system

Abstract

The system according to this embodiment aims to quickly and accurately determine the authenticity of reused items. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a determination unit, and an issuing unit. The reception unit inputs a photograph and tag information of the reused item. The analysis unit analyzes the information input by the reception unit. The determination unit determines authenticity based on the information analyzed by the analysis unit. The issuing unit issues a certificate based on the result determined by the determination unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it takes time and effort to determine the authenticity of reused products, and there is a risk that counterfeits will circulate in the market.

[0005] The system according to the embodiment aims to quickly and accurately determine the authenticity of reused products.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a determination unit, and an issuance unit. The reception unit inputs a photo of a reused product and tag information. The analysis unit analyzes the information input by the reception unit. The determination unit determines authenticity based on the information analyzed by the analysis unit. The issuance unit issues a certificate based on the result determined by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and accurately determine the authenticity of reused items. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The authenticity determination system according to an embodiment of the present invention is an AI-based authenticity determination system for the reuse market, which has become widespread due to the proliferation of smartphones and the COVID-19 pandemic. This system makes it easier for "genuine" items to circulate and harder for "counterfeit" items to circulate, thereby suppressing the occurrence of crimes related to the sale of counterfeit goods. Specifically, it consists of the following steps. First, the user inputs photos (overhead and micro-photographs) and tag information of the reused item into the system. Next, the AI ​​analyzes this information and determines its authenticity. For example, in determining the authenticity of works of art or branded goods, the AI ​​performs analysis using image recognition technology and infrared irradiation. Based on the results of this analysis, the system determines whether the item is "genuine" or "counterfeit" and issues a certificate. This system targets individuals who list and buy used goods between individuals, as well as small and medium-sized pawn shops. In person-to-person transactions, it solves the problem of wanting to sell quickly and easily but not wanting to be complicit in crime by selling counterfeit goods. In pawn shops, it solves the problem of difficulty in securing personnel capable of authenticity determination and the high labor costs involved. Furthermore, this system aims to promote fair transactions in the reuse market, which has a market size of approximately 3 trillion yen, and to realize a society where everyone can obtain "genuine" items. This allows the authenticity verification system to efficiently determine the authenticity of reused items and issue certificates.

[0029] The authenticity determination system according to the embodiment comprises a reception unit, an analysis unit, a determination unit, and an issuing unit. The reception unit inputs a photograph and tag information of a reused item. For example, the reception unit can receive a photograph of a reused item and tag information from a user using a smartphone. The reception unit can also receive photographs of the reused item from multiple angles to provide detailed information. For example, the reception unit can receive overhead and micro-photographs of the reused item. Furthermore, the reception unit can receive barcodes and QR codes (registered trademarks) as tag information. The analysis unit analyzes the information input by the reception unit. For example, the analysis unit can analyze a photograph of a reused item using image recognition technology. Furthermore, the analysis unit can analyze the internal structure of a reused item using infrared irradiation. For example, the analysis unit can detect internal abnormalities of a reused item using infrared irradiation. Furthermore, the analysis unit can use AI to analyze the characteristics of a reused item and provide data for determining its authenticity. The determination unit determines authenticity based on the information analyzed by the analysis unit. The determination unit can, for example, determine whether an item is "genuine" or "fake" based on the analysis results. The determination unit can also use AI to evaluate the analysis results and determine authenticity. For example, the determination unit can use an AI model to take the analysis results as input and output authenticity. The issuing unit issues a certificate based on the results determined by the determination unit. The issuing unit can, for example, issue a digital certificate based on the determination results. The issuing unit can also issue a paper certificate. For example, the issuing unit can print the certificate using a printer. Thus, the authenticity determination system according to this embodiment can efficiently determine the authenticity of reused items and issue certificates.

[0030] The reception desk inputs photos and tag information of the reusable items. For example, users can take photos of reusable items using their smartphones and input tag information. Specifically, users use their smartphone cameras to take pictures of the overall appearance and specific features of the reusable items and upload these images to the system. Furthermore, the reception desk can also provide detailed information by taking photos of the reusable items from multiple angles. For example, by taking photos from different angles such as the front, back, sides, and bottom of the reusable item, the overall condition of the reusable item can be understood in detail. It is also possible to capture details and minute features of the reusable item using microphotography. This allows for the acquisition of information that is often missed in regular photos, such as minute scratches, markings, and material texture. In addition, the reception desk can also input barcodes and QR codes as tag information. Users scan the barcode or QR code attached to the reusable item with their smartphones and input the information into the system. This allows for the quick and accurate acquisition of detailed information such as the manufacturer, manufacturing date, and serial number of the reusable item. The reception department centrally manages this information and transmits it to the analysis department. This allows the reception department to efficiently collect detailed information on reused items, improving the overall accuracy and reliability of the system.

[0031] The analysis unit analyzes the information entered by the reception unit. For example, the analysis unit can analyze photographs of reused items using image recognition technology. Specifically, it uses an image recognition algorithm to extract features such as the shape, color, texture, and markings of reused items, and compares these features with known information in the database. The analysis unit can also analyze the internal structure of reused items using infrared irradiation. By using infrared irradiation technology, it is possible to detect abnormalities and defects hidden inside reused items. For example, it can scan the inside of reused items using an infrared camera to detect abnormalities in the internal structure and materials. Furthermore, the analysis unit can also use AI to analyze the features of reused items and provide data for determining authenticity. The AI ​​uses a deep learning model to analyze images and internal structure data of reused items and extract features. As a result, the AI ​​can recognize minute features and patterns of reused items with high accuracy and generate data for determining authenticity. Based on this data, the analysis unit provides information for determining the authenticity of reused items. Furthermore, the analysis unit can utilize past data and statistical information to analyze the characteristics of reused items in more detail. For example, it can learn the characteristics of specific brands or models based on data of reused items that have been previously identified, and compare them with the characteristics of newly entered reused items. This allows the analysis unit to analyze the authenticity of reused items with high accuracy and provide reliable data.

[0032] The judgment unit determines authenticity based on the information analyzed by the analysis unit. For example, the judgment unit can determine whether an item is "genuine" or "fake" based on the analysis results. Specifically, it evaluates the authenticity of a reused item based on the feature quantities and anomaly detection results provided by the analysis unit. The judgment unit can also use AI to evaluate the analysis results and determine authenticity. For example, the judgment unit can use an AI model to take the analysis results as input and output authenticity. This AI model has learned from past authenticity determination data and can determine the authenticity of reused items with high accuracy. Furthermore, the judgment unit can integrate multiple analysis results to make a comprehensive judgment. For example, by integrating image recognition results, infrared analysis results, and AI analysis results and performing a comprehensive evaluation, it can achieve more reliable authenticity determination. The judgment unit also has an interface for providing the judgment results to the user, allowing the user to check the judgment results in real time. As a result, the judgment unit can quickly and accurately determine the authenticity of reused items based on the information provided by the analysis unit and provide the user with reliable information.

[0033] The issuing unit issues certificates based on the results determined by the judgment unit. For example, the issuing unit can issue digital certificates based on the judgment results. Specifically, it generates a digital certificate based on the authenticity judgment results provided by the judgment unit and provides it to the user. This digital certificate can be made tamper-proof using blockchain technology and serves as highly reliable evidence to prove the authenticity of reused items. The issuing unit can also issue paper certificates. For example, the issuing unit can print the certificate using a printer and provide it to the user. This paper certificate contains the judgment results and detailed information about the reused item, and the user can keep it as physical evidence. Furthermore, the issuing unit can manage the certificate issuance history and refer to information on certificates issued in the past. This allows the issuing unit to provide users with highly reliable authenticity judgment results for reused items and improve the reliability of reused item transactions.

[0034] The analysis unit can perform analysis using image recognition technology and infrared irradiation. For example, the analysis unit can analyze a photograph of a reused item using image recognition technology with deep learning. For example, the analysis unit can extract features of a reused item using a deep learning model and provide data for determining its authenticity. The analysis unit can also analyze the internal structure of a reused item using infrared irradiation. For example, the analysis unit can detect internal abnormalities of a reused item using infrared irradiation. Furthermore, the analysis unit can also analyze the features of a reused item using pattern recognition technology. For example, the analysis unit can analyze the surface pattern and texture of a reused item using pattern recognition technology and provide data for determining its authenticity. This improves the accuracy of the analysis by using image recognition technology and infrared irradiation. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input a photograph of a reused item into a generating AI and have the generating AI perform analysis using image recognition technology.

[0035] The judgment unit can determine "authenticity" based on the analysis results. For example, the judgment unit can determine whether an item is "genuine" or "fake" based on the analysis results. For example, the judgment unit can also use AI to evaluate the analysis results and determine authenticity. For example, the judgment unit can use an AI model to take the analysis results as input and output authenticity. The judgment unit can also use a rule-based algorithm to determine authenticity based on the analysis results. For example, the judgment unit can determine authenticity according to predefined rules based on the analysis results. Furthermore, the judgment unit can also use a machine learning model to determine authenticity based on the analysis results. For example, the judgment unit can use a machine learning model to take the analysis results as input and output authenticity. This enables accurate authenticity determination based on the analysis results. Some or all of the above-described processes in the judgment unit may be performed using AI or not. For example, the judgment unit can input the analysis results into a generating AI and have the generating AI perform the authenticity determination.

[0036] The issuing unit can issue a certificate based on the judgment result. For example, the issuing unit can issue a digital certificate based on the judgment result. For example, the issuing unit can send the digital certificate by email. The issuing unit can also issue a paper certificate. For example, the issuing unit can print the certificate using a printer. Furthermore, the issuing unit can include a QR code or barcode in the certificate. For example, the issuing unit can print a QR code on the certificate and provide a link to verify the authenticity of the certificate. This makes it possible to issue certificates quickly based on the judgment result. Some or all of the above processes in the issuing unit may be performed using AI or not. For example, the issuing unit can input the judgment result into a generating AI and have the generating AI issue the certificate.

[0037] The reception unit can input photos and tag information of reused items in person-to-person transactions and pawn shops. For example, in person-to-person transactions, the reception unit can input photos of reused items taken by users using their smartphones and input tag information. The reception unit can also provide detailed information by taking photos of reused items from multiple angles in pawn shops. For example, the reception unit can input overhead and micro-photographs of reused items. Furthermore, the reception unit can input barcodes and QR codes as tag information. This makes it possible to efficiently input information on reused items in person-to-person transactions and pawn shops. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input photos and tag information of reused items entered by users into a generating AI and have the generating AI perform information analysis.

[0038] The reception desk can analyze the user's past input history and provide the optimal input interface. For example, the reception desk can automatically display as suggestions categories of reusable items that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest categories of reusable items to be used during a specific time period based on the user's past input history. This improves input efficiency by providing the optimal interface based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input interface.

[0039] The reception unit can filter the input of photos and tag information for reused items based on the user's current transaction status and areas of interest. For example, the reception unit can prioritize displaying information related to reused items the user is currently trading. The reception unit can also automatically suggest relevant reused item categories based on the user's areas of interest. Furthermore, the reception unit can analyze the user's transaction history and prioritize displaying information related to reused items traded in the past. This allows the system to provide highly relevant information by filtering based on the user's transaction status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's transaction history data into a generating AI and have the generating AI perform the filtering of relevant information.

[0040] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting photos and tag information for reused items. For example, if the user is in a specific region, the reception desk will prioritize displaying categories of reused items that are popular in that region. The reception desk can also prioritize inputting information based on the user's geographical location, referencing nearby transaction history. Furthermore, if the user is traveling, the reception desk can prioritize displaying reused item transaction information in their travel destination. In this way, highly relevant information can be prioritized by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize relevant information.

[0041] The reception desk can analyze the user's social media activity and input relevant information when inputting photos and tag information for reused items. For example, the reception desk can prioritize displaying categories of reused items that the user frequently posts about on social media. It can also prioritize inputting information about reused items that the user's social media followers are interested in. Furthermore, the reception desk can analyze the content of the user's social media posts and automatically input relevant reused item information. This allows for efficient input of relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the analysis of relevant information.

[0042] The analysis unit can optimize its analysis algorithm by referring to past transaction data of reused items during analysis. For example, the analysis unit can improve accuracy by optimizing the analysis algorithm based on past transaction data of reused items. The analysis unit can also refer to past transaction data to reflect the characteristics of specific reused items in the analysis algorithm. Furthermore, the analysis unit can analyze past transaction data and adjust the parameters of the analysis algorithm. This improves the accuracy of the analysis algorithm by referring to past transaction data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past transaction data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0043] The analysis unit can apply different analysis methods depending on the category of the reused item during analysis. For example, in the case of artwork, the analysis unit uses image recognition technology. In the case of branded goods, the analysis unit can also use infrared irradiation for analysis. Furthermore, in the case of electronic devices, the analysis unit can analyze the internal structure. By applying analysis methods appropriate to the category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input reused item category data into a generating AI and have the generating AI execute the application of different analysis methods.

[0044] The analysis unit can perform analysis while considering the geographical distribution of reused items. For example, the analysis unit can optimize the analysis algorithm based on the geographical distribution of reused items. The analysis unit can also reflect the characteristics of reused items in a specific region in the analysis, taking geographical distribution into account. Furthermore, the analysis unit can analyze the geographical distribution and add region-specific information to the analysis results. This makes it possible to perform analysis that reflects region-specific information by considering geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical distribution data of reused items into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on reused items during the analysis process. For example, the analysis unit can refer to relevant literature on reused items and optimize its analysis algorithm. Furthermore, the analysis unit can reflect the characteristics of specific reused items in the analysis based on the relevant literature. In addition, the analysis unit can analyze the relevant literature and add the literature information to the analysis results. This improves the accuracy of the analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI optimize the analysis algorithm.

[0046] The judgment unit can improve the accuracy of its judgment by considering the interrelationships of reused items during the judgment process. For example, the judgment unit optimizes the judgment algorithm based on the interrelationships of reused items. The judgment unit can also reflect the characteristics of specific reused items in the judgment by considering the interrelationships. Furthermore, the judgment unit can analyze the interrelationships and add relevant information to the judgment result. This improves the accuracy of the judgment by considering the interrelationships of reused items. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input data on the interrelationships of reused items into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0047] The judgment unit can make a judgment while considering the attribute information of the submitter of the reused item. For example, the judgment unit can optimize the judgment algorithm based on the submitter's attribute information. The judgment unit can also reflect the characteristics of a specific reused item in the judgment, taking the attribute information into consideration. Furthermore, the judgment unit can analyze the attribute information and add relevant information to the judgment result. This improves the accuracy of the judgment by considering the submitter's attribute information. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0048] The judgment unit can perform judgments while considering the geographical distribution of reused items. For example, the judgment unit optimizes the judgment algorithm based on the geographical distribution of reused items. The judgment unit can also reflect the characteristics of reused items in a specific region in the judgment, taking geographical distribution into account. Furthermore, the judgment unit can analyze the geographical distribution and add region-specific information to the judgment results. This makes it possible to perform judgments that reflect region-specific information by considering geographical distribution. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input geographical distribution data of reused items into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0049] The judgment unit can improve the accuracy of its judgment by referring to relevant literature on reused items during the judgment process. For example, the judgment unit can refer to relevant literature on reused items and optimize its judgment algorithm. The judgment unit can also reflect the characteristics of specific reused items in its judgment based on the relevant literature. Furthermore, the judgment unit can analyze the relevant literature and add literature information to the judgment result. This improves the accuracy of the judgment by referring to relevant literature. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input relevant literature data into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0050] The issuing unit can select the optimal issuance method by referring to past transaction data of the reused item when issuing a certificate. For example, the issuing unit can select the optimal certificate format based on past transaction data of the reused item. The issuing unit can also refer to past transaction data to determine the content of a certificate that is appropriate for a specific reused item. Furthermore, the issuing unit can analyze past transaction data and optimize the certificate issuance method. This allows the optimal issuance method to be selected by referring to past transaction data. Some or all of the above processes in the issuing unit may be performed using AI or not. For example, the issuing unit can input past transaction data into a generating AI and have the generating AI perform the optimization of the certificate issuance method.

[0051] The issuing unit can apply different issuance methods depending on the category of the reused item when issuing certificates. For example, in the case of artwork, the issuing unit can issue a certificate that includes a detailed description and images. In the case of branded goods, the issuing unit can also issue a certificate that includes the product's serial number and manufacturing information. Furthermore, in the case of electronic equipment, the issuing unit can issue a certificate that includes technical specifications and operational verification results. This allows for the issuance of appropriate certificates by applying the appropriate issuance method according to the category. Some or all of the above processing in the issuing unit may be performed using AI or not. For example, the issuing unit can input reused item category data into a generating AI and have the generating AI execute the application of different issuance methods.

[0052] The issuing unit can select the optimal issuance method when issuing certificates, taking into account the geographical distribution of reused items. For example, the issuing unit can select the optimal certificate format based on the geographical distribution of reused items. The issuing unit can also determine the content of the certificate that is appropriate for reused items in a specific region, taking geographical distribution into consideration. Furthermore, the issuing unit can analyze geographical distribution and optimize the certificate issuance method. This allows for the issuance of certificates that reflect region-specific information by considering geographical distribution. Some or all of the above processes in the issuing unit may be performed using AI or not. For example, the issuing unit can input geographical distribution data of reused items into a generating AI and have the generating AI optimize the certificate issuance method.

[0053] The issuing unit can improve the accuracy of certificate issuance by referring to relevant literature on reused items when issuing certificates. For example, the issuing unit can refer to relevant literature on reused items and optimize the content of the certificate. The issuing unit can also determine the content of the certificate that is appropriate for a specific reused item based on the relevant literature. Furthermore, the issuing unit can analyze the relevant literature and optimize the certificate issuance method. This improves the accuracy of issuance by referring to relevant literature. Some or all of the above processes in the issuing unit may be performed using AI or not. For example, the issuing unit can input relevant literature data into a generating AI and have the generating AI perform the optimization of the certificate issuance method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can analyze the user's past input history and provide the optimal input interface. For example, it can automatically display categories of reusable items that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest categories of reusable items that the user will use at a specific time of day based on their past input history. This improves input efficiency by providing the optimal interface based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input interface.

[0056] The reception unit can filter the input of photos and tag information for reused items based on the user's current transaction status and areas of interest. For example, it can prioritize displaying information related to reused items the user is currently trading. It can also automatically suggest relevant reused item categories based on the user's areas of interest. Furthermore, it can analyze the user's transaction history and prioritize displaying information related to reused items traded in the past. This allows for the provision of highly relevant information by filtering based on the user's transaction status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's transaction history data into a generating AI and have the generating AI perform the filtering of relevant information.

[0057] The analysis unit can optimize its analysis algorithm by referring to past transaction data of reused items during analysis. For example, it can optimize the analysis algorithm based on past transaction data of reused items to improve accuracy. It can also refer to past transaction data to reflect the characteristics of specific reused items in the analysis algorithm. Furthermore, it can analyze past transaction data and adjust the parameters of the analysis algorithm. This improves the accuracy of the analysis algorithm by referring to past transaction data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past transaction data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0058] The judgment unit can improve the accuracy of its judgment by considering the interrelationships of reused items during the judgment process. For example, it can optimize the judgment algorithm based on the interrelationships of reused items. It can also reflect the characteristics of specific reused items in the judgment by considering the interrelationships. Furthermore, it can analyze the interrelationships and add relevant information to the judgment result. As a result, the accuracy of the judgment is improved by considering the interrelationships of reused items. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input data on the interrelationships of reused items into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0059] The issuing unit can select the optimal issuance method when issuing a certificate by referring to past transaction data of the reused item. For example, it can select the optimal certificate format based on past transaction data of the reused item. It can also determine the content of a certificate suitable for a specific reused item by referring to past transaction data. Furthermore, it can analyze past transaction data and optimize the certificate issuance method. This allows the optimal issuance method to be selected by referring to past transaction data. Some or all of the above processes in the issuing unit may be performed using AI or not. For example, the issuing unit can input past transaction data into a generating AI and have the generating AI perform the optimization of the certificate issuance method.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk inputs photos and tag information of the reusable items. Users can take photos of reusable items using their smartphones and input tag information. The reception desk can also take photos of the reusable items from multiple angles and provide detailed information. For example, overhead and close-up photos can be input, and barcodes or QR codes can be input as tag information. Step 2: The analysis unit analyzes the information entered by the reception unit. It can analyze photographs of reused items using image recognition technology and analyze the internal structure of reused items using infrared illumination. For example, it can detect internal abnormalities and use AI to analyze the characteristics of reused items and provide data for determining authenticity. Step 3: The judgment unit determines authenticity based on the information analyzed by the analysis unit. Based on the analysis results, it determines whether the item is "genuine" or "fake," and it is also possible to use AI to evaluate the analysis results and determine authenticity. For example, an AI model can be used as input for the analysis results and output for authenticity. Step 4: The issuing unit issues a certificate based on the result determined by the determination unit. Based on the determination result, a digital certificate can be issued, and a paper certificate can also be issued. For example, the certificate can be printed using a printer.

[0062] (Example of form 2) The authenticity determination system according to an embodiment of the present invention is an AI-based authenticity determination system for the reuse market, which has become widespread due to the proliferation of smartphones and the COVID-19 pandemic. This system makes it easier for "genuine" items to circulate and harder for "counterfeit" items to circulate, thereby suppressing the occurrence of crimes related to the sale of counterfeit goods. Specifically, it consists of the following steps. First, the user inputs photos (overhead and micro-photographs) and tag information of the reused item into the system. Next, the AI ​​analyzes this information and determines its authenticity. For example, in determining the authenticity of works of art or branded goods, the AI ​​performs analysis using image recognition technology and infrared irradiation. Based on the results of this analysis, the system determines whether the item is "genuine" or "counterfeit" and issues a certificate. This system targets individuals who list and buy used goods between individuals, as well as small and medium-sized pawn shops. In person-to-person transactions, it solves the problem of wanting to sell quickly and easily but not wanting to be complicit in crime by selling counterfeit goods. In pawn shops, it solves the problem of difficulty in securing personnel capable of authenticity determination and the high labor costs involved. Furthermore, this system aims to promote fair transactions in the reuse market, which has a market size of approximately 3 trillion yen, and to realize a society where everyone can obtain "genuine" items. This allows the authenticity verification system to efficiently determine the authenticity of reused items and issue certificates.

[0063] The authenticity determination system according to the embodiment comprises a reception unit, an analysis unit, a determination unit, and an issuing unit. The reception unit inputs a photograph of a reused item and tag information. For example, the reception unit can input a photograph of a reused item and tag information using a smartphone. The reception unit can also take photographs of the reused item from multiple angles and provide detailed information. For example, the reception unit can input overhead and micro-photographs of the reused item. Furthermore, the reception unit can input barcodes or QR codes as tag information. The analysis unit analyzes the information input by the reception unit. For example, the analysis unit can analyze a photograph of a reused item using image recognition technology. Furthermore, the analysis unit can analyze the internal structure of a reused item using infrared irradiation. For example, the analysis unit can detect internal abnormalities of a reused item using infrared irradiation. Furthermore, the analysis unit can use AI to analyze the characteristics of a reused item and provide data for determining its authenticity. The determination unit determines authenticity based on the information analyzed by the analysis unit. The determination unit can, for example, determine whether an item is "genuine" or "fake" based on the analysis results. The determination unit can also use AI to evaluate the analysis results and determine authenticity. For example, the determination unit can use an AI model to take the analysis results as input and output authenticity. The issuing unit issues a certificate based on the results determined by the determination unit. The issuing unit can, for example, issue a digital certificate based on the determination results. The issuing unit can also issue a paper certificate. For example, the issuing unit can print the certificate using a printer. Thus, the authenticity determination system according to this embodiment can efficiently determine the authenticity of reused items and issue certificates.

[0064] The reception desk inputs photos and tag information of the reusable items. For example, users can take photos of reusable items using their smartphones and input tag information. Specifically, users use their smartphone cameras to take pictures of the overall appearance and specific features of the reusable items and upload these images to the system. Furthermore, the reception desk can also provide detailed information by taking photos of the reusable items from multiple angles. For example, by taking photos from different angles such as the front, back, sides, and bottom of the reusable item, the overall condition of the reusable item can be understood in detail. It is also possible to capture details and minute features of the reusable item using microphotography. This allows for the acquisition of information that is often missed in regular photos, such as minute scratches, markings, and material texture. In addition, the reception desk can also input barcodes and QR codes as tag information. Users scan the barcode or QR code attached to the reusable item with their smartphones and input the information into the system. This allows for the quick and accurate acquisition of detailed information such as the manufacturer, manufacturing date, and serial number of the reusable item. The reception department centrally manages this information and transmits it to the analysis department. This allows the reception department to efficiently collect detailed information on reused items, improving the overall accuracy and reliability of the system.

[0065] The analysis unit analyzes the information entered by the reception unit. For example, the analysis unit can analyze photographs of reused items using image recognition technology. Specifically, it uses an image recognition algorithm to extract features such as the shape, color, texture, and markings of reused items, and compares these features with known information in the database. The analysis unit can also analyze the internal structure of reused items using infrared irradiation. By using infrared irradiation technology, it is possible to detect abnormalities and defects hidden inside reused items. For example, it can scan the inside of reused items using an infrared camera to detect abnormalities in the internal structure and materials. Furthermore, the analysis unit can also use AI to analyze the features of reused items and provide data for determining authenticity. The AI ​​uses a deep learning model to analyze images and internal structure data of reused items and extract features. As a result, the AI ​​can recognize minute features and patterns of reused items with high accuracy and generate data for determining authenticity. Based on this data, the analysis unit provides information for determining the authenticity of reused items. Furthermore, the analysis unit can utilize past data and statistical information to analyze the characteristics of reused items in more detail. For example, it can learn the characteristics of specific brands or models based on data of reused items that have been previously identified, and compare them with the characteristics of newly entered reused items. This allows the analysis unit to analyze the authenticity of reused items with high accuracy and provide reliable data.

[0066] The judgment unit determines authenticity based on the information analyzed by the analysis unit. For example, the judgment unit can determine whether an item is "genuine" or "fake" based on the analysis results. Specifically, it evaluates the authenticity of a reused item based on the feature quantities and anomaly detection results provided by the analysis unit. The judgment unit can also use AI to evaluate the analysis results and determine authenticity. For example, the judgment unit can use an AI model to take the analysis results as input and output authenticity. This AI model has learned from past authenticity determination data and can determine the authenticity of reused items with high accuracy. Furthermore, the judgment unit can integrate multiple analysis results to make a comprehensive judgment. For example, by integrating image recognition results, infrared analysis results, and AI analysis results and performing a comprehensive evaluation, it can achieve more reliable authenticity determination. The judgment unit also has an interface for providing the judgment results to the user, allowing the user to check the judgment results in real time. As a result, the judgment unit can quickly and accurately determine the authenticity of reused items based on the information provided by the analysis unit and provide the user with reliable information.

[0067] The issuing unit issues certificates based on the results determined by the judgment unit. For example, the issuing unit can issue digital certificates based on the judgment results. Specifically, it generates a digital certificate based on the authenticity judgment results provided by the judgment unit and provides it to the user. This digital certificate can be made tamper-proof using blockchain technology and serves as highly reliable evidence to prove the authenticity of reused items. The issuing unit can also issue paper certificates. For example, the issuing unit can print the certificate using a printer and provide it to the user. This paper certificate contains the judgment results and detailed information about the reused item, and the user can keep it as physical evidence. Furthermore, the issuing unit can manage the certificate issuance history and refer to information on certificates issued in the past. This allows the issuing unit to provide users with highly reliable authenticity judgment results for reused items and improve the reliability of reused item transactions.

[0068] The analysis unit can perform analysis using image recognition technology and infrared irradiation. For example, the analysis unit can analyze a photograph of a reused item using image recognition technology with deep learning. For example, the analysis unit can extract features of a reused item using a deep learning model and provide data for determining its authenticity. The analysis unit can also analyze the internal structure of a reused item using infrared irradiation. For example, the analysis unit can detect internal abnormalities of a reused item using infrared irradiation. Furthermore, the analysis unit can also analyze the features of a reused item using pattern recognition technology. For example, the analysis unit can analyze the surface pattern and texture of a reused item using pattern recognition technology and provide data for determining its authenticity. This improves the accuracy of the analysis by using image recognition technology and infrared irradiation. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input a photograph of a reused item into a generating AI and have the generating AI perform analysis using image recognition technology.

[0069] The judgment unit can determine "authenticity" based on the analysis results. For example, the judgment unit can determine whether an item is "genuine" or "fake" based on the analysis results. For example, the judgment unit can also use AI to evaluate the analysis results and determine authenticity. For example, the judgment unit can use an AI model to take the analysis results as input and output authenticity. The judgment unit can also use a rule-based algorithm to determine authenticity based on the analysis results. For example, the judgment unit can determine authenticity according to predefined rules based on the analysis results. Furthermore, the judgment unit can also use a machine learning model to determine authenticity based on the analysis results. For example, the judgment unit can use a machine learning model to take the analysis results as input and output authenticity. This enables accurate authenticity determination based on the analysis results. Some or all of the above-described processes in the judgment unit may be performed using AI or not. For example, the judgment unit can input the analysis results into a generating AI and have the generating AI perform the authenticity determination.

[0070] The issuing unit can issue a certificate based on the judgment result. For example, the issuing unit can issue a digital certificate based on the judgment result. For example, the issuing unit can send the digital certificate by email. The issuing unit can also issue a paper certificate. For example, the issuing unit can print the certificate using a printer. Furthermore, the issuing unit can include a QR code or barcode in the certificate. For example, the issuing unit can print a QR code on the certificate and provide a link to verify the authenticity of the certificate. This makes it possible to issue certificates quickly based on the judgment result. Some or all of the above processes in the issuing unit may be performed using AI or not. For example, the issuing unit can input the judgment result into a generating AI and have the generating AI issue the certificate.

[0071] The reception unit can input photos and tag information of reused items in person-to-person transactions and pawn shops. For example, in person-to-person transactions, the reception unit can input photos of reused items taken by users using their smartphones and input tag information. The reception unit can also provide detailed information by taking photos of reused items from multiple angles in pawn shops. For example, the reception unit can input overhead and micro-photographs of reused items. Furthermore, the reception unit can input barcodes and QR codes as tag information. This makes it possible to efficiently input information on reused items in person-to-person transactions and pawn shops. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input photos and tag information of reused items entered by users into a generating AI and have the generating AI perform information analysis.

[0072] The reception desk can estimate the user's emotions and adjust the input method for photos and tag information of reusable items based on the estimated emotions. For example, if the user is nervous, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of photos and tag information of reusable items. This improves user convenience by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception desk can analyze the user's past input history and provide the optimal input interface. For example, the reception desk can automatically display as suggestions categories of reusable items that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest categories of reusable items to be used during a specific time period based on the user's past input history. This improves input efficiency by providing the optimal interface based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input interface.

[0074] The reception unit can filter the input of photos and tag information for reused items based on the user's current transaction status and areas of interest. For example, the reception unit can prioritize displaying information related to reused items the user is currently trading. The reception unit can also automatically suggest relevant reused item categories based on the user's areas of interest. Furthermore, the reception unit can analyze the user's transaction history and prioritize displaying information related to reused items traded in the past. This allows the system to provide highly relevant information by filtering based on the user's transaction status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's transaction history data into a generating AI and have the generating AI perform the filtering of relevant information.

[0075] The reception desk can estimate the user's emotions and prioritize the information to be entered based on those emotions. For example, if the user is nervous, the reception desk may prioritize important information and postpone other information. If the user is relaxed, the reception desk may also prioritize detailed information. Furthermore, if the user is in a hurry, the reception desk may prioritize only the essential information. This allows for efficient information entry by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting photos and tag information for reused items. For example, if the user is in a specific region, the reception desk will prioritize displaying categories of reused items that are popular in that region. The reception desk can also prioritize inputting information based on the user's geographical location, referencing nearby transaction history. Furthermore, if the user is traveling, the reception desk can prioritize displaying reused item transaction information in their travel destination. In this way, highly relevant information can be prioritized by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize relevant information.

[0077] The reception desk can analyze the user's social media activity and input relevant information when inputting photos and tag information for reused items. For example, the reception desk can prioritize displaying categories of reused items that the user frequently posts about on social media. It can also prioritize inputting information about reused items that the user's social media followers are interested in. Furthermore, the reception desk can analyze the content of the user's social media posts and automatically input relevant reused item information. This allows for efficient input of relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the analysis of relevant information.

[0078] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can increase the accuracy of the analysis to provide reliable results. The analysis unit can also adjust the accuracy of the analysis to provide faster results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can adjust the accuracy of the analysis to provide faster results. This allows for the provision of reliable results by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit can optimize its analysis algorithm by referring to past transaction data of reused items during analysis. For example, the analysis unit can improve accuracy by optimizing the analysis algorithm based on past transaction data of reused items. The analysis unit can also refer to past transaction data to reflect the characteristics of specific reused items in the analysis algorithm. Furthermore, the analysis unit can analyze past transaction data and adjust the parameters of the analysis algorithm. This improves the accuracy of the analysis algorithm by referring to past transaction data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past transaction data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0080] The analysis unit can apply different analysis methods depending on the category of the reused item during analysis. For example, in the case of artwork, the analysis unit uses image recognition technology. In the case of branded goods, the analysis unit can also use infrared irradiation for analysis. Furthermore, in the case of electronic devices, the analysis unit can analyze the internal structure. By applying analysis methods appropriate to the category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input reused item category data into a generating AI and have the generating AI execute the application of different analysis methods.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, by adjusting the display method according to the user's emotions, highly visible results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0082] The analysis unit can perform analysis while considering the geographical distribution of reused items. For example, the analysis unit can optimize the analysis algorithm based on the geographical distribution of reused items. The analysis unit can also reflect the characteristics of reused items in a specific region in the analysis, taking geographical distribution into account. Furthermore, the analysis unit can analyze the geographical distribution and add region-specific information to the analysis results. This makes it possible to perform analysis that reflects region-specific information by considering geographical distribution. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical distribution data of reused items into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on reused items during the analysis process. For example, the analysis unit can refer to relevant literature on reused items and optimize its analysis algorithm. Furthermore, the analysis unit can reflect the characteristics of specific reused items in the analysis based on the relevant literature. In addition, the analysis unit can analyze the relevant literature and add the literature information to the analysis results. This improves the accuracy of the analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI optimize the analysis algorithm.

[0084] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated user emotions. For example, if the user is tense, the judgment unit can apply strict judgment criteria to provide reliable results. It can also apply flexible judgment criteria to provide quick results if the user is relaxed. Furthermore, if the user is in a hurry, the judgment unit can apply quick judgment criteria to provide results. This allows for reliable results by adjusting the judgment criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The judgment unit can improve the accuracy of its judgment by considering the interrelationships of reused items during the judgment process. For example, the judgment unit optimizes the judgment algorithm based on the interrelationships of reused items. The judgment unit can also reflect the characteristics of specific reused items in the judgment by considering the interrelationships. Furthermore, the judgment unit can analyze the interrelationships and add relevant information to the judgment result. This improves the accuracy of the judgment by considering the interrelationships of reused items. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input data on the interrelationships of reused items into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0086] The judgment unit can make a judgment while considering the attribute information of the submitter of the reused item. For example, the judgment unit can optimize the judgment algorithm based on the submitter's attribute information. The judgment unit can also reflect the characteristics of a specific reused item in the judgment, taking the attribute information into consideration. Furthermore, the judgment unit can analyze the attribute information and add relevant information to the judgment result. This improves the accuracy of the judgment by considering the submitter's attribute information. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0087] The judgment unit can estimate the user's emotions and adjust the display order of the judgment results based on the estimated emotions. For example, if the user is nervous, the judgment unit can prioritize displaying important information. It can also prioritize displaying detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the judgment unit can prioritize displaying concise information. This allows for highly visible results by adjusting the display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0088] The judgment unit can perform judgments while considering the geographical distribution of reused items. For example, the judgment unit optimizes the judgment algorithm based on the geographical distribution of reused items. The judgment unit can also reflect the characteristics of reused items in a specific region in the judgment, taking geographical distribution into account. Furthermore, the judgment unit can analyze the geographical distribution and add region-specific information to the judgment results. This makes it possible to perform judgments that reflect region-specific information by considering geographical distribution. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input geographical distribution data of reused items into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0089] The judgment unit can improve the accuracy of its judgment by referring to relevant literature on reused items during the judgment process. For example, the judgment unit can refer to relevant literature on reused items and optimize its judgment algorithm. The judgment unit can also reflect the characteristics of specific reused items in its judgment based on the relevant literature. Furthermore, the judgment unit can analyze the relevant literature and add literature information to the judgment result. This improves the accuracy of the judgment by referring to relevant literature. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input relevant literature data into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0090] The issuing unit can estimate the user's emotions and adjust the certificate issuance method based on the estimated user emotions. For example, if the user is nervous, the issuing unit may issue a simple and highly legible certificate. If the user is relaxed, the issuing unit may also issue a certificate containing detailed information. Furthermore, if the user is in a hurry, the issuing unit may provide a concise certificate that can be issued quickly. This allows for the provision of highly legible certificates by adjusting the issuance method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the issuing unit may be performed using AI or not. For example, the issuing unit may input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The issuing unit can select the optimal issuance method by referring to past transaction data of the reused item when issuing a certificate. For example, the issuing unit can select the optimal certificate format based on past transaction data of the reused item. The issuing unit can also refer to past transaction data to determine the content of a certificate that is appropriate for a specific reused item. Furthermore, the issuing unit can analyze past transaction data and optimize the certificate issuance method. This allows the optimal issuance method to be selected by referring to past transaction data. Some or all of the above processes in the issuing unit may be performed using AI or not. For example, the issuing unit can input past transaction data into a generating AI and have the generating AI perform the optimization of the certificate issuance method.

[0092] The issuing unit can apply different issuance methods depending on the category of the reused item when issuing certificates. For example, in the case of artwork, the issuing unit can issue a certificate that includes a detailed description and images. In the case of branded goods, the issuing unit can also issue a certificate that includes the product's serial number and manufacturing information. Furthermore, in the case of electronic equipment, the issuing unit can issue a certificate that includes technical specifications and operational verification results. This allows for the issuance of appropriate certificates by applying the appropriate issuance method according to the category. Some or all of the above processing in the issuing unit may be performed using AI or not. For example, the issuing unit can input reused item category data into a generating AI and have the generating AI execute the application of different issuance methods.

[0093] The issuing unit can estimate the user's emotions and adjust the certificate display method based on the estimated emotions. For example, if the user is nervous, the issuing unit can provide a simple and highly visible display method. If the user is relaxed, the issuing unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the issuing unit can provide a concise display method. In this way, a highly visible certificate can be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the issuing unit may be performed using AI or not. For example, the issuing unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The issuing unit can select the optimal issuance method when issuing certificates, taking into account the geographical distribution of reused items. For example, the issuing unit can select the optimal certificate format based on the geographical distribution of reused items. The issuing unit can also determine the content of the certificate that is appropriate for reused items in a specific region, taking geographical distribution into consideration. Furthermore, the issuing unit can analyze geographical distribution and optimize the certificate issuance method. This allows for the issuance of certificates that reflect region-specific information by considering geographical distribution. Some or all of the above processes in the issuing unit may be performed using AI or not. For example, the issuing unit can input geographical distribution data of reused items into a generating AI and have the generating AI optimize the certificate issuance method.

[0095] The issuing unit can improve the accuracy of certificate issuance by referring to relevant literature on reused items when issuing certificates. For example, the issuing unit can refer to relevant literature on reused items and optimize the content of the certificate. The issuing unit can also determine the content of the certificate that is appropriate for a specific reused item based on the relevant literature. Furthermore, the issuing unit can analyze the relevant literature and optimize the certificate issuance method. This improves the accuracy of issuance by referring to relevant literature. Some or all of the above processes in the issuing unit may be performed using AI or not. For example, the issuing unit can input relevant literature data into a generating AI and have the generating AI perform the optimization of the certificate issuance method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception desk can estimate the user's emotions and adjust the input method for photos and tag information of reusable items based on the estimated emotions. For example, if the user is nervous, a simple and intuitive interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing for quick input of photos and tag information of reusable items. This improves user convenience by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is nervous, the accuracy of the analysis can be increased to provide reliable results. If the user is relaxed, the accuracy of the analysis can be adjusted to provide quick results. Furthermore, if the user is in a hurry, the accuracy of the analysis can be adjusted to provide quick results. In this way, reliable results can be provided by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0099] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated user emotions. For example, if the user is nervous, a strict judgment criterion can be applied to provide a reliable result. If the user is relaxed, a flexible judgment criterion can be applied to provide a quick result. Furthermore, if the user is in a hurry, a quick judgment criterion can be applied to provide a result. In this way, reliable results can be provided by adjusting the judgment criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI or not using AI. For example, the judgment unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0100] The issuing unit can estimate the user's emotions and adjust the certificate issuance method based on the estimated emotions. For example, if the user is nervous, a simple and highly visible certificate can be issued. If the user is relaxed, a certificate containing detailed information can be issued. Furthermore, if the user is in a hurry, a concise certificate that can be issued quickly can be provided. In this way, highly visible certificates can be provided by adjusting the issuance method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the issuing unit may be performed using AI or not using AI. For example, the issuing unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The judgment unit can estimate the user's emotions and adjust the display order of the judgment results based on the estimated user emotions. For example, if the user is nervous, important information can be displayed preferentially. If the user is relaxed, detailed information can be displayed preferentially. Furthermore, if the user is in a hurry, concise information can be displayed preferentially. By adjusting the display order according to the user's emotions, highly visible results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI or not using AI. For example, the judgment unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0102] The reception desk can analyze the user's past input history and provide the optimal input interface. For example, it can automatically display categories of reusable items that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest categories of reusable items that the user will use at a specific time of day based on their past input history. This improves input efficiency by providing the optimal interface based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input interface.

[0103] The reception unit can filter the input of photos and tag information for reused items based on the user's current transaction status and areas of interest. For example, it can prioritize displaying information related to reused items the user is currently trading. It can also automatically suggest relevant reused item categories based on the user's areas of interest. Furthermore, it can analyze the user's transaction history and prioritize displaying information related to reused items traded in the past. This allows for the provision of highly relevant information by filtering based on the user's transaction status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's transaction history data into a generating AI and have the generating AI perform the filtering of relevant information.

[0104] The analysis unit can optimize its analysis algorithm by referring to past transaction data of reused items during analysis. For example, it can optimize the analysis algorithm based on past transaction data of reused items to improve accuracy. It can also refer to past transaction data to reflect the characteristics of specific reused items in the analysis algorithm. Furthermore, it can analyze past transaction data and adjust the parameters of the analysis algorithm. This improves the accuracy of the analysis algorithm by referring to past transaction data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past transaction data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0105] The judgment unit can improve the accuracy of its judgment by considering the interrelationships of reused items during the judgment process. For example, it can optimize the judgment algorithm based on the interrelationships of reused items. It can also reflect the characteristics of specific reused items in the judgment by considering the interrelationships. Furthermore, it can analyze the interrelationships and add relevant information to the judgment result. As a result, the accuracy of the judgment is improved by considering the interrelationships of reused items. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input data on the interrelationships of reused items into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0106] The issuing unit can select the optimal issuance method when issuing a certificate by referring to past transaction data of the reused item. For example, it can select the optimal certificate format based on past transaction data of the reused item. It can also determine the content of a certificate suitable for a specific reused item by referring to past transaction data. Furthermore, it can analyze past transaction data and optimize the certificate issuance method. This allows the optimal issuance method to be selected by referring to past transaction data. Some or all of the above processes in the issuing unit may be performed using AI or not. For example, the issuing unit can input past transaction data into a generating AI and have the generating AI perform the optimization of the certificate issuance method.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk inputs photos and tag information of the reusable items. Users can take photos of reusable items using their smartphones and input tag information. The reception desk can also take photos of the reusable items from multiple angles and provide detailed information. For example, overhead and close-up photos can be input, and barcodes or QR codes can be input as tag information. Step 2: The analysis unit analyzes the information entered by the reception unit. It can analyze photographs of reused items using image recognition technology and analyze the internal structure of reused items using infrared illumination. For example, it can detect internal abnormalities and use AI to analyze the characteristics of reused items and provide data for determining authenticity. Step 3: The judgment unit determines authenticity based on the information analyzed by the analysis unit. Based on the analysis results, it determines whether the item is "genuine" or "fake," and it is also possible to use AI to evaluate the analysis results and determine authenticity. For example, an AI model can be used as input for the analysis results and output for authenticity. Step 4: The issuing unit issues a certificate based on the result determined by the determination unit. Based on the determination result, a digital certificate can be issued, and a paper certificate can also be issued. For example, the certificate can be printed using a printer.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, and issuance unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can input photos and tag information of reused items using the reception device 38 of the smart device 14. The analysis unit can analyze photos of reused items using image recognition technology or infrared irradiation, for example, by the identification processing unit 290 of the data processing unit 12. The determination unit can determine authenticity based on the analysis results, for example, by the identification processing unit 290 of the data processing unit 12. The issuance unit can issue digital certificates, for example, by the identification processing unit 290 of the data processing unit 12, and can also print paper certificates using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, and issuance unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can input photos and tag information of reused items using the microphone 238 of the smart glasses 214. The analysis unit can analyze photos of reused items using image recognition technology or infrared irradiation, for example, by the identification processing unit 290 of the data processing unit 12. The determination unit can determine authenticity based on the analysis results, for example, by the identification processing unit 290 of the data processing unit 12. The issuance unit can issue digital certificates, for example, by the identification processing unit 290 of the data processing unit 12, and can also print paper certificates using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, and issuance unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can input photographs and tag information of reused items using the microphone 238 of the headset terminal 314. The analysis unit can analyze photographs of reused items using image recognition technology or infrared irradiation, for example, by the identification processing unit 290 of the data processing unit 12. The determination unit can determine authenticity based on the analysis results, for example, by the identification processing unit 290 of the data processing unit 12. The issuance unit can issue digital certificates, for example, by the identification processing unit 290 of the data processing unit 12, and can also print paper certificates using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, and issuance unit, can be implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit can input photographs and tag information of reused items using the microphone 238 of the robot 414. The analysis unit can analyze photographs of reused items using image recognition technology or infrared irradiation by the identification processing unit 290 of the data processing unit 12. The determination unit can determine authenticity based on the analysis results by the identification processing unit 290 of the data processing unit 12. The issuance unit can issue digital certificates by the identification processing unit 290 of the data processing unit 12, or it can print paper certificates using the controlled object 443 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A system characterized by comprising: a reception unit for inputting photographs and tag information of reused items; an analysis unit for analyzing the information input by the reception unit; a determination unit for determining authenticity based on the information analyzed by the analysis unit; and an issuing unit for issuing a certificate based on the result determined by the determination unit. (Note 2) The system according to Appendix 1, characterized in that the analysis unit performs analysis using image recognition technology and infrared irradiation. (Note 3) The system according to Appendix 1, characterized in that the determination unit determines "authenticity" based on the analysis results. (Note 4) The aforementioned issuing department, A certificate will be issued based on the assessment results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The system described in Appendix 1 is characterized in that the reception unit inputs photographs and tag information of reused items in person-to-person transactions and pawn shops. (Note 6) The system described in Appendix 1 is characterized in that the reception unit estimates the user's emotions and adjusts the input method for photos and tag information of reused items based on the estimated user emotions. (Note 7) The system according to Appendix 1, characterized in that the reception unit analyzes the user's past input history and provides an appropriate input interface. (Note 8) The system described in Appendix 1, characterized in that the reception unit filters the user's current transaction status and areas of interest when inputting photos and tag information of reused items. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The system described in Appendix 1 is characterized in that, when the reception unit inputs photos and tag information of reused items, it prioritizes inputting information that is highly relevant based on the user's geographical location information. (Note 11) The system described in Appendix 1 is characterized in that the reception unit analyzes the user's social media activity and inputs relevant information when inputting photos and tag information of reused items. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The system described in Appendix 1 is characterized in that the analysis unit optimizes the analysis algorithm by referring to past transaction data of reused items during analysis. (Note 14) The system described in Appendix 1, characterized in that the analysis unit applies different analysis methods depending on the category of the reused item during analysis. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The system according to Appendix 1, characterized in that the analysis unit performs the analysis based on the geographical distribution of reused items during the analysis. (Note 17) The system described in Appendix 1 is characterized in that the analysis unit improves the accuracy of the analysis by referring to relevant literature on reused items during the analysis. (Note 18) The determination unit, The system estimates the user's emotions and adjusts the judgment criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The system according to Appendix 1, characterized in that the determination unit improves the accuracy of the determination based on the interrelationships of the reused items during the determination process. (Note 20) The system according to Appendix 1, characterized in that the determination unit makes a determination based on the attribute information of the person who submitted the reused item at the time of determination. (Note 21) The determination unit, The system estimates the user's emotions and adjusts the display order of the judgment results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The system according to Appendix 1, characterized in that the determination unit makes a determination based on the geographical distribution of the reused items at the time of determination. (Note 23) The system according to Appendix 1, characterized in that the determination unit improves the accuracy of the determination by referring to relevant literature on reused items during the determination process. (Note 24) The aforementioned issuing department, The system estimates the user's sentiment and adjusts the certificate issuance method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The system described in Appendix 1, characterized in that the issuing unit selects an appropriate issuance method by referring to past transaction data of reused items when issuing a certificate. (Note 26) The system described in Appendix 1, characterized in that the issuing unit applies different issuance methods depending on the category of the reused item when issuing a certificate. (Note 27) The aforementioned issuing department, It estimates the user's sentiment and adjusts how certificates are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The system described in Appendix 1, characterized in that the issuing unit selects an appropriate issuing method based on the geographical distribution of reused items when issuing a certificate. (Note 29) The system described in Appendix 1 is characterized in that the issuing unit improves the accuracy of issuance by referring to relevant literature on reused items when issuing certificates. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A system characterized by comprising: a reception unit for inputting photographs and tag information of reused items; an analysis unit for analyzing the information input by the reception unit; a determination unit for determining authenticity based on the information analyzed by the analysis unit; and an issuing unit for issuing a certificate based on the result determined by the determination unit.

2. The system according to claim 1, characterized in that the analysis unit performs analysis using image recognition technology and infrared irradiation.

3. The system according to claim 1, characterized in that the determination unit determines authenticity based on the analysis results.

4. The aforementioned issuing department, A certificate will be issued based on the assessment results. The system according to feature 1.

5. The system according to claim 1, characterized in that the reception unit inputs photographs and tag information of reused items in person-to-person transactions and pawn shops.

6. The system according to claim 1, characterized in that the reception unit estimates the user's emotions and adjusts the input method for photos and tag information of reused items based on the estimated user emotions.

7. The system according to claim 1, characterized in that the reception unit analyzes the user's past input history and provides an appropriate input interface.

8. The system according to claim 1, characterized in that the reception unit filters the user's current transaction status and areas of interest when inputting photos and tag information of reused items.

9. The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system according to feature 1.

10. The system according to claim 1, characterized in that the reception unit prioritizes inputting highly relevant information based on the user's geographical location information when inputting photos and tag information of reused items.

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