suspicion rate, delivery accident rate, appraisal discrepancy rate, regional security index, or legitimate transaction rate. In other words, this invention does not limit itself to the determination of individual goods or individual traders, but quantifies the state of the entire antique goods market as a higher-level indicator, and defines a technology for quantitatively grasping the abnormality or health of a market that spans the long term, a wide area, and multiple channels.

[0328] The problem we aim to solve is that, in conventional antique trading practices, counterfeit prevention, anti-social activities, AML measures, delivery management, appraisal quality control, and local security information were all handled separately. As a result, it was impossible to grasp, using a unified indicator, whether the market as a whole was deteriorating or whether fraud was increasing concentrated in a particular region or brand. For example, even if you look only at the counterfeit detection rate, it is difficult to discern the fundamental deterioration trend of the market without also looking at the increase in delivery accident rates and the number of suspected AML cases. This invention solves this problem by simultaneously inputting different market anomaly indicators and integrating them into a single market health index.

[0329] The market health index is composed of multiple indicators, including counterfeit rates, anti-social activity rates, AML (Anti-Money Laundering) suspicion cases, delivery accident rates, assessment discrepancies, regional security index, and legitimate transaction rates. Therefore, it can comprehensively reflect biases in multiple elements that constitute the market, rather than just a single outlier. In particular, it is highly compatible with a structure that uses statistical data after anonymizing individual transaction logs, allowing for continuous tracking of market health by region, brand, channel, and time period without identifying individuals. This enables the creation of a market diagnostic infrastructure that balances privacy protection with market surveillance.

[0330] The benefits are, firstly, that it allows for the early detection of signs of market deterioration and can be used to prioritize trade restrictions, strengthen delivery, audits of member stores, or cooperation with government agencies. Secondly, by visualizing regional and brand-specific differences, it becomes easier to identify localized trends such as the influx of counterfeit goods, the infiltration of anti-social elements, or a high incidence of accidents. Thirdly, it goes beyond mere individual risk management and establishes a foundation for continuously monitoring the health of the entire antique goods trading market.

[0331] For example, if the counterfeit detection rate, delivery accident rate, and suspected anti-social activity rate all rise simultaneously in a given region, the market health index will decline, allowing for policy and operational controls such as temporarily restricting postal transactions in that region, strengthening merchant screening, or increasing the weight of the risk index for individual delivery companies. Conversely, in markets with a high rate of legitimate transactions, a low rate of appraisal discrepancies, and a stable regional security index, the health index will remain high, which can be used for measures such as expanding the number of new merchants or easing price lock-in conditions. Thus, the present invention has significant importance as a high-level analytical technique for "health checks" of the entire market.

[0332] <20 Adaptive UI Control> Adaptive UI optimized according to user attributes and device characteristics The information processing device disclosed herein, based on at least one of the following: the customer's age group, user experience, the performance of the customer's information processing terminal, screen size, operation history, or language settings, The invention further includes an adaptive UI control unit that automatically adjusts the display items, layout, explanatory content, and operation guidance, as described above, for information processing equipment for managing the security of commodity transactions. It's not an ergonomic UI, but rather an AI-driven, dynamic adaptive system.

[0333] This is an adaptive UI control AI that automatically switches screen items, layouts, descriptions, and operation guides based on the user's age and experience, the device's performance and screen size, and actual operation history and language settings.

[0334] The systems described so far (authenticity AI, delivery AI, insurance, training, market indices, recommendations, etc.) operate "behind the scenes," while the "front-end UI" is technically controlled based on the results and user attributes. This is done by inputting UI optimization data (user attributes and device characteristics) based on "at least one" of the following: age group, user experience (beginner / intermediate / advanced, etc.), device performance (CPU, memory, connection speed, etc.), screen size (smartphone / tablet / PC, resolution), operation history (where users tend to get stuck, transition patterns), and language settings (Japanese, English, Chinese, Korean, etc.). These are the items that are displayed. This is an "adaptive UI control unit that automatically adjusts" which information to show / hide (e.g., whether to display detailed risk indicators or simplified ones), layout, button size and placement, amount of information per screen (card type / list type), explanation content, amount of text, level of technical terms and difficulty of expression, operation guidance, tutorial display, presence or absence of step guides, and explanations and next step suggestions in case of errors.

[0335] Depending on the PC or large tablet model, the button size, information density, length of explanatory text, and error guidance may change. The UI should support multiple languages ​​(Japanese, English, Chinese, Korean, etc.), with automatic translation of explanatory text, assessment guides, and notification messages, and the UI expression should be optimized according to regional culture. This would also improve the understanding of international users. However, 1. Age group, device performance, screen size, operation history, and language settings are all data that the system can acquire and record, and 2. The processing has a technical structure like "conditional branching + automatic layout generation," for example, internally

[0336]

number

[0337] Traditionally, 1. The "Same UI for Everyone" problem: The service is used with the same screen, explanations, and amount of information, regardless of age, smartphone or PC usage, and whether the user is a beginner or a professional. As a result, errors, misunderstandings, user drop-offs, and complaints occur. 2. There is a rigidity in the UI when expanding internationally; in other words, simply translating screens created for the domestic market into English will not allow for adaptation to differences in culture, regulations, and customs. 3. The way risk information is conveyed is not suitable for users. Even if AI performs advanced risk assessments, it is difficult for users to understand "how dangerous it is" and "what they should do." On the other hand, it is possible to display a "visually appealing and easy-to-understand" screen that suits the user's attributes and device, and for the platform to automatically generate a UI that conveys risk information and contract information without misunderstanding, thereby reducing complaints, errors, and problems caused by insufficient explanation.

[0338] Specific examples will be described later.

[0339] In other words, by tailoring the UI to each user's level of understanding, physical characteristics, and device environment, it becomes possible to significantly reduce errors, abandonment, and misunderstandings. As a result, the sophisticated judgment results of the AI ​​for authenticity and risk assessment can be presented in a way that is easiest for each user to understand, leading to a reduction in complaints and troubles, increased use by international users, and a financially secure interface that can be used with confidence even by the elderly and beginners, all realized through AI + ergonomics-based technical UI control.

[0340] <21 Data Anonymization Processing> Data anonymization processing that completely anonymizes personal information while still making it analyzable. The information processing device disclosed herein handles at least a portion of the following: name, address, biometric information, customer information processing terminal ID and other transaction identification information, financial information, location information, or delivery information. To ensure that personal identification becomes impossible after the retention period has expired, the data is converted into anonymized data by performing at least one of the following processes: deletion, masking, random number substitution, or hashing. The anonymized data can be used for authenticity detection model training, risk analysis, or market statistics. The invention is that the aforementioned information processing device for managing the security of commodity transactions may further include a data anonymization processing unit.

[0341] Structured technical anonymization to comply with GDPR (EU General Data Protection Regulation) / Personal Information Protection Act. This data anonymization process takes "raw data linked to an individual," such as name, address, biometric information, device ID, financial information, location information, and delivery information, and deletes, masks, replaces with random numbers, and hashes it in a way that makes it impossible to identify the individual again after the retention period has expired, while still making it usable for AI learning, risk analysis, and market statistics. This system balances privacy protection with the utilization of AI.

[0342] The system performs "at least some" of the following processes: deletion, masking (e.g., replacing part of a name with "**"), random number substitution (replacing with a random ID), and hashing (passing through a one-way function that cannot be reversed). After a predetermined retention period, it combines at least one of these processes—deletion, masking (e.g., replacing part of a name with "**"), random number substitution (replacing with a random ID), and hashing (passing through a one-way function that cannot be reversed)—to convert the data into a form that makes personal identification impossible. The purpose of use is for analysis and AI learning that does not require the identification of individuals, such as for authenticity model training, risk analysis, and market statistics.

[0343] For more details, first 1. During normal operation (within the retention period), raw data such as name / address / biometrics / device ID, bank transaction history, GPS logs / delivery logs are stored and used for fraud detection, complaint handling, insurance claims, etc. 2. As a determination of the expiration of the retention period, periodically check the timestamp attached to each log to see "Has Y years passed since its creation?" 3. Perform anonymization processing, for example, deleting or masking names and addresses, replacing terminal IDs and user IDs with random IDs, hashing bank account numbers, and converting precise latitude and longitude into regional codes (city / town level).

[0344] This makes it impossible to recover "whose data it is." The logs, with personal identifiers removed, can then be used as training data for AI models that determine authenticity, for tuning comprehensive risk models, and as statistical material for market health indices, etc.

[0345] (1) Irreversibility of hashing Hashing is the irreversible transformation of original data (such as passwords or files) into a fixed-length random string (hash value) using a specific computational procedure called a hash function. A well-designed hash function makes it easy to calculate the input → output, but practically impossible to calculate the output → input. In other words, it achieves "de facto irreversibility," meaning that the original personal information cannot be recovered from the hash value with a realistic amount of computation. (2) Statistical properties of random number permutation The ID replaced with a random number will be an independent value unrelated to the original user (assuming appropriate random number generation). (3) Trade-off between preserving statistics and identifying individuals Anonymization is also an information-theoretic trade-off problem, where it makes it impossible to identify individuals while preserving statistical trends (such as forgery rates, accident rates, and regional risks). To enable its use in "authenticity model learning, risk analysis, and market statistics," personal IDs will be removed, but brand type, region code, transaction amount range, etc., will be retained.

[0346] Traditionally, 1. It often comes down to a choice between "delete everything" or "keep everything." In other words, if you "delete everything" for the sake of protecting personal information, the learning materials for AI and statistics will also be deleted. Conversely, if you keep everything, the risk of violating GDPR and personal data protection laws increases. 2. Anonymization is haphazard (manual, Excel level), meaning it's not structured as a system, processed haphazardly by each department, making it unclear who deleted what and making it impossible to assess the risk of re-identification. 3. The design for balancing AI learning and legal compliance is unclear; that is, questions such as "Will reducing data decrease AI accuracy?" and "To what extent is anonymization legally acceptable?" are not clear at the technical design level. at the system level 1. Personal information that has exceeded its retention period, 2. Complete anonymization is achieved through technical means such as deletion, masking, random number substitution, and hashing. 3. Nevertheless, it has a "processing flow" that utilizes it for AI learning and statistical analysis.

[0347] As a result, compliance with laws and regulations (GDPR, Personal Information Protection Act) and continuous advancement of AI We can design a data infrastructure that can achieve both of these goals simultaneously.

[0348] Specific examples will be described later.

[0349] After a certain period, the data will only remain in a form that cannot be restored to show "who did what," significantly reducing the risk of privacy violations and re-identification. For platforms, brands, insurance companies, and government agencies, this allows for long-term analysis of counterfeit trends, accident rates, and market health without identifying individuals, enabling compliance with laws and regulations without compromising the accuracy of AI and statistics. Overall, it enables the construction of a next-generation transaction infrastructure that achieves a balance of "security, AI, and privacy."

[0350] <22 AI Fairness Correction> Bias detection and correction mechanism to maintain fairness in AI judgments (AI fairness) The information processing device disclosed herein has at least one of the following functions: authenticity determination AI, behavioral abnormality AI, anti-social group analysis AI, anti-money laundering scoring AI, or delivery abnormality AI. Analyze statistical deviations, inter-group imbalances, or excessive influence on specific attributes caused by input features. In addition to calculating an equity index that shows the degree of bias, The invention further includes an AI fairness correction unit that automatically applies at least one of the following: adjustment of feature weights, retraining, feature exclusion, or correction processing, if the fairness index deviates from an acceptable range.

[0351] AI-based fairness check + automatic correction function → Integration of ethical AI and commercial transactions It serves as the operational infrastructure layer supporting safety and transparency, and oversees the fairness of AI-based decision-making. This is a supervisory correction module that quantifies the judgment deviation of each AI as a fairness indicator and automatically corrects it when it deviates from an acceptable level. This is not about AI itself, but rather control technology for the healthy operation of AI systems. It's not about the AI's performance itself, but rather a higher-level safety mechanism that monitors and corrects biases in AI judgments. It not only ensures high accuracy, but also continuously monitors within the system to prevent unfair biases.

[0352] The constituent elements are, 1. Deviation analysis, 2. Calculation of fairness indicators, 3. Automatic correction in case of tolerance deviation It has a three-tiered structure.

[0353] For example, if the behavioral anomaly AI consistently scores excessively high for a certain attribute group, the AML score is unnecessarily strict, or the delivery anomaly (analysis) AI consistently scores high only for specific regions, the fairness index will deteriorate. In this case, the AI ​​fairness correction unit will reduce the bias by performing weight adjustments, retraining, and excluding problematic features.

[0354] In AI learning, statistical deviations naturally occur due to biases in the training data and peculiarities in feature selection. Rather than denying these deviations, we assume they will occur and honestly monitor and correct them from an engineering perspective.

[0355] Previously, even if AI judgments were biased, the operators often only noticed it later through complaints or audits, leading to inexplicable biases, unfairness, and legal risks. However, this disclosure allows biases to be visualized using internal indicators, enabling autonomous correction.

[0356] Specific examples will be provided later.

[0357] The challenge lies in the fact that even if an AI model is highly accurate, bias in the training data or uneven distribution of input features can lead to the continuous issuance of excessively strict or excessively lenient judgments for specific attribute groups, regional groups, or behavioral groups. In a secondhand goods transaction security management system, since authenticity determination, anti-social group detection, AML detection, behavioral abnormality detection, and delivery abnormality detection all directly affect whether a transaction can be approved or stopped, if a biased AI is allowed to operate as is, unfair results, false stops, or oversights will accumulate, damaging the reliability of the entire system. This invention makes it possible to autonomously monitor and correct such biases within the system before they are pointed out during audits after operation.

[0358] First, the system analyzes the relationship between each AI's judgment result and the input features to determine statistical deviations, inter-group imbalances, or excessive influence on specific attributes, and calculates a fairness index. Then, if the fairness index deviates from the acceptable range, it automatically applies adjustments to feature weights, retraining, feature exclusion, or correction processes. Therefore, this invention is not merely a "warning" mechanism, but a closed-loop supervisory module that integrates bias detection and correction execution into a single loop.

[0359] The benefits are, firstly, that it allows for continuous monitoring of the fairness of AI judgments and the correction of any biases during system operation. Secondly, because it can be applied across multiple AI modules, such as authenticity detection, anti-social / AML ​​systems, and delivery anomaly detection systems, it enhances the reliability of the entire system rather than requiring individual repairs. Thirdly, because critical controls such as transaction suspension, additional verification, and delivery restrictions do not rely solely on the output of overly biased AI, legal explainability, user acceptance, and regulatory compliance are improved.

[0360] For example, if a delivery anomaly (analysis) AI consistently issues a high-risk judgment for deliveries to a specific region, the fairness correction unit of the present invention can identify this bias as a fairness indicator and correct it through weight adjustment or retraining of regional features. Furthermore, if an AML scoring AI is overreacting to a certain group of attributes, it can reduce false over-detection by weakening or excluding features strongly associated with those attributes. Thus, the present invention has technical significance in realizing not just "highly accurate AI," but "a group of AIs that can be operated without bias."

[0361] <23 Automatic API Anomaly Switching> API Anomaly Control Unit that detects API anomalies (delay, unresponsiveness, inconsistent response) and switches dynamically. If the information processing device of this disclosure detects a response delay, error response, no response, or content inconsistency with respect to at least one of the following APIs: anti-social API, financial API, delivery API, insurance API, or market data API, Automatically switches to at least one of the following: alternative API, cached data, or local estimation model. The invention relates to the aforementioned information processing device for managing the security of commodity transactions, which may further include an API anomaly control unit that maintains continuous transaction processing.

[0362] It can determine the type of API error and switch dynamically. It is an operational infrastructure layer that supports safety and continuity, and provides detailed control and automatic API failure failover for API anomalies. This control unit detects not only delays and unresponsiveness but also content inconsistencies as abnormalities for multiple types of APIs, and automatically switches to alternative APIs, caches, or local estimations to maintain continuous processing.

[0363] While the previously described fail-safe switching unit encompasses a relatively broad concept of "safe continuity," this invention more explicitly defines itself as an API anomaly control unit, enumerating a group of target APIs, detecting even content inconsistencies, and concretely implementing switching to alternative APIs, caches, and local estimation models. In other words, it goes beyond simple failure response, targeting cases where the API response content "cannot be trusted even if a response is received," treating not only no response but also inconsistent responses as anomalies. In cases where an API is alive but its content is corrupted, some nodes return outdated information, or a market data API momentarily returns an anomaly, normal health monitoring alone is insufficient. This invention fills that gap, enabling switching control that includes not only health checks but also consistency checks.

[0364] Specific examples will be given later.

[0365] This can be positioned as a more refined version of the previously described fail-safe switching unit, with a clearer definition of the target API and types of abnormalities. In particular, the significance of this invention lies in the fact that it explicitly defines "content inconsistency" as an abnormality, in addition to mere response delays or no response. That is, even if an API formally responds, if its content is not logically consistent with past values, internal values, or the state of other modules, it is treated as an abnormality, thereby preventing dangerous control based on incorrect external information.

[0366] The challenge lies in preventing miscontrol caused by "live but unreliable API responses" that cannot be detected by API availability monitoring alone. In the antique goods transaction security management system, anti-social group verification, financial linkage, delivery tracking, insurance application, and market data reference depend on external APIs. If erroneous responses are treated as normal values, it can lead to failures to stop transactions, oversight of risky deliveries, incorrect insurance processing, and miscalculations of market indices. This invention solves this problem by detecting anomalies, including content inconsistencies, and immediately redirecting to an alternative route.

[0367] The benefits are, firstly, that it reduces vulnerabilities due to external dependencies in integrated systems where multiple external APIs coexist. Secondly, it achieves a higher level of security than simple availability monitoring by eliminating cases where a response exists but cannot be trusted. Thirdly, it prevents intermittent interruptions in transaction feasibility determination, delivery monitoring, insurance processing, and market indicator updates through the API anomaly control unit, thereby ensuring both transaction continuity and security.

[0368] For example, if a market data API returns a suddenly abnormal market price, the present invention can detect this value as a content inconsistency and switch to a cached market price or an alternative market API. Furthermore, even if a delivery API returns data that is inconsistent with the current location and time, the local estimation model can continue calculating the delivery anomaly index, enabling appropriate suspension of postal transactions or transition to a delivery enhancement mode. Thus, the present invention is a highly reliable collaboration technology that includes not only the "presence or absence" of API responses but also their "correctness" as control targets.

[0369] <24 Automatic Learning of Fraudulent Templates> AI that automatically generates fraudulent templates by continuously learning forgery patterns and fraudulent behavior patterns. The information processing device disclosed herein analyzes at least one of the following: differences in markings, abnormal material composition, abnormal weight patterns, abnormal delivery patterns, behavioral inconsistencies, or tendencies to indicate suspected anti-social activities or anti-money laundering, which are characteristic of counterfeit goods. Automatically generate fraud templates representing new forgery techniques or fraudulent behavior patterns. The invention is that the aforementioned information processing device for managing the security of commodity transactions may further include a fraudulent template learning unit that continuously trains each AI model with the aforementioned fraudulent templates.

[0370] Automatically generate and learn from unknown forged patterns → GAN (Generative Adversarial Network) anomaly detection Of the cases previously identified as "anomalous," those confirmed to be "truly forged / fraudulent" through human review and subsequent investigations are "templated" as feature patterns such as markings, materials, brands, delivery, behavior, and finance. These templates are then fed back into detection engines such as similarity search, anomaly detection, overall risk assessment, and kill switch for reuse. In other words, it's an engine that automatically builds up a "dictionary of fraudulent patterns" by accumulating judgment results from both AI and human judges.

[0371] For more details, 1. For cases where counterfeiting or fraud has been "confirmed," that is, cases that have been identified as "high risk" by AI for authenticity verification / anomaly detection AI / delivery anomaly (analysis) AI, and which have been confirmed by human investigations (franchise headquarters, brand headquarters, police, insurance companies) to be "counterfeit goods," "internal delivery fraud," or "used for money laundering," 2. Regarding the set of features associated with the case, for example, engraving features (line width, depth, edge, placement, font), brand features (stitching pitch, logo ratio, internal structure), material features (XRF spectrum, specific gravity, magnetism, ultrasound), damage features (unnatural polishing, modification marks), delivery features (settling at specific locations + seal difference + weight difference + impact pattern), and human / financial features (AS-index, AML score, R-index, device confidence, location spoofing index), first, 1. Extraction and clustering of feature patterns, i.e., represented confirmed fraud cases as "feature vectors (embeddings)," and clustered together similar ones, for example, "group of counterfeit marking patterns for Rolex watches made by a certain workshop," "group of XRF+ specific gravity patterns for gold bars with shavings coming from a specific region," "group of patterns of packages being stolen from specific delivery hubs," etc. 2. Template creation, that is, extracting common features and thresholds for each cluster (e.g., marking pitch within this range + this material spectrum + this delivery pattern), and saving them as "malfunction templates". 3. Automatic updating of thresholds and weights: As new fraud cases increase, the template thresholds and weights (detection sensitivity) are automatically adjusted to reduce false negatives (missed cases) while also suppressing false positives (overdetections).

[0372] The generated fraud templates are fed back to the next module. Similarity feature search AI: "Does this new case resemble an existing fraud template?" Unknown counterfeit detection AI: When detecting anomalies, it determines "Is this pattern already included in the template?", issuing a strong warning if it's a known fraud, and holding it as a candidate for a new template if it's completely new and anomaly. Overall risk index Kill Switch: If it matches a specific template, the risk is increased dramatically. Rules such as "Kill Switch immediately if this pattern occurs" can also be implemented. Franchisee training AI: It identifies which patterns the store lacks resistance to, for example, by stating "This store has overlooked counterfeits that match the template several times."

[0373] Statistically speaking, a fraudulent pattern is defined as "a group of labeled samples among the outliers of a genuine cluster," so the foundation is a combination of outlier theory, clustering, and supervised learning.

[0374] Traditionally, 1. It is difficult for AI to systematize the "forgery patterns it has learned" afterward. In other words, even if it is determined on-site that "this case was forgery," it tends to be unclear to what extent that knowledge will be shared with other stores and used in future cases. 2. It is difficult to detect patterns at the counterfeiting group level; that is, counterfeit goods made by the same workshop or organization have commonalities in markings, materials, and distribution routes, but it is difficult for a human to identify them as a "series" even when examining each item individually. 3. The updating of detection rules becomes dependent on individual judgment, meaning that rules are added based on hearsay such as "It seems there are a lot of counterfeit items like this lately," which leads to significant omissions, errors, and inconsistencies between stores. Confirmed fraud cases are systematically templated based on AI + human review results, and the new templates are automatically shared with all member stores and all AI models, so that the "learned counterfeit patterns" are immediately reflected throughout the system. As a result, 1. The counterfeiting group and internal fraud patterns were identified as a chain of operations. 2. Detection rules will no longer be dependent on individual judgment and will be continuously updated in a data-driven manner. 3. The detection accuracy of each store and each engine is designed to increase monotonically over time.

[0375] Specific examples will be described later.

[0376] The system will be structured in such a way that patterns of forgery, internal fraud, and money laundering are continuously accumulated as "experience." A forgery pattern that has been detected once will never be overlooked again. The infrastructure will allow for the identification of forgery groups and internal fraud as "cliques" and their elimination at an early stage.

[0377] <25 Autonomous Recovery (DR)> Self-diagnostic and reconfiguration function (autonomous recovery engine) that autonomously recovers the system in the event of a system failure. If a failure, abnormal load, error, or functional degradation is detected in at least one of the following modules of the information processing device disclosed herein: AI inference module, material analysis module, delivery analysis module, anti-social matching module, or financial API integration module, The aforementioned information processing device for managing the security of commodity transactions may further include an autonomous recovery engine that autonomously performs at least one of the following to recover: cause determination, activation of an alternative module, reconfiguration, or cache restoration.

[0378] Automatic diagnosis and reconfiguration in the event of a failure → Cloud-native This is the operational infrastructure layer that supports safety and continuity, and it performs self-recovery, or autonomous recovery (DR), in the event of module failure. This is an autonomous recovery engine that, when a major module experiences a failure, abnormal load, or performance degradation, automatically performs actions such as alternative startup, configuration reconfiguration, and cache restoration based on the cause of the problem to recover. It's not just a backup system; it performs self-repair control with cause detection.

[0379] The present invention provides an autonomous recovery engine that, when a failure, abnormal load, error occurs, or performance degradation is detected in at least one of the following modules—AI inference module, material analysis module, delivery analysis module, anti-social matching module, and financial API integration module—autonomously performs at least one of the following actions to recover: cause determination, activation of an alternative module, configuration reconfiguration, and cache restoration. This is not just a backup; it's an automated disaster recovery and self-healing system that identifies what's broken, automatically selects and executes the appropriate recovery method.

[0380] The constituent elements are, 1. Detection of malfunctions, loads, and functional degradation. 2. Cause determination; 3. Selection of recovery method, 4. Autonomous execution It has a four-tiered structure.

[0381] For example, if the material analysis module is experiencing delays due to an abnormal load, instead of simply restarting it, the recovery method will be changed depending on the cause: reconfiguration if the load is the cause, starting an alternative module if it's a temporary error, or cache restoration if it's data corruption. In large-scale systems, failures are not assumed to "never" occur, but rather designed with the assumption that they will happen. Therefore, recovery is not reliant on manual processes, but rather automated, involving disconnection, replacement, relocation, and restoration. Traditionally, when a failure occurred, monitoring systems would notify the system, people would investigate the cause, make recovery decisions, and then manually restart the system. This process was slow and inconsistent. However, this new system will enable the autonomous recovery of key modules necessary for transaction security management.

[0382] Specific examples will be provided later.

[0383] The present invention is technically characterized by having an autonomous recovery engine that, when a failure, abnormal load, error, or functional degradation is detected in at least one of the following modules—AI inference module, material analysis module, delivery analysis module, anti-social matching module, or financial API integration module—autonomously performs at least one of the following actions to recover: cause determination, activation of an alternative module, configuration rearrangement, or cache restoration.

[0384] Because the commodity trading security management system is composed of multiple AI modules and multiple external integration modules, a failure or performance degradation in any one module can easily halt the entire security control or trading process. Traditionally, the operation involved humans checking monitoring alerts, isolating the cause, and restarting or switching to an alternative system, which resulted in problems such as delays in response, inconsistencies in judgment, and delayed recovery during nighttime or high-load periods. This invention solves this problem by enabling autonomous execution of this series of recovery processes.

[0385] The key difference lies not in simply restarting, but in selecting a recovery method based on the cause of the problem. Specifically, if an abnormal load is the cause, a configuration reconfiguration is performed; if a process failure occurs, an alternative module is started; and if cache corruption occurs, the cache is restored. This allows for the selection of a recovery method appropriate to the cause. This avoids situations where the system repeatedly restarts without recovery, or where even healthy modules are affected and shut down.

[0386] The benefits are, firstly, that it reduces the average downtime during major module failures and increases the availability of the antique goods trading security management system. Secondly, because self-repair is possible at the module level, it is possible to keep the entire system running while partially maintaining critical functions such as authenticity determination, anti-social group verification, financial linkage, and delivery analysis. Thirdly, autonomous control of cause determination, alternative startup, redeployment, and restoration reduces reliance on human intervention and standardizes the quality of fault response.

[0387] For example, if the material analysis module is delayed due to high load, recovery can be attempted by activating an alternative material analysis module while distributing and rearranging the processing queue. If the financial API integration module fails, depending on the result of the cause determination, the system can switch to an alternative integration system after cache restoration, ensuring the safe operation of at least deposit confirmation can continue. Furthermore, even if a temporary error occurs in the anti-social group verification module, input to the composite risk control unit can be maintained by activating an alternative verification module. Thus, the present invention has technical significance in realizing a self-healing security infrastructure that assumes module failures.

[0388] Furthermore, this disclosure can be viewed from the perspective of information processing methods, that is, the information processing methods of this disclosure A method for managing the security of commodity transactions, which includes determining the authenticity of the materials of specific goods, including precious metals, jewelry, or branded goods, that are the subject of trading, An XRF acquisition step to acquire spectral data of fluorescent X-rays irradiated onto the specified product, Extracted data including the peak position, peak intensity, peak width, and trace element distribution of each element is generated from the spectral data. Based on the extracted data, the content of precious metals including gold, platinum, palladium, or silver is estimated, A material analysis AI processing step that detects a hollow structure, foreign material inclusion, plating treatment, or use of counterfeit metal based on anomalies in the spectral pattern of the specified product, The present invention provides a method for processing information for managing the security of product transactions, which includes a material analysis information processing method, which includes a material analysis result, which is an estimation result obtained by the material analysis AI processing step, used for determining the authenticity of the specified product.

[0389] Furthermore, this disclosure can be viewed from the perspective of an information processing program, that is, the information processing program disclosed herein is an information processing program for managing the security of commodity transactions, which includes determining the authenticity of the materials of specific goods, including precious metals, jewelry, or branded goods that are the subject of trading. On the computer, An XRF acquisition step to acquire spectral data of fluorescent X-rays irradiated onto the specified product, Extracted data including the peak position, peak intensity, peak width, and trace element distribution of each element is generated from the spectral data. Based on the extracted data, the content of precious metals including gold, platinum, palladium, or silver is estimated, A material analysis AI processing step that detects a hollow structure, foreign material inclusion, plating treatment, or use of counterfeit metal based on anomalies in the spectral pattern of the specified product, The present invention provides an information processing program for managing the security of product transactions that executes a material analysis step, which is an estimation result obtained by the material analysis AI processing step, and a material authenticity determination step, which uses the material analysis result to determine the authenticity of the specified product.

[0390] <Other specific examples> Other specific embodiments of the above-mentioned invention are shown below in bullet points or similar format. Furthermore, when a number follows each "Example," the number, excluding the sub-number, corresponds to the number representing the invention described above (the number for each individual function).

[0391] Example 1-1 [Ordinary 18K Necklace] A regular user A brings their item to affiliated store M (a buyback store) and undergoes XRF material analysis using AI. 1. Person A brought in an 18K gold necklace, which was then measured using an XRF device. 2. The XRF acquisition unit acquires spectral data. 3. The AI ​​Department for Material Analysis We analyzed the peak position, intensity, and width of Au, Ag, and Cu. It is estimated to be approximately 75% Au, 12% Ag, and 13% Cu. 4. No unusual elemental peaks (such as W or Ni) were detected. Spectral shape is within the template range → No abnormalities.

[0392] effect M can calculate a reasonable material value as 18K based on the content, even if the markings have faded. A can be easily convinced by the explanation that "it was determined to be 18K as a result of XRF spectroscopy."

[0393] Example 1-2 [Counterfeit gold bars containing tungsten] A dishonest dealer X (who brought in gold bars with a markup) brought them to the buying center C. 1. C receives a gold bar that looks and is stamped as "1kg of pure gold". 2. When XRF measurements are performed, a sharp peak of W (tungsten) is detected in addition to the peak of Au. 3. The material analysis AI found the Au content to be abnormally low. The W peak does not exist in the template (pure gold) → It is determined to be "contains foreign material / use of counterfeit metal".

[0394] effect C can refuse the purchase at this point. The objective evidence that "the markings indicate pure gold, but the elemental composition of XRF is clearly abnormal" makes this explanation and evidence very strong.

[0395] Example 1-3 [Plated chain (18K gold-like surface only)] User B (no malicious intent, believes the cheap item is 18K gold). 1. B brings in a chain that he believes to be "18K." The chain is stamped with "18K." 2. Based on the results of the XRF measurement, Although an Au peak is visible from the surface, when the measurement conditions are slightly changed, strong Cu and Ni peaks emerge from within. 3. The material analysis AI indicates that the Au content is low overall. The spectral pattern is similar to that of a "plating template". This was the determination.

[0396] effect To B, the explanation given is, "The markings say 18K, but the XRF results indicate that 'only the surface is gold, the inside is made of a different material.'" This also prevents the store from mistakenly paying a high price for it as 18K.

[0397] Examples 1-4 [Chains containing hollow structures] (While hollowness detection typically uses specific gravity and ultrasound, signs can also be detected through "abnormalities in elemental ratios and spectral shape.") 1. When measuring the surface with XRF, the "Au ratio is reasonably high," 2. The density calculated from weight and volume does not match → "It should be heavier given its shape," therefore. Furthermore, when specific gravity, magnetic field, and ultrasonic measurements are performed in conjunction, as described later, XRF: There is an Au peak. Specific gravity: Clearly lighter than pure gold Ultrasound: internal cavity reflection

[0398] → It can be determined that it has a "hollow structure + thin gold plating".

[0399] Example 2-1 [Hollow Ingot (with a hollow center)] At Buyback Center C, situation Appearance: 1kg pure gold bar XRF: 99% Au (Surface is almost pure gold) 1. Specific gravity measurement Estimate the volume V from the external shape, and measure the actual weight m → ρ = m / V Result: ρ = 17.4 (should be around 19.3) → An anomaly that is too mild. 2. Magnetic Testing Gold is nonmagnetic, therefore

[0400]

number

[0401] effect Based on appearance and XRF alone, even a gold bar that "looks like pure gold" Composite material analysis immediately identifies the presence of intermediaries, allowing for rejection of the assessment.

[0402] Example 2-2 [Tungsten-filled ingot (W core + Au cover)] situation Malicious gold bars: Exterior: Pure gold cover Contents: High-density tungsten 1. XRF: Measuring only the surface reveals composition as Au 91% + Cu 9%, which at first glance appears equivalent to K22. 2. Specific gravity: Tungsten has almost the same density as gold, The difference isn't as clear as with hollow cases (it's a subtle discrepancy). 3. Magnetism: Au is diamagnetic, W is weakly magnetic / paramagnetic → M is slightly high 4. Ultrasound: Reflection at the interface of the inner layer → Typical "two-layer structure" waveform U rises. AI judgment While XRF alone appears normal, abnormalities in M ​​and U cause a decrease in Smat, leading to a strong warning of "suspected contamination with foreign materials."

[0403] effect The deception that relies solely on XRF can be exposed by the contradiction in the physical laws of magnetism and ultrasound.

[0404] Example 2-3 [Surface-plated accessories] situation Appearance: A necklace engraved with "18K". Actual: Brass with a thin gold plating 1. XRF: There is an Au signal when measured from the surface, Changing the spectral depth conditions strengthens the Cu and Zn peaks. 2. Specific gravity: A ρ of around 8 to 9 indicates that it is clearly light for gold or 18K. 3. Magnetism: Depending on the alloy composition, the M value may be slightly prominent. 4. Ultrasound: The material is thin and the interior is soft, which is why the reflected waveform also shows abnormalities. → The composite materials analysis department clearly treated it as a fraud, citing "suspected plating."

[0405] effect It allows us to logically explain to users not only the markings and colors, but also the "authenticity of the material, including its contents."

[0406] Example 3-1 [A watch that is "as close to the real thing" as possible] Image analysis AI: Engravings, scratches, appearance → Score 0.95 Brand Authenticity AI: Stitching (bracelet), logo / internal structure → Score 0.97 Material Analysis AI: XRF, Specific Gravity, Magnetism, Ultrasound → Score 0.98 Fusion movements Weighted integration of the three scores → Overall Authenticity Index ≈ 0.97

[0407] effect As it is "almost certainly genuine," the appraisal value is high, the collateral value is highly rated, and insurance can be obtained under standard conditions.

[0408] Example 3-2 [A watch with genuine materials but a replaced case] situation The movement inside is genuine. The case and bracelet are aftermarket parts. Input score Image analysis AI: Appearance is slightly different from normal → 0.6 Brand Authenticity AI: Internal structure (movement) is OK, but the exterior structure does not match → 0.7 Material Analysis AI: The 18K ratio is correct → 0.95 Fusion results When combined, the overall authenticity index is approximately 0.75, placing it in the "gray: caution advised" range. interpretation This allows us to express, with a single numerical value, a situation where a material has value but "cannot be considered completely authentic as a branded product."

[0409] effect Buyback shops limit the appraisal price based on the value of the materials. They can explain that "it's not covered by the brand warranty because it's an aftermarket case."

[0410] Example 3-3 [Bag with only the engraving replaced with an original part] situation The engraved parts were removed from the genuine bag. In some cases, these items are being transplanted into counterfeit bags. Score Image analysis AI: Looking only at the engraved area, it's 0.95, and the scratches look natural → Over 0.9 Brand Authenticity AI: Discrepancies in stitching pitch, logo placement, and internal structure from the template → 0.4 to 0.5 Material analysis AI: Differences in canvas and leather texture and specific gravity → 0.5 Fusion A combined authenticity index of approximately 0.5 indicates a strong suspicion of it being a fake.

[0411] effect Even with sophisticated counterfeits where only the engraving is genuine, Fusion can detect inconsistencies with other elements and, overall, determine that it is "risky."

[0412] Example 4-1 [Deep scratches on a gold necklace (K18)] situation 18k gold necklace weighing 50g There is one deep scratch near the clasp, but the weight remains unchanged. 1. Damage Value Correction AI: Detects deep scratches (depth d high) The scope is localized (small scope). 2. Material analysis AI: The correct elemental ratios and specific gravity for 18K. 3. Overall Authenticity Index: Score 0.98 (Genuine metal). 4. AI for Damage Value Correction: Judging that the intended use is "closer to scrap metal", Prioritizing "material value" over appearance → Price reduction rate is small (a few percent).

[0413] effect It's not a case of "a significant price reduction because it looks bad," but rather a reasonable reduction based on the assumption that the metal will be melted down.

[0414] Example 4-2 [Minor scratches on the dial of a rare Rolex watch] situation Rare collector's item (discontinued / limited edition) There are small scratches in a noticeable location on the dial. AI behavior 1. Damage Value Correction AI: The scratch is small, but its location was determined to be near the center of the dial, where visibility is high. 2. Material analysis AI: The materials are genuine stainless steel and 18K gold → No problem. 3. Overall Authenticity Index: Score 0.99 (genuine). 4. AI for Damage Value Correction: Due to its high authenticity score + rarity as a brand, It was determined to be an item with "collector's value." Scratches on the dial significantly affect its value, → Set a larger reduction rate (e.g., -20%).

[0415] effect We do not treat "scratches on the base metal" and "scratches on a rare watch" the same way. This allows for value adjustments that are consistent with practical considerations (the actual situation in the collector market).

[0416] Example 4-3 [Corner wear and resale of branded bags] situation Popular brand bags There are scuffs on two corners of the bottom due to use. Resale (second sale). AI behavior 1. The damage value correction AI detects corner scratches (location = bottom corner, depth = shallow to medium). 2. Material Analysis AI: Leather material is genuine. 3. Overall authenticity index: 0.97. 4. Comparison with digital twin information (from the previous transaction): "There are more scratches than last time" = Natural wear and tear due to aging. 5. AI for Damage Value Correction: We will only evaluate the "additional wear since the last time," limiting the reduction to just a few percent.

[0417] effect Instead of subjectively deducting the value from zero each time it's resold, the deduction will be rational, based on the amount of damage compared to past data. Buyers will also be informed of how much damage has been added since the last time, allowing them to trust the temporal continuity of value.

[0418] Example 5-1 [The item is genuine, but the person / delivery poses a risk → Stopped] Authenticity Index A: 0.98 (Almost genuine) AS-index: 0.8 (Anti-social DB, PEP hit) AML score: 0.75 (Frequent high-value short-term transactions) R-index: 0.7 (Nighttime concentration, abnormal movement pattern) Delivery Anomaly Index D: 0.6 (Route deviation + Weight -6g) R total teeth, 1 - A ≈ 0.02 It's small, but Because AS·AML·R·D is high, R total Approximately 0.7 or higher (threshold greater than 0.6) result Trading ban (KillSwitch) AML / police report if necessary "The item is perfectly authentic, but this transaction is risky," they determined.

[0419] → Previously, there were cases where people bought something because it was "authentic," It can be stopped due to the overall risk.

[0420] Example 5-2 [The item is suspicious, but the person and delivery are normal → Switch to a different route] Authenticity Index A: 0.6 (Suspicion of forgery) AS-index: 0.1 (Almost no suspicion of being involved with anti-social groups) AML score: 0.2 R-index: 0.1 D:0.1 (No delivery issues) R total teeth, 1-A = 0.4 is slightly large, but the others are low. R total ≈ 0.3 to 0.4 control "The overall risk isn't high, but the product seems suspicious." Additional authenticity check Inquiry to the brand's headquarters This leads to a "reconfirming items" process.

[0421] → Since there is no suspicion regarding the people involved or the delivery, financial and AML regulations will not be as stringent, and the focus can be on additional inspections to verify authenticity.

[0422] Example 5-3 [All low-risk → Smooth approval] A: 0.97 AS:0.1 AML:0.1 R:0.1 D:0.1

[0423] result R total ≈ 0.1 to 0.2 → Threshold less than 0.6 Transactions are approved instantly, and price locks, insurance, and resale functions operate as usual. → Transactions for typical users proceed with almost no friction, It strikes a balance between safety and user experience.

[0424] Example 6-1 [A high-risk case where complete shutdown is not necessary] Total Risk Score = 0.65 (above the threshold of 0.6, but below the Kill Switch threshold of 0.8). Typical pattern: AS-index: Low (No suspicion of ties to organized crime) AML: Medium to High (Deposit and withdrawal patterns are slightly suspicious) R-index: Medium (concentrated late at night) A (Authenticity Index): High (Genuine) D (Shipping): Low to medium (normal range) control I won't go as far as a complete shutdown (Kill Switch), but: (1) Prohibition of postal transactions (2) Limitation to over-the-counter transactions (3) Additional identity verification request Set to apply automatically.

[0425] effect High-risk users will be required to "visit the store in person and complete additional KYC (Know Your Customer) procedures," which will increase the deterrent effect against money laundering and identity theft.

[0426] Example 6-2 [A case where only the delivery risk increased sharply] situation Product: Genuine high-end watch (A-grade). User: AS·AML·R is low to medium. However, during delivery, the route deviated significantly, resulting in a weight difference of 5g, and there is suspicion that the sealing tag was reattached. → D (Delivery Anomaly Index) has surged, and the Total Risk Score has also exceeded the threshold. control The user receives a notification that "Enhanced Delivery Mode is in effect." The system automatically applies enhanced delivery mode, and packages are routed to an audit route. During inspection, double weighing and camera recording are performed, and if necessary, deposits to the bank are temporarily suspended.

[0427] effect In cases where "the user and the goods are safe, but only the delivery is questionable," restrictions can be placed specifically on the delivery aspect.

[0428] Example 6-3 [Ultra-high risk, almost at the level of a Kill Switch] situation High AS index, high AML, high R, high D → Total Risk Score ≧ 0.8. example: AS: Multiple hits on anti-social group sanctions list AML: High-value smeefing + international money transfers R: High frequency late at night D: Sealing abnormality + weight difference control As soon as the Kill Switch is activated, all restrictions (1) through (5) are "naturally" met, resulting in a complete shutdown.

[0429] effect A final line of defense, which "stops all transactions, deliveries, and fund transfers," is automatically activated in conjunction with the Total Risk Score.

[0430] Example 7-1 [Ordinary User (Pattern with High Positional Confidence)] User A (resident of Tokyo), using the app on a smartphone situation GPS: Indicates location within Shinjuku Ward. WiFi: Matches the group of access points around Shinjuku Station. IP: Mobile network IP address within Tokyo. Movement in the last 10 minutes: Within a few hundred meters on foot from the station. Evaluation of the multiple position matching unit Matching level: High (All three are in the Tokyo / Shinjuku area) Time consistency: Good (travel distance and time are reasonable) Movement trajectory: Along the road, in nature → Location confidence score ≈ 0.95 to 0.99

[0431] result The nearby merchant notification function works without any problems. No "location inconsistency" is recorded on the R-index side, and it is treated as normal behavior.

[0432] Example 7-2 [Case where you are in Japan while using a foreign IP address via VPN] User B (resident of Japan) uses an overseas VPN service. situation GPS: Shibuya Ward, Tokyo WiFi: Access point at a cafe in Shibuya IP: Location information of the US (VPN server) analysis GPS vs WiFi: Match (Tokyo) IP: USA → Major discrepancy Recent travel route: Nature Location confidence The device's "physical location" is highly reliable due to GPS + WiFi, Inconsistency with IP location → Slight reduction in confidence score (e.g., from 0.7 to 0.8).

[0433] Handling This is a case where the issue is judged as "communication path spoofing" rather than location spoofing (GPS spoofing). When combined with subsequent R-index and AML scores, simply using a VPN will not result in immediate restrictions. If other risks are high, flexible control is possible, such as "additional KYC" or "in-store only."

[0434] Example 7-3 [Location spoofing using a GPS spoofing app] (An example that also works in conjunction with R-index) User C (attempting to impersonate someone) situation GPS: Spoofing to point to a high-risk country abroad (Spoofing App). WiFi: I can see the access point near my home in Japan. IP: A fixed-line internet connection within Japan. In the last 5 minutes, the GPS showed the location fluctuating between "Tokyo → Seoul → Tokyo". Evaluation of the multiple position matching unit GPS vs. WiFi vs. IP: A contradiction at the national level. The travel speed is also physically impossible, as it's described as "round trip to the border in a few minutes." → Location confidence score ≈ 0.2 or less.

[0435] effect A low score in "Current Location Trustworthiness" indicates that the user is at high risk, linked to the Location Spoofing Index, Device Trustworthiness, and R-index. This can result in restrictions such as being banned from mail delivery, limited to in-store purchases, and additional KYC requirements. Kill Switch can be activated if necessary.

[0436] Example 8-1 [Honest User (Low Location Spoofing Index)] User A (using the app at home) situation GPS: Near home WiFi: Home router IP: Home internet connection Sensor: Mostly stationary, occasionally being held in the hand. Judgment The relationship between position and sensors is natural. → Location spoofing index ≈ 0.05 R-index and Device Trust Score are normal → No restrictions.

[0437] effect Normal users can easily send items for appraisal and purchase via mail without having to think about anything.

[0438] Example 8-2 [Using a VPN (only the communication is overseas, but the terminal is in Japan)] User B (connecting from Japan via overseas VPN) situation GPS / WiFi: Tokyo IP: USA (VPN server) Sensor: Natural stationary and moving Judgment Location (GPS / WiFi) and sensors are synchronized. Only the IP address is different → Presumed to be a spoofing of the communication path. Location spoofing index is mild (e.g., 0.3 to 0.4)

[0439] Handling This alone won't trigger a kill switch, but it allows for a gray area approach, such as requiring additional verification for high-value transactions and imposing some weight on the AML (Anti-Monetary Policy) side.

[0440] Example 8-3 [Pretending to be abroad using a GPS spoofing app] User C (an errand boy for organized crime) falsified his location to appear overseas while in Japan. situation GPS: Set to point to a city in high-risk country H. WiFi: Cafe access point in Tokyo IP: Japanese mobile network Sensor: Nearly stationary on the desk Judgment Inconsistencies between locations (GPS vs WiFi vs IP) → Low location reliability. The sensor is stationary, but the GPS is showing an overseas location → This is unnatural from a motion equation perspective. The location spoofing index is high (0.8 to 0.9).

[0441] effect By passing the index to the Device Trust Score, Total Risk Score, and Kill Switch, security controls are activated that automatically prohibit mail-order transactions, limit transactions to in-store only, or immediately halt transactions from devices that are spoofing their location.

[0442] Example 8-4 [Linking with the delivery person's smartphone location log] The delivery driver's smartphone, while showing the truck shaking and moving according to the sensors, suddenly warps its GPS location to a different town.

[0443] → This is detected as location falsification or equipment malfunction on the delivery side. This allows for structural detection of "fraud and impersonation during delivery" by linking with delivery anomaly (analysis) AI and automated insurance claim AI.

[0444] Example 9-1 [Secure device with the latest OS and applied patches] User A (latest iOS device, no jailbreak) situation OS: Latest version Security patch: Latest Root / JB: None Malicious apps: None Location spoofing index: almost 0 Past fraud history: None Device Trust Score If each element is good → Score ≈ 0.95 to 0.99

[0445] effect Transactions in category A are largely unaffected by terminal-related restrictions. Similarly, R-index and AS-index offer virtually no terminal-related bonus points.

[0446] Example 9-2 [Rooted device + numerous suspicious apps] User B (rooted their Android device and installed multiple location spoofing apps) situation OS: Older Patch: Not updated for a long time Root:YES Malicious apps: multiple Location spoofing index: 0.7 Past fraudulent activity: Suspicious access history from the same device. Device Trust Score Root access + malicious apps + high location spoofing index → ​​Score ≈ 0.2 to 0.3

[0447] effect High-value transactions and mail-order transactions from this device are automatically prohibited / limited to in-store transactions, additional KYC required, and may be subject to Kill Switch → Risky activities will be difficult to perform unless the device is replaced.

[0448] Example 9-3 [Frequent VPN use, but the terminal itself is healthy] User C (a regular user with high security awareness who frequently uses a VPN) situation OS:Latest Patch: Latest Root: None Malicious apps: None Location spoofing index: Moderate (IP location and GPS location sometimes differ) Fraudulent activity history: None Score No root access required, apps are healthy → High rating Location spoofing index due to heavy use of VPNs: moderate → slight deduction → Device Trust Score ≈ 0.7 to 0.8

[0449] Handling Because it's not a low score Transactions of normal amounts are permitted. However, in extremely high-value transactions or transactions with high-risk countries, Additional KYC (Know Your Customer) verification may be required. → Instead of immediately dismissing a VPN, a more realistic evaluation is possible by considering other factors.

[0450] Example 10-1 [Avoid delivery companies with a history of frequent package losses] Platform operating company P Delivery company A (frequent lost packages) Delivery company B (stable track record) data A: Loss rate: 0.3% Damage rate: 0.5% Latency rate: 2% B: Loss rate: 0.02% Damage rate: 0.1% Latency rate: 0.5% Output from the delivery risk assessment unit Risk index for delivery company A: 0.7 Risk index for delivery company B: 0.2

[0451] effect For high-value gold bullion and brand-name watches, option B should be prioritized as a general rule. If option A must be used, "enhanced shipping mode," high-value insurance, and double sealing must be required.

[0452] Example 10-2 [Risk assessment of routes passing through areas with high crime rates] User C (sending from a regional city to a buyback center in Tokyo) Delivery company D situation Standard route for D: The route includes highways and urban areas, and the safety index is moderate. Alternative route: Driving for extended periods in areas with a high incidence of theft and car break-ins. Delivery route risk index Standard route: 0.3 Alternative route: 0.8

[0453] effect The delivery risk assessment AI selects the standard route whenever possible. If only alternative routes are available, a "Comprehensive Delivery Risk Index," which is calculated by multiplying the risk index for each delivery company by the alternative route, is used and reflected in the Total Risk Score / Delivery Enhancement Mode.

[0454] Example 10-3 [Risk control combined with IoT sensors] situation Delivery company E: The loss rate is low, The route passes through many mountainous areas and other locations with a high risk of natural disasters. correspondence AI for shipping risk assessment: Corporate risk index: 0.3 (Low accident rate) Pathway risk index: 0.7 (High risk of natural disasters) Delivery abnormality (analysis) AI: We place particular emphasis on temperature, humidity, and impact logs from IoT sensors to monitor for avalanches, rockfalls, and anomalies during prolonged periods of stationary use.

[0455] effect Due to the high risk associated with the route, the system is operated in "enhanced delivery mode" from the start (insulation materials, temperature and humidity sensors, and high-cost insurance). This allows for a reduction in the actual accident rate through AI control, even on high-risk routes.

[0456] Example 11-1 [The product is genuine, but the transaction is completely "shady"] situation Authenticity Index A: 0.98 (Genuine) AS-index: 0.9 (match in anti-social database + PEP + FATF high-risk country) AML score: 0.85 (Frequent short-term, high-value international money transfers) Delivery anomaly index: 0.8 (route deviation + weight ▲6g) Location spoofing index: 0.7 (GPS spoofing) Device Trust Score: 0.2 (Rooted device + Malicious app) Kill Wire determination AS-index: Danger threshold exceeded AML score: Exceeds the danger threshold Delivery Anomaly Index: Exceeds Danger Threshold Location spoofing index / DeviceTrust is also at a dangerous level. → At least 3 out of 5 are dangerous → Kill Switch activated.

[0457] result The transaction in question will be canceled immediately. Bank transfers and withdrawals are also blocked. Delivery has stopped, and the process has shifted to reporting to the police and banks. → The shift is from "I'll buy it because it's authentic" to "I'll only buy it once total safety is guaranteed."

[0458] Example 11-2 [A case in which internal fraud in delivery is strongly suspected] situation AS-index: 0.1 (No suspicion of being involved with anti-social groups) AML score: 0.2 (Low financial risk) Delivery abnormality index: 0.9 (Weight -6g + seal broken + significant deviation from delivery route) Location spoofing index: 0.8 (The delivery person's terminal location jumps unnaturally) Device Trust Score (Delivery Terminal): 0.3 (Suspected dangerous app / log tampering) Kill Green The person (seller) is mostly clean, Process (delivery) + location spoofing + device malfunction → 3 or more risks. → Kill Switch will: halt processing of the relevant delivery, automatically notify the insurance company and delivery company's audit department, and only display "Under Investigation" to the user (no assessment or payment will be made).

[0459] effect This allows us to pinpoint and stop fraudulent activity within the delivery process without raising unnecessary suspicions about the seller.

[0460] Example 11-3 [Terminal-based fraud (location spoofing + modified terminal)] situation AS-index: Medium (0.5) AML: Medium (0.5) Delivery error: None yet (0.1) Location spoofing index: 0.85 (Highly likely to be GPS spoofing) Device Trust Score: 0.2 (Rooted / Jailbroken + Numerous Malicious Apps) Kill Wire determination Location spoofing index: Exceeds danger threshold Device Trust: Risky (1 - Score of 0.8) Even if other scores are moderate, a device may still meet the Kill Switch criteria as a device with a high risk of future fraud.

[0461] result Transactions are suspended at this moment, accounts and devices require "re-registration" and "additional KYC," and high-value transactions and mailings are prohibited unless the device is replaced. → This allows for the physical blocking of fraudulent routes starting with "risky devices + location spoofing" at an early stage.

[0462] Example 12-1 [Anti-social API malfunction] Using alternative anti-social APIs or recently verified caches, high-value transactions will be put on hold, while low-value transactions will proceed with additional verification.

[0463] Example 12-2 [Delivery API Failure] Even if the latest delivery tracking information is unavailable, the system will switch to enhanced delivery mode based on the previous location cache and local risk estimation.

[0464] Example 12-3 [Insurance API Failure] Even if immediate billing is suspended, the collection of incident evidence and internal reception will continue.

[0465] Example 13-1 [Detection of a novel product that is "identical" to a known counterfeit pattern] Affiliated store A receives a request from a user to appraise a branded bag. flow 1. Brand authenticity AI, image analysis AI, and material analysis AI extract features → vectorize them. 2. The similarity feature search AI searches the vector database. 3. A group of bags that have been previously confirmed to be "counterfeit," The vector distance is very small (almost the same cluster). System movement Automatically flagged as a "candidate with a high similarity to known forgery patterns". Combined with Fusion Authenticity Index and Unknown Counterfeit Detection, → Treat this as a strong suspicion of forgery.

[0466] effect It can be stopped immediately by reusing "past forged knowledge" without relying on human intuition.

[0467] Example 13-2 [Discovery of a "series" of counterfeit groups distributing goods by the same organization] Platform operators, police, and brand companies situation Similar counterfeit watches are sporadically appearing from various affiliated stores. The role of AI for similarity feature search 1. Plot the feature vectors of each counterfeit item in a vector space. 2. By repeatedly searching for similar products, A group of counterfeit goods belonging to the same vector cluster becomes visible. → By overlaying this with address, time, and delivery route, it becomes possible to extract patterns in the criminal organization's behavior, such as "this counterfeit group mainly operates from the XX region" or "they frequently use the same transportation routes and delivery companies."

[0468] effect This goes beyond simply combating individual counterfeit goods; it can lead to the detection and deterrence of organized crime at the level of organized crime.

[0469] Example 13-3 [Improving the consistency of genuine product assessment and recommendation integration] Merchant B, General User D (Want to sell genuine products) flow 1. Create the feature vector for clock D. 2. Using the similarity feature search AI, The top N "very similar genuine products" that have been previously assessed are retrieved.

[0470] How to use By referring to the authenticity index, degree of damage, and appraisal value of similar past cases, it becomes easier to ensure consistency in appraisals, such as knowing that "for items in similar condition and models, the price will generally fall within this range." Furthermore, by integrating with a transaction recommendation AI, it becomes possible to recommend "buyout, consignment sale, or auction" based on the market price of similar products and the behavior of similar users.

[0471] Example 14-1 [Hyperspectral Forgery (Unexperienced Coating Technology)] situation XRF, specific gravity, magnetism, ultrasound, engraving, and brand structure appear normal at first glance. However, when viewed with future hyperspectral cameras, The surface reflection spectrum is clearly different from that of a genuine cluster. The behavior of the AI ​​for detecting forgery anomalies 1. Hyperspectral feature vectors are also incorporated as "material features." 2. Compare with the hyperspectral distribution of the original work → Determined to be a singularity. 3. Even if there are no anomalies in other dimensions, The counterfeit anomaly index is increased if the "degree of deviation from material characteristics is high."

[0472] result If the item is treated as an "unknown counterfeit suspected" in the Fusion Authenticity Index or Total Risk, measures such as re-examination, inquiry to the brand's headquarters, and temporary suspension will be automatically applied.

[0473] Example 14-2 ["Subtly Different Engravings" by a New Counterfeiting Workshop] situation The markings look almost identical to the real thing, Edge fluctuations Subtle patterns of line width However, it's different from the genuine works cluster. AI behavior 1. The marking characteristics (depth, line width, fluctuation) are calculated as z-score and anomaly index. 2. If that z-score is an "extreme outlier" such as +4σ, → Set the forgery anomaly index high.

[0474] effect Even forged markings from a workshop I've never seen before can be detected from the very first time as a statistical anomaly in the markings.

[0475] Example 14-3 [Detection of a "new modus operandi" from a case where only the delivery route was abnormal] situation The item itself is genuine, and the materials are also authentic. However, a pattern has emerged where packages originating from certain areas consistently follow unnaturally roundabout routes and have unusually long layovers. The role of AI in detecting forgery and anomalies 1. As a “delivery feature,” route Stopping time Delivery company risks Treat it as a multidimensional feature, 2. Compared with other genuine delivery data Abnormal correlation patterns (unusually long in specific regions / a high number of shock logs from specific sales offices) Detected.

[0476] result Initially, it might be detected not as "counterfeit goods" but as a sign of "internal fraud in delivery," but later it could be templated as a new method of "content swapping during delivery" and passed on to fraud template learning.

[0477] Example 15-1 [Detection of "snatching" at relay points] situation User A ships a gold necklace (100g). It weighed 94g upon arrival. The sealing tag AI also indicated "suspected re-attachment." Delivery Anomaly (Analysis) AI's Movement 1. Seal tag matching score T: Low value (large difference in seal image). 2. Weight difference W: |100 - 94| / 100 = 0.06 → W = 0.94. This may seem small at first glance, but a 6% decrease is significant for high-priced items → set the weight θ2 higher. 3. Pathway abnormalities P: The time spent at relay point X is unusually long. The route from that point onward involves an unnatural detour → P is high. 4. D = θ1(1―T)+θ2(1―W)+θ3P exceeds the threshold. The delivery anomaly index D has surged → Suspicion of internal delivery fraud.

[0478] result The transaction control unit temporarily suspends payment and product confirmation to the user, requests investigations from the delivery company and insurance company, and the automated insurance claim AI generates data for the insurance company by combining the data, seal image, weight log, and route log.

[0479] Example 15-2 [Dropping / damage accident during delivery] situation Luxury watches delivered by mail. Upon arrival, the exterior had a large dent and the glass was broken. AI behavior 1. IoT shock sensor log: A large impact value (Δs large) was detected at a certain point on the highway. 2. Route and stop log: The vehicle comes to a sudden stop at that point (the pattern is Δt / Δd). 3. The seal tag match rate T is high (the seal has not been broken). 4. The weight difference W is also almost 1 (the weight remains the same). 5. P is large and D is also high → Determined as "damage due to dropping or impact during delivery".

[0480] result Instead of being treated as a substitution, it is classified as a "transportation accident," and the automated insurance claim AI generates data including the cause, location, time, and impact value of the accident. This makes it easier for users to be covered as an "accident during delivery."

[0481] Example 15-3 [A case where delivery was not affected, but "the user made a false declaration"] situation User B declared the weight as "100g" and shipped the item. Weight upon arrival: 100g. The sealing tag AI, route, and impact logs are all normal (T, W, and P are all normal). AI conclusion D is almost 0 (no delivery issues). Even if B later claims that "the necklace was damaged / lighter," D. Based on weight logs, images, and IoT logs, abnormalities in the delivery process are ruled out.

[0482] effect Since D can also be used to prove that the delivery process is "clean," delivery companies, platforms, and insurance companies can more easily make a fair judgment about "how much should be covered."

[0483] Example 16-1 [In case of delivery accident (dropping / damage)] situation Luxury watches delivered by mail. Upon arrival, the case had a large dent and the glass was cracked. The seal and weight were OK, but IoT sensor logs detected a significant impact at a point on the highway. movement 1. Delivery Anomaly Index D: High value due to impact / path pattern. 2. Damage analysis results: Glass breakage and case dents were detected. 3. Overall Authenticity Index: A ≈ 0.98 (Genuine). 4. The AI ​​for automated insurance claims is, Cause classification: "Dropping / impact during delivery" Time of occurrence: The time when the maximum impact occurred according to the sensor log. Location of occurrence: GPS location at that time Evidence data: Before / After images Time series of IoT sensors Delivery route log Product Information: Authenticity Index, Appraisal Value, Material Information These are then compiled into an insurance API format to generate insurance claim data.

[0484] effect Users and participating businesses only need to report that an "accident has occurred," and the insurance claim form is automatically generated by the system. Insurance companies can more easily determine a "delivery accident" based on AI logs (impact, weight, route) rather than human subjectivity.

[0485] Example 16-2 [Suspected substitution → Assistance in determining whether the item is not covered by insurance] situation User B ships a gold necklace (100g). Upon arrival, the package weighed 94g, and there is suspicion that the sealing tag (AI) had been reattached → High delivery abnormality index (D). However, the delivery route and impact logs show no unusual stops or impacts, and the logistics side finds no evidence of theft. AI's judgment Damage analysis results: There were few scratches upon arrival. Sealing tag detection results: Compared to the pre-shipment image, there was already something unusual at the time of shipping. → The automated insurance claim AI flagged the possibility of fraud occurring before shipment, rather than an accident during the delivery process, and requested the insurance company to perform a causal classification from an AI perspective and log the evidence. Create the billing data with the following information.

[0486] effect Insurance companies can calmly determine the scope of coverage based on data. They can prevent fraudulent claims while processing legitimate claims quickly.

[0487] Example 16-3 [Clarification of the rationale for excluding counterfeit goods from insurance coverage] situation An insurance claim was filed for a high-end designer bag, claiming it was damaged during shipping. Authenticity Index A: 0.35 (Highly likely to be counterfeit). movement 1. The AI ​​for automated insurance claims is: A low overall authenticity index was detected. 2. The delivery anomaly index D is low, so deliveries appear to be fine. 3. Damage analysis results are available, The AI ​​determined that the problem is most likely due to counterfeit materials and stitching.

[0488] → In insurance claim data, this will be organized as "AI-determined as suspected counterfeit" and "no abnormalities detected during the delivery process," giving insurance companies a clear basis for excluding the item from coverage.

[0489] Example 17-1 [Affiliated store that excels at authenticity verification but always offers excessively low prices] situation Store X: Authenticity / Counterfeit Accuracy: High (Almost the same judgment as AI) Counterfeit detection history: Good (It is good at detecting fakes). However, the price discrepancy rate between the AI-recommended price and the price compared to other stores is consistently between -10% and -20% (tendency towards lower buyback prices). AI-powered feedback for franchisee training The breakdown of the assessment quality indicators is as follows: "Authenticity accuracy: ◎" "Price Appropriateness: △" The dashboard displays the message, "Prices are consistently lower than market rates. We recommend reviewing prices for some brands and categories."

[0490] effect The headquarters can prevent unfairly low purchase prices and the accumulation of customer dissatisfaction by reviewing store X's pricing policies and incentives.

[0491] Example 17-2 [Affiliate store with many complaints and frequent oversight of counterfeit goods] situation Shop Y: Complaint rate: High (more than twice that of other stores) Counterfeit detection history: Low (Many cases are discovered to be counterfeit later) Authenticity / Counterfeiting Accuracy Rate: There are many discrepancies with the AI. AI's judgment Assessment quality index: Low score. It also works in conjunction with the internal control engine, flagging the possibility of "internal fraud or lack of skills." Feedback example "Compared to the average of other stores over the past three months, • High rate of overlooking counterfeit products • High complaint rate → We recommend taking additional training in authenticity verification and implementing a double-check system for the appraisal process.

[0492] effect The headquarters can focus its audits on store Y while also providing training tailored to the specific weaknesses identified by the AI ​​(such as brand X and category Y).

[0493] Example 17-3 [Implementing the know-how of a top-performing franchisee throughout the organization] situation Store Z: It scored highly overall in areas such as authenticity matching rate, price fairness, complaint rate, and processing speed. Utilization of AI For member stores whose appraisal quality indicators exceed a certain level: The store is granted "Expert Store / Expert Assessor" status, and its assessment procedures, comments, and points to note are shared with other franchisees as training content.

[0494] effect The "tacit knowledge of top-performing stores" is visualized through AI as concrete checklists and guides. The overall assessment level is standardized and raised, and as a result, the health of the market improves.

[0495] Example 18-1 [Recommendation for safety-conscious users] situation User A: Elderly, first-time user, lives in a rural area. "I want an option that is as safe as possible and has minimal problems." AI input Product: A ring with a high authenticity index and high material value. User attributes: Safety-focused Merchant score: Store X: Close by, high appraisal quality, few complaints. Shop Y: It's a bit far, but the prices are a little on the high side. Shipping risk: Shipping from rural areas to urban areas carries a moderate risk.

[0496] Recommendation "We recommend that Mr. / Ms. A bring their device to store X, which is a 15-minute drive from their home. Based on safety, the number of reported problems, and past reviews, it offers the best balance of price and peace of mind." → While Y is slightly more expensive in terms of price alone, X is recommended to match A's safety-first approach.

[0497] Example 18-2 [Recommendations for young users, price-conscious users] situation User B: People in their 20s who are comfortable with online interactions. "I want to sell it for even one yen more." AI input Product: Popular brand bag (in good condition) AI-driven market trends: Big difference between in-store purchases and auctions. Delivery risk: Region B → Mailing from the center is low to medium. Merchant score: Auction Center C: Has a commission fee, but has a high average winning bid. Storefront D: Cash payment available, but slightly lower than the market rate.

[0498] Recommendation "In Mr. B's case, it is predicted that selling this bag at auction with a minimum bid of AB yen will be the most profitable option, rather than selling it immediately. The shipping risk can be covered under these conditions (sealed and insured shipping recommended)." → We recommend a price-first channel while still meeting safety constraints.

[0499] Example 19-1 [Counterfeit product detection rate and delivery accident rate increased in Region A] → The market health index declined, leading to temporary restrictions on postal transactions in that region.

[0500] Example 19-2 [Region B, which has a high rate of legitimate transactions and a low rate of appraisal discrepancies] → The market health index is high, which can be used to decide on easing trading restrictions or expanding the number of member stores.

[0501] Example 20-1 [Elderly person × Beginner × Small smartphone] Beginner user E, in his 70s Device: Small smartphone (small screen size, medium performance) Adaptive UI behavior 1. At the time of initial registration, the user registered / estimated "Age group = Elderly" and "Usage experience = First time". 2. Obtain screen size from device information → Small screen. 3. Looking at the operation history, users tend to spend a long time on explanation pages and hesitate before using the back button. Automatic UI adjustment Displayed items: The detailed graph of authenticity and risk is hidden. Only three levels of warning ("Safe / Caution / Dangerous") plus a brief explanation are displayed. Layout: The buttons are made larger and the steps are presented in a wizard format: "1 / 3 → 2 / 3 → 3 / 3". Explanation Content: Technical terms are given furigana (pronunciation guides) and annotations. Essential steps, such as "Please take a photo before mailing," are displayed in large letters. Operation Guide: A single sentence, "What to do on this screen," is displayed at the top of each screen.

[0502] effect For Ms. E, it becomes a "UI that allows her to complete the process without getting lost, even if it's a little slower," reducing errors and abandonment of the process midway.

[0503] Example 20-2 [Professional Trader × Large Screen PC] Professional trader F, who lives abroad Device: High-performance PC + large screen display Usage experience: Several hundred C2C transactions UI adjustments Display items: All detailed indicators (authenticity index, each AI score, total risk, market health index) are consolidated on a single screen. Layout: Table format + chart format allows you to compare multiple "buy / auction / consignment" scenarios. Description: The quick guide is hidden, and only links to each indicator are provided (to be referenced when needed). Operation Guide: Bring advanced shortcuts (keyboard operations and batch settings) to the forefront.

[0504] effect For F, it becomes a "professional UI that allows for a comprehensive overview of high-density information without unnecessary explanations getting in the way," enabling quick decision-making.

[0505] Example 20-3 [UI Optimization Based on Language and Culture] English speaker G (using from overseas), English UI settings Natural UI Based on language settings, Switch all explanatory and contract documents to English templates. The price is not displayed as "¥1,000,000" but as "¥1,000,000" plus the "USD equivalent". Depending on the local culture, the examples and metaphors used in risk explanations should be replaced with those that are easily understood in that region.

[0506] effect G will feel less uncomfortable using "foreign services" and will be able to use them with the same level of understanding as domestic services.

[0507] Example 21-1 [Anonymization of transaction logs after 3 years] situation The "transaction logs" maintained within the system must be retained as raw data for three years, according to contractual and legal requirements. For logs older than three years, personal identification is not required. Activity of the Data Anonymization Processing Unit 1. Batch processing is performed on logs that are "more than 3 years old". 2. Name, address, biometric information, device ID, bank account number, etc. Delete, hash, or mask. 3. Regarding the region Round "Somewhere in Shinjuku Ward, Tokyo" to "Shinjuku Ward, Tokyo". 4. The transaction amount is Instead of precise figures, convert them to a "range of X yen".

[0508] result While it's impossible to know whose data it is, statistics such as "when, which brand, what price range, and what kind of counterfeiting or incidents occurred" can be directly used in AI reports.

[0509] Example 21-2 [Use as training data] situation We want to continuously retrain our AI for authenticity verification, delivery anomaly analysis, and AML (Anti-Money Laundering) AI. However, we want to avoid holding users' PII (Primary Indicator) for extended periods. Use after anonymization What is needed to improve accuracy Product images, material analysis data, damage characteristics, shipping route, price range, region code, etc. Personal identifiers have been hashed or removed, For applications where it is not necessary to track the "consecutive actions of the same person" Anonymized data alone is sufficient for retraining.

[0510] → Data deletion does not equal resetting the AI's intelligence. This can be interpreted as a system where "even if the individual is forgotten, the experience remains."

[0511] Example 21-3 [Use in Market Health Index] situation Market Health Index Counterfeit detection rate Rate of suspected involvement with anti-social groups Number of AML (Anti-Money Laundering) cases Delivery accident rate Assessment discrepancy rate Regional Security Index Fair trade rate An indicator that shows the "overall health of the market" based on factors such as these. The role of anonymized data There is no need to identify individual users; only the "results" of each transaction need to be known.

[0512] By using anonymized logs, health indicators can be calculated over the long term by region, brand, and channel with zero risk of identifying individuals. → By combining the Market Health Index with "complete anonymization processing," it becomes an infrastructure that "continues to conduct health checks on the entire market while protecting privacy."

[0513] Example 22-1 [Delivery Anomaly (Analysis) AI Consistently Determines High Risk Only for Deliveries in Mountainous Areas] → The system determined that geographical characteristics were having an excessive impact and corrected the feature weights.

[0514] Example 22-2 [AML score overreacts to specific attributes] → Adjust or remove the weights of those features.

[0515] Example 23-1 [When the market data API returns an abnormal market price] → Switch to alternative market API or cached price.

[0516] Example 23-2 [When location information and time synchronization are disrupted in the delivery API] → Recalculate the probability of reaching the destination and the deviation rate using the local estimation model.

[0517] Example 23-3 [Financial API response contradicts past account status] → The transfer process will be temporarily suspended and switched to an alternative route.

[0518] Example 24-1 [Counterfeit Rolex watches with the same engraving pattern found in various locations across Japan] Multiple Rolex watches with "slightly incorrect engravings" were brought in from different affiliated stores. Each was later confirmed to be a forgery → by a fraudulent template learning AI It was found that they belong to the same marking feature cluster. Templating If a Rolex model number is xx and the line width, depth, and edge characteristics of the engraving fall within this distribution, it will be treated as template #RLX-23.

[0519] Future operations If a new project shows a high similarity to template #RLX-23, It can be treated as a "strong suspicion of forgery" from the very first instance.

[0520] Example 24-2 [Frequent removal of contents from packages that have passed through specific delivery centers] The delivery anomaly (analysis) AI detected numerous cases involving "delay at base X + weight reduction." Insurance company and police investigations confirmed that the theft occurred within base X. Templating The combination of "Delivery company A x Location X x Specific time slot" is, Registered as template #DLS-07.

[0521] Future operations Packages likely to travel along that route will automatically be placed in enhanced shipping mode, insurance will be required, and transaction restrictions will be imposed. Prevention can be achieved by combining these methods.

[0522] Example 24-3 [Money Laundering Patterns by Specific User Groups] AML AI detects user groups with "similar behavioral patterns" as outliers. An investigation by the Financial Institutions Unit (FIU) confirmed that the funds were being used for "organized money laundering." Templating The timeline pattern is: "High-value purchase in one week → immediate diversified transfer → partial transfer to a specific country." Registered as template #AML-12.

[0523] Future operations If a similar time-series pattern emerges, it will be considered "high risk" from the first instance and will be heavily reflected in the Kill Switch conditions for STR candidates.

[0524] Example 25-1 [Material Analysis Module Failure] → Launching the alternative material analysis module.

[0525] Example 25-2 [Abnormal Load on Financial API Integration Module] → Reconfigure to a different configuration, restore the cache, and then reconnect.

[0526] Example 25-3 [Temporary functional degradation of anti-social group matching module] → After determining the cause, the system switches to the alternative matching module.

[0527] Example A-1 [Contents swapped during delivery + counterfeit goods mixed in] 1. When shipping:

[0528]

number

[0529]

number

[0530] → Users, merchants, delivery companies, insurance companies, government agencies, and courts can all reproduce "why they reached this conclusion" using the same data and algorithms.

[0531] Example A-2 [Automatic generation and accuracy improvement of insurance claims] Traditionally, Long exchanges between the store, user, delivery, and insurance → Incomplete documentation, delays, and distrust.

[0532] The effects of the present invention are, AI estimates the type of accident and potential causes, and automatically extracts the set of evidence the insurance company needs from pre- and post-shipment photos, weight, route, impact logs, and authenticity verification results → automatically generates claim data. → This speeds up the process, ensures legitimate claims are processed smoothly, and allows fraudulent claims to be refuted with data. 1. Person A wants to sell a gold necklace by mail. The app guides you through the process of "sealing tag + weight registration + pre-shipment photo". Our AI-powered delivery risk assessment system suggests the optimal delivery company and insurance options. 2. The contents were swapped at a transit point during delivery. From IoT impact logs and route logs, An unusual shock and prolonged delay were recorded at relay station X. The sealing tag AI and weight difference are also abnormal values ​​→ Delivery abnormality index D increases. 3. Arrive at store B → Authenticity detection AI determines the item is counterfeit. The contents are clearly different from the photo taken by person A before shipping. The authenticity index A is low. 4. The integrated risk AI highly rated Rtotal and put the trade on hold. There are no major problems with AS / AML, D.A. is dangerous → Becomes a candidate for Kill Switch. 5. AI-powered insurance claim system estimates accident type. It is suspected that the contents were swapped during delivery. Extract the location, time, and evidence (photos, weight, logs), Automatically generate claims data for insurance companies 6. Person A will be notified of the insurance compensation results and the circumstances in a visible format. The explanation stated, "Due to a delivery accident at relay point X, the contents were swapped, and we will compensate you XX yen through insurance," along with a summary of the accident log. 7. Store B, delivery company, insurance company, operating company, and government agency share the same log score. Having a shared understanding of what happened and where makes it easier to define responsibility and develop preventative measures. 8. This case will also be reflected in the franchisee training AI and Market Health Index. Increased risk for specific location X and specific delivery company → Audit and improvement target. If store B's response is appropriate, it will be reflected positively in the quality evaluation.

[0533] Example A-3 [Visualization of the end-user screen] General users (app users) Before mailing: "Sending it this way reduces the risk of delivery (the gauge is green)." A step display shows "Sealing tag and weight registration completed." In transit: "The package is currently at location XY, and no anomalies have been detected." In case of an abnormality, a clear message will be displayed, such as "Suspected seal damage" or "Suspected weight reduction." Upon arrival: "Authenticity AI Determination: Genuine / Caution / Dangerous" + Simple Reason Notification and justification for whether insurance coverage applies or not.

[0534] → For the user, We can provide a UI that allows users to constantly see "how secure their current transactions are and how they are being handled as a result."

[0535] Example A-4 [Visualization of the merchant dashboard] Franchise owners and headquarters staff can access their store-specific dashboards: Authenticity Match Rate Graph Average price deviation (difference from AI market price) Trends in the complaint rate Number of cases of forgery overlooked Training participation status / Passing status You can check the display.

[0536] → This makes it possible to visualize "what your store is strong and weak at" and "how far you are from headquarters' target line," enabling data-driven improvements and training rather than relying on intuition.

[0537] Example A-5 [AI for determining authenticity] Please enter the following: Product images (overall view, engraving, appearance, damage) XRF spectrum Weight / Volume (Specific Gravity) Magnetic response Ultrasonic reflection waveform

[0538] The following processing is possible for these items. Extraction of engraved area → Extraction of line width, depth, edge, alignment, and font features → Template matching Spectral analysis → Estimation of elemental ratios → Consistency check with specific gravity, magnetism, and ultrasound. Image score, brand score, and material score are combined to calculate A. Furthermore, image feature extraction can utilize methods such as CNNs, SIFT / SURF / Vision Transformers, spectral analysis can use peak detection + regression models, and integration can employ simple weighted averages, logistic regression, metamodels, and more.

[0539] Example A-6 [Resale becomes safer with digital warranty and digital twin] Users (asset holders), secondary market platforms, brand companies, and insurance companies, Using digital twins, electronic warranty certificates (NFT / VC format), and integrated assessment views, 1. At the time of the first transaction, a "digital twin" is created by compiling material composition, authenticity index, damage, photos, and owner information. 2. An electronic warranty will be issued based on that information and will be used as is in subsequent transactions. 3. During the reassessment, the AI ​​compares the "difference from the previous data" to determine whether it is natural deterioration due to aging or intentional modification / deception.

[0540] These factors High-value items that have been properly appraised can be treated as trustworthy assets in subsequent transactions, increasing the safety and liquidity of the resale market.

[0541] <Other Embodiments> The above inventions can also be implemented by substituting or adding to the technologies described below.

[0542] [Multi-brand, multi-category] This disclosure allows for authenticity determination across categories, including not only precious metals such as gold and platinum, but also bags, watches, jewelry, apparel, shoes, works of art, antiques, and electronic devices. Different features (sewing, markings, internal structure, electrical properties, acoustic data, weight distribution, etc.) are extracted depending on the category and input into a category-specific AI model. Because a "Category-specific Evaluation Pipeline" is automatically built according to the category, it offers high extensibility.

[0543] [Advanced analysis of XRF data] XRF data analysis goes beyond simple peak detection; it analyzes the overall spectral shape, relative intensity between peaks, and the abundance of trace elements. Spectral anomalies specific to counterfeit products (peak shifts, mismatched elemental ratios, abnormal trace element concentrations) are detected by AI. Metal oxidation due to aging, chemical treatment marks, etc., can also be identified from the spectral pattern.

[0544] [Climate correction for material changes] Since the surface condition of precious metals, leather goods, and jewelry changes depending on climatic conditions (humidity, temperature, UV radiation, etc.), the AI ​​uses a climate impact model to correct for deterioration patterns. In assessments in hot and humid regions, adjustments are made to distinguish between natural deterioration and counterfeit processing. This prevents unfairly low valuations due to environmental differences.

[0545] [Integrated Hyperspectral Analysis] As a future extension, this invention can be integrated with material analysis using a hyperspectral camera. Hyperspectral data can accurately identify minute differences in material composition, traces of surface processing, and the progression of aging degradation. This significantly improves the accuracy of detecting subtle counterfeiting, which was difficult with conventional analysis.

[0546] [Extension of the Authenticity Integration Function: A (Fusion Authenticity)] It is also possible to add damage value correction AI to the above-mentioned authenticity integration index and use it as the following authenticity integration function: A (Fusion Authenticity).

[0547] A is a function that reduces "image analysis AI × brand authenticity AI × material analysis AI × damage value correction AI" to a single scalar (from 0 to 1).

[0548]

number

[0549] The above invention can also utilize the following technologies.

[0550] [Delivery abnormality index (delivery abnormality analysis AI)]

[0551]

number

[0552] How to use parameters Expensive and fragile items → Increase the impact (U) Areas with a high risk of theft → Increase the severity of route deviation (P) Business models with many intermediaries → Increase the weight difference (1-W) Depending on the risk profile of the site, the parameter θ i By adjusting this setting, you can fine-tune "how much weight is given to each type of anomaly."

[0553] Each term in the equation represents the "degree of anomaly" corresponding to a natural law, so a large D can be explained as suspicious behavior.

[0554] [Brand features in AI for brand authenticity detection] Brand characteristics are

[0555]

number

[0556] The similarity is,

[0557]

number

[0558] Additionally, the following process (logistics) AI formula / calculation, [seal tag matching degree], can also be used.

[0559]

number

[0560] Additionally, the following process (logistics) AI for...

Claims

1. A commodity trading security management information processing device that includes determining the authenticity of the materials of specific goods, including precious metals, jewelry, or branded goods, which are the subject of trading, An XRF acquisition unit that acquires spectral data of fluorescent X-rays irradiated onto the specified product, Extracted data including the peak position, peak intensity, peak width, and trace element distribution of each element is generated from the spectral data. Based on the extracted data, the content of precious metals including gold, platinum, palladium, or silver is estimated, A material analysis AI unit that detects hollow structures, foreign material inclusions, plating treatments, or the use of counterfeit metals based on abnormal values ​​in the spectral pattern of the specified product, A commodity transaction security management information processing device comprising a material analysis information processing device and a material authenticity determination unit that uses the material analysis results, which are estimation results from the material analysis AI unit, for determining the authenticity of the specified product.

2. A specific gravity measuring unit that calculates the specific gravity value based on the weight and volume of the object, A magnetic inspection unit that detects the presence or absence of a magnetic field response, the presence of foreign materials based on magnetic field strength, or tungsten substitution, It comprises an ultrasonic analysis unit that detects internal cavities, layered structures, or density unevenness based on the reflected waveform of ultrasonic pulses, The information processing device for managing the security of commodity transactions according to claim 1, further comprising a composite material analysis unit that inputs the specific gravity value, magnetic response, or ultrasonic reflection characteristics to the material analysis AI unit and performs composite material determination.

3. The marking features, scratch features, or appearance features obtained from the image analysis AI unit, Sewing characteristics, logo characteristics, or internal structure characteristics obtained from the brand authenticity AI department, or The AI ​​unit for material analysis takes as input three or more integrated features, including elemental content, specific gravity, magnetic response, or ultrasonic reflection pattern, Based on the aforementioned integrated features, a comprehensive authenticity index is calculated to indicate the genuineness of the traded item. The information processing device for managing the security of commodity transactions according to claim 2, further comprising an integrated authenticity / counterfeiting AI unit.

4. We extract damage features, including scratches, defects, dents, polishing marks, and wear marks, from product images. The relationship between the aforementioned damage characteristics, the material analysis results, and the overall authenticity index was analyzed. The information processing device for managing the security of commodity transactions according to claim 3, further comprising a damage value correction AI unit that calculates an assessed value correction value according to the type, location, depth, and extent of the damage.

5. The inputs include three or more of the following: the overall authenticity index, the anti-social activity index, the anti-money laundering score, the behavioral abnormality index, or a delivery risk index that includes at least one of the following: deviation from the delivery route, abnormal sealing tag, or weight discrepancy. The information processing device for managing the security of commodity transactions according to claim 3, further comprising a risk integration AI unit that calculates a comprehensive risk index indicating the overall risk level of the transaction based on the aforementioned input.

6. If the aforementioned overall risk index exceeds a predetermined threshold, (1) Prohibition of postal transactions, (2) Limitation to over-the-counter transactions, (3) Additional identity verification request, (4) Transaction suspension linked to banks, (5) The information processing apparatus for managing the security of commodity transactions according to claim 5, further comprising a transaction restriction control unit that automatically controls the application of at least one of the following delivery enhancement modes, including seal reinforcement, dual weight measurement, or surveillance camera recording.

7. The system acquires at least two of the following from the trader's terminal: GPS positioning information, surrounding Wi-Fi access point information, or IP address-derived location estimation information. By analyzing the degree of consistency of these location data, temporal consistency, and the rationality of the movement trajectory, The information processing device for managing the security of commodity transactions according to claim 1 or 2, further comprising a multi-location verification unit that calculates the reliability of the current location of the trader.

8. Terminal behavior data obtained from at least one of the following: acceleration sensor, gyroscope sensor, barometric pressure sensor, or tilt sensor, By comparing changes in location information, movement speed, and radio wave intensity, The information processing device for managing the security of commodity transactions according to claim 7, further comprising a location spoofing detection unit that calculates a location spoofing index indicating the possibility that the terminal location has been spoofed, including GPS spoofing.

9. Based on at least one of the following: the trading device's OS version, security patch status, rooted or jailbroken status, installed applications, past fraudulent activity history, or location spoofing index, The information processing apparatus for managing the security of commodity transactions according to claim 1 or 2, further comprising a terminal evaluation unit that calculates a device reliability score indicating the security and reliability of the terminal.

10. Referencing at least one of the following for each delivery company: past accident rate, loss rate, delay rate, damage rate, or risk index of the delivery route, The information processing device for managing the security of commodity transactions according to claim 1 or 2, further comprising a delivery risk assessment unit that calculates a risk index for each delivery company and a risk index for delivery routes.

11. The information processing apparatus for managing the security of commodity transactions according to claim 1 or 2, further comprising an emergency stop control unit that immediately suspends a transaction, delivery process, or fund transfer process if three or more of the following exceed a predetermined risk threshold: anti-social activity index, anti-money laundering score, delivery anomaly index, location falsification index, or device reliability score.

12. If a delay in response, an error response, no response, or inconsistent response is detected in the anti-social matching API, banking API, delivery API, or insurance API, The information processing apparatus for managing the security of commodity transactions according to claim 1 or 2, further comprising a fail-safe switching unit that maintains the continuity of transaction processing by automatically switching to at least one of an alternative API, cached data, or local estimation logic.

13. Vectorize at least one of the following: product image, engraving features, material analysis features, or brand features. By calculating the degree of deviation from past product features recorded in the vector database, The information processing device for managing the security of commodity transactions according to claim 1 or 2, further comprising a similarity feature search AI unit that extracts candidates that match patterns of similar products, related products, or counterfeit products.

14. Regarding at least one of the following: marking characteristics, brand characteristics, material characteristics, damage characteristics, or shipping characteristics, Based on the difference from known genuine data, statistical outliers, anomalous correlation patterns, or the degree of deviance in the trained feature space, The information processing device for managing the security of commodity transactions according to claim 1 or 2, further comprising a counterfeit anomaly detection AI unit that calculates a counterfeit anomaly index indicating suspicion of unknown counterfeit goods.

15. Integrating at least two of the following: delivery tracking data including location, time, and dwell time; seal tag image differences; weight differences; IoT sensor data; or delivery company risk information. The information processing apparatus for managing the security of commodity transactions according to claim 1 or 2, further comprising a delivery anomaly analysis unit that calculates a delivery anomaly index indicating deviations, impacts, substitutions, seal breakage, or internal fraud during the delivery process.

16. Based on at least one of the following: the overall authenticity index, the delivery anomaly index, the seal tag detection result, the weight difference result, the damage analysis result, or the delivery company log, Estimate the possibility of a delivery accident or damage occurring. The information processing device for managing the security of commodity transactions according to claim 1 or 2, further comprising an automated insurance claim AI unit that automatically extracts items requested by an insurance company, including the cause of the accident, evidence data, time of occurrence, delivery route, or product information, and generates insurance claim data.

17. At least one of the following at member stores—appraisal results, authenticity matching rate, damage detection accuracy, price discrepancy rate, claim occurrence rate, processing speed, or counterfeit detection history for each member store—is subjected to machine learning. We will calculate an appraisal quality index for each member store, The information processing device for managing the security of commodity transactions according to claim 1 or 2, further comprising an AI unit for franchisee training that presents assessment procedures, points to note, or items for improvement based on the aforementioned assessment quality indicators.

18. Recommendation features are extracted from at least one of the following: the trader's transaction history, product category, brand, material authenticity index, price range, or market demand index. The information processing device for managing the security of commodity transactions according to claim 1 or 2, further comprising a transaction recommendation AI unit that presents a suitable transaction method, recommended merchant, or appropriate price range to the trader based on the aforementioned recommendation features.

19. Input at least three of the following: counterfeit detection rate, suspected anti-social activity rate, suspected anti-money laundering cases, delivery accident rate, appraisal discrepancy rate, regional security index, or legitimate transaction rate. The information processing device for managing the security of commodity transactions according to claim 1 or 2, further comprising a market health analysis unit that calculates a market health index indicating the overall health of the market.

20. Based on at least one of the following: the age group of the trader, their experience, device performance, screen size, operation history, or language settings, The information processing device for managing the security of commodity transactions according to claim 1 or 2, further comprising an adaptive UI control unit that automatically adjusts display items, layout, explanatory content, and operation guidance.

21. Regarding at least a portion of the following: name, address, biometric information, transaction identification information including terminal ID, financial information, location information, or delivery information, To ensure that personal identification becomes impossible after the retention period has expired, the data is converted into anonymized data by performing at least one of the following processes: deletion, masking, random number substitution, or hashing. The information processing apparatus for managing the security of commodity transactions according to claim 1 or 2, further comprising a data anonymization processing unit that makes the anonymized data available for use in authenticity model training, risk analysis, or market statistics.

22. Regarding at least one of the following: authenticity detection AI, behavioral abnormality AI, anti-social group analysis AI, anti-money laundering scoring AI, or delivery abnormality AI, Analyze statistical deviations, inter-group imbalances, or excessive influence on specific attributes caused by input features. In addition to calculating an equity index that shows the degree of bias, The information processing apparatus for managing the security of commodity transactions according to claim 1 or 2, further comprising an AI fairness correction unit that automatically applies at least one of the following processes: adjustment of feature weights, retraining, feature exclusion, or correction processing, when the fairness index deviates from an acceptable range.

23. If a response delay, error response, no response, or content inconsistency is detected for at least one of the following APIs: anti-social API, financial API, delivery API, insurance API, or market data API, Automatically switches to at least one of the following: alternative API, cached data, or local estimation model. The information processing apparatus for managing the security of commodity transactions according to claim 1 or 2, further comprising an API abnormality control unit that maintains continuous transaction processing.

24. Analyze at least one of the following characteristics specific to counterfeit goods: differences in markings, abnormal material composition, abnormal weight patterns, abnormal delivery patterns, inconsistent behavior patterns, or tendencies to raise suspicions of anti-social activities or anti-money laundering. Automatically generate fraud templates representing new forgery techniques or fraudulent behavior patterns. The information processing apparatus for managing the security of commodity transactions according to claim 1 or 2, further comprising a fraudulent template learning unit that causes each AI model to continuously learn the aforementioned fraudulent template.

25. If a failure, abnormal load, error, or functional degradation is detected in at least one of the following modules: AI inference module, material analysis module, delivery analysis module, anti-social matching module, or financial API integration module, The information processing device for managing the security of commodity transactions according to claim 1 or 2, further comprising an autonomous recovery engine that autonomously performs at least one of the following: cause determination, activation of an alternative module, configuration reconfiguration, or cache restoration to perform recovery.

26. A method for managing the security of commodity transactions, which includes determining the authenticity of the materials of specific goods, including precious metals, jewelry, or branded goods, that are the subject of trading, An XRF acquisition step to acquire spectral data of fluorescent X-rays irradiated onto the specified product, Extracted data including the peak position, peak intensity, peak width, and trace element distribution of each element is generated from the spectral data. Based on the extracted data, the content of precious metals including gold, platinum, palladium, or silver is estimated, A material analysis AI processing step that detects a hollow structure, foreign material inclusion, plating treatment, or use of counterfeit metal based on abnormal values ​​in the spectral pattern of the specified product, A method for managing the security of product transactions, including a material analysis information processing method, which includes a material analysis result, which is an estimation result obtained by the material analysis AI processing step, used for determining the authenticity of the specified product.

27. A commodity trading security management information processing program that includes determining the authenticity of materials of specific goods, including precious metals, jewelry, or branded goods, which are the subject of trading, On the computer, An XRF acquisition step to acquire spectral data of fluorescent X-rays irradiated onto the specified product, Extracted data including the peak position, peak intensity, peak width, and trace element distribution of each element is generated from the spectral data. Based on the extracted data, the content of precious metals including gold, platinum, palladium, or silver is estimated, A material analysis AI processing step that detects a hollow structure, foreign material inclusion, plating treatment, or use of counterfeit metal based on abnormal values ​​in the spectral pattern of the specified product, A product transaction security management information processing program that executes a material authenticity determination step in which the material analysis results, which are estimation results from the material analysis AI processing step, are used to determine the authenticity of the specified product.

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