system

Abstract

The system according to this embodiment aims to determine the authenticity of goods and prevent product substitution. [Solution] The system according to the embodiment comprises a collection unit, a determination unit, a storage unit, a guarantee unit, and a verification unit. The collection unit collects external information such as product photos, descriptions, and websites. The determination unit analyzes the information collected by the collection unit to determine the authenticity of the product. The storage unit stores data of the product's condition at the time of shipment. The guarantee unit uses a shipment guarantee seal or tab. The verification unit checks for tampering upon return.

Description

Technical Field

[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

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the authenticity determination of products and the prevention of product substitution are not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to determine the authenticity of products and prevent product substitution.

Means for Solving the Problems

[0006] ​The system according to this embodiment comprises a collection unit, a determination unit, a storage unit, a guarantee unit, and a verification unit. The collection unit collects external information such as product photos, descriptions, and websites. The determination unit analyzes the information collected by the collection unit to determine the authenticity of the product. The storage unit stores data of the product's condition at the time of shipment. The guarantee unit uses a shipment guarantee seal or tab. The verification unit checks for tampering upon return. [Effects of the Invention]

[0007] The system according to this embodiment can determine the authenticity of goods and prevent product substitution. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 verification and product substitution prevention system according to an embodiment of the present invention is a system that uses an AI agent to verify the authenticity of products and further prevents product substitution. This system verifies the authenticity of shipped products by referring to external information such as photos, descriptions, and websites, and greatly contributes to improving the reliability of flea market transactions. It also functions as a product substitution prevention system. When shipping a product, the shipping process is photographed with a dedicated app, the AI ​​analyzes the condition of the product at the time of shipping, saves the condition as data, attaches a shipping guarantee sticker or tab (a sticker or tab with a 2D code (e.g., QR code®)) to any part of the product, photographs the attached area with the dedicated app, and then ships the product to the buyer. If the buyer returns the product, they must return it without removing the shipping guarantee sticker or tab. When the seller receives the returned product, they can read the location of the shipping guarantee sticker with the dedicated app, and the AI ​​agent can verify whether or not it has been tampered with. For example, the authenticity verification and product substitution prevention system collects external information such as photos, descriptions, and websites of shipped products, and AI analyzes this information to determine the authenticity of the product. Next, the shipping process is photographed using a dedicated app, and the AI ​​analyzes the product's condition at the time of shipment and saves the data. A shipping guarantee sticker or tab is attached to any part of the product, photographed with the dedicated app, and then the product is shipped to the buyer. If the buyer returns the product, they must return it with the shipping guarantee sticker or tab still attached. When the seller receives the returned product, the dedicated app reads the location of the shipping guarantee sticker, and the AI ​​checks for tampering. This system accurately determines the authenticity of products and prevents product substitution. This improves the reliability of flea market transactions and reduces transaction problems. In short, the authenticity verification and product substitution prevention system can determine the authenticity of products and prevent product substitution.

[0029] The authenticity authentication and product substitution prevention system according to this embodiment comprises a collection unit, a determination unit, a storage unit, a guarantee unit, and a verification unit. The collection unit collects external information such as product photos, descriptions, and websites. For example, the collection unit takes product photos, inputs descriptions, and obtains information from websites. The collection unit inputs this information into an AI and can use it as data for analysis. The determination unit analyzes the information collected by the collection unit to determine the authenticity of the product. For example, the determination unit analyzes product photos using image analysis technology to determine authenticity. The determination unit can also analyze descriptions using text analysis technology to determine authenticity. Furthermore, the determination unit can use a machine learning algorithm to determine authenticity based on the collected information. The storage unit stores data of the product's condition at the time of shipment. For example, the storage unit takes photos of the product's appearance and packaging and stores them as data. The storage unit can also store information such as the product's serial number and shipment date and time in a database. The guarantee unit uses a shipment guarantee seal or tab. The warranty unit, for example, attaches a shipping warranty sticker to any location on the product and photographs it with a dedicated app. The warranty unit can also attach a tab with a 2D code (e.g., a QR code) to any location on the product and photograph it with a dedicated app. The verification unit checks for tampering when a product is returned. The verification unit, for example, checks the condition of the shipping warranty sticker on the returned product and determines whether it has been tampered with. The verification unit can also check the condition of the tab on the returned product and determine whether it has been tampered with. As a result, the authenticity authentication and product substitution prevention system according to this embodiment can determine the authenticity of a product and prevent product substitution.

[0030] The data collection unit gathers external information such as product photos, descriptions, and websites. Specifically, it takes photos of products with a high-resolution camera and acquires detailed images from multiple angles. This allows for accurate recording of even the smallest details of the product, such as scratches and markings. The descriptions include detailed information about the product's features, specifications, and manufacturer, and are entered as text data. Furthermore, information such as product sales pages, reviews, and related news articles are automatically obtained from websites using scraping technology. This information is input into the AI ​​and used as data for analysis. The AI ​​analyzes product photos using image recognition technology and extracts features. For example, it identifies features such as the product's logo, markings, shape, and color, and compares them with information on genuine products stored in the database. It also analyzes the descriptions using text analysis technology to verify whether the product's features and specifications match those of genuine products. In this way, the data collection unit can efficiently collect detailed product information and provide data for authenticity determination.

[0031] The judgment unit analyzes the information collected by the collection unit to determine the authenticity of a product. Specifically, it uses image analysis technology to analyze product photos and determine authenticity. For example, the AI ​​extracts features from product photos and compares them with the features of genuine products stored in a database. This verifies whether the product's logo, markings, shape, and color match those of a genuine product. It also uses text analysis technology to analyze the product description and verify whether the product's features and specifications match those of a genuine product. For example, it verifies whether information such as the manufacturer, manufacturing date, and serial number listed in the product description matches those of a genuine product. Furthermore, the judgment unit uses machine learning algorithms to determine the authenticity of a product based on the collected information. For example, it uses an AI model trained on past authenticity determination data to analyze product features and the content of the description to determine authenticity. This allows the judgment unit to determine the authenticity of a product with high accuracy and efficiency.

[0032] The storage unit stores data on the product's condition at the time of shipment. Specifically, it photographs the product's appearance and packaging and saves the data. For example, it uses a high-resolution camera to photograph the overall appearance and details of the product and saves the image data. It also photographs the product's packaging to accurately record its condition at the time of shipment. Furthermore, the storage unit stores information such as the product's serial number and shipment date and time in a database. This allows for a detailed record of the product's condition at the time of shipment, which can be used for later verification and troubleshooting. The saved data is stored on a cloud server and can be accessed as needed. This allows the storage unit to accurately record the product's condition at the time of shipment, which can be used for later verification and troubleshooting.

[0033] The warranty department uses either a shipping guarantee sticker or a tab. Specifically, they attach the shipping guarantee sticker to any location on the product and photograph it with a dedicated app. The shipping guarantee sticker uses special printing technology and holograms to prevent counterfeiting and cannot be easily removed. The warranty department can also attach a tab with a 2D code to any location on the product and photograph it with a dedicated app. The 2D code contains information such as the product's serial number and shipping date, and by scanning it with the dedicated app, they can verify the product's condition at the time of shipment. This allows the warranty department to accurately record the product's condition at the time of shipment, which is useful for later verification and troubleshooting.

[0034] The verification unit checks for tampering upon return. Specifically, it checks the condition of the shipping guarantee seal on the returned product to determine if tampering has occurred. For example, it checks whether the shipping guarantee seal has been peeled off or damaged. The verification unit can also check the condition of the tab on the returned product to determine if tampering has occurred. For example, it checks whether the tab with the 2D code is damaged and whether the 2D code is readable. This allows the verification unit to accurately determine whether the returned product has been tampered with and prevent product substitution. Furthermore, the verification unit takes another photograph of the returned product's condition and saves it to a database. This allows for a detailed record of the product's condition at the time of return, which can be used for subsequent verification and troubleshooting. This allows the verification unit to accurately determine whether the product has been tampered with and prevent product substitution.

[0035] The collection unit can collect external information such as product photos, descriptions, and websites. For example, the collection unit can take product photos, input descriptions, and retrieve information from websites. The collection unit can input this information into AI and use it as data for analysis. This allows the collection unit to collect the information necessary for determining the authenticity of a product. Some or all of the above processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can take product photos, input them into AI, and perform analysis.

[0036] The judgment unit can analyze the collected information to determine the authenticity of a product. For example, the judgment unit can analyze a photograph of a product using image analysis technology to determine its authenticity. The judgment unit can also analyze a description using text analysis technology to determine its authenticity. The judgment unit can also use a machine learning algorithm to determine the authenticity of a product based on the collected information. This allows the judgment unit to determine the authenticity of a product based on the collected information. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the collected information into AI and perform authenticity determination.

[0037] The storage unit can save data on the product's condition at the time of shipment. For example, the storage unit can photograph the product's appearance and packaging and save it as data. The storage unit can also save information such as the product's serial number and shipment date and time in a database. This allows the product's condition at the time of shipment to be saved as data. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can photograph the product's appearance, input it into the AI, and save the data.

[0038] The warranty department can use shipping warranty stickers or tabs. For example, the warranty department can attach a shipping warranty sticker to any location on the product and photograph it with a dedicated app. The warranty department can also attach a tab with a QR code to any location on the product and photograph it with a dedicated app. This prevents product substitution by using shipping warranty stickers or tabs. Some or all of the above processes in the warranty department may be performed using AI, for example, or without AI. For example, the warranty department can input an image of a product with a shipping warranty sticker attached into AI and perform analysis.

[0039] The verification unit can check for tampering when a product is returned. For example, the verification unit checks the condition of the shipping guarantee seal on the returned product to determine if it has been tampered with. The verification unit can also check the condition of the tab on the returned product to determine if it has been tampered with. This prevents product substitution by checking for tampering when a product is returned. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input an image of the returned product into the AI ​​to determine if it has been tampered with.

[0040] The data collection unit can analyze the past transaction history of a product and optimize the scope of information it collects. For example, the data collection unit can prioritize collecting information on products that have been frequently counterfeited in the past. For example, the data collection unit can focus on collecting information about specific sellers from past transaction history. For example, the data collection unit can broadly collect information about products in a specific category based on past transaction history. This allows the scope of information collected to be optimized by analyzing the past transaction history of a product. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past transaction history data into AI to optimize the scope of information.

[0041] The data collection unit can apply different collection algorithms depending on the product category. For example, the data collection unit can apply an algorithm to collect detailed information for expensive brand-name goods. For example, the data collection unit can also apply an algorithm to collect information quickly for common everyday items. For example, the data collection unit can apply an algorithm to collect data from specialized information sources for products in a specific category. By applying different collection algorithms depending on the product category, more appropriate information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input an algorithm corresponding to the product category into AI and perform information collection.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of the product. For example, if the product is traded frequently in a particular region, the data collection unit will prioritize the collection of information from that region. The data collection unit can also collect relevant information based on the geographical location of the product's origin. For example, the data collection unit can collect information on the sales region of the product and prioritize the analysis of highly relevant data. This allows for the priority collection of highly relevant information by considering the geographical location of the product. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of the product into AI and perform data collection.

[0043] The data collection unit can collect information from social media and use it to determine the authenticity of a product. For example, the data collection unit can collect the reputation of a product on social media and use it to determine its authenticity. The data collection unit can also analyze user comments on social media and evaluate the reliability of a product. The data collection unit can also collect images of a product on social media and use them to determine its authenticity. In this way, by collecting information from social media, it can be used to determine the authenticity of a product. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information from social media into AI and perform authenticity determination.

[0044] The judgment unit can adjust the level of detail in its judgment based on the importance of the product. For example, the judgment unit can perform a detailed judgment for expensive products. For example, the judgment unit can also perform a rapid judgment for general products. For example, the judgment unit can perform a specialized judgment for products in a specific category. By adjusting the level of detail in the judgment based on the importance of the product, a more appropriate judgment can be made. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input product importance data into AI and adjust the level of detail in its judgment.

[0045] The judgment unit can apply different judgment algorithms depending on the product category. For example, the judgment unit can apply a detailed judgment algorithm to expensive brand-name goods. For example, the judgment unit can apply a rapid judgment algorithm to general daily necessities. For example, the judgment unit can apply a specialized judgment algorithm to products in a specific category. By applying different judgment algorithms depending on the product category, more appropriate judgments can be made. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input an algorithm corresponding to the product category into AI and perform the judgment.

[0046] The judgment unit can determine the priority of judgments based on the product submission date. For example, the judgment unit may prioritize judging products with approaching deadlines. The judgment unit may also quickly judge products with earlier submission dates. The judgment unit may also dynamically adjust the priority of judgments based on the submission date. This allows for more appropriate judgments by determining the priority of judgments based on the product submission date. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input product submission date data into AI to determine the priority of judgments.

[0047] The judgment unit can adjust the order of the judgment results based on the relevance of the products. For example, the judgment unit may prioritize displaying the judgment results of highly relevant products. For example, the judgment unit may also postpone displaying the judgment results of less relevant products. The judgment unit can also dynamically adjust the order of the judgment results based on relevance. This allows for the provision of more appropriate judgment results by adjusting the order of the judgment results based on the relevance of the products. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input product relevance data into AI and adjust the order of the judgment results.

[0048] The storage unit can optimize the storage algorithm by referring to past stored data of the product. For example, the storage unit applies the optimal storage algorithm based on previously stored data. The storage unit can also adjust the data storage method by referring to past stored data. The storage unit can also optimize the storage algorithm by analyzing past stored data. This allows the storage algorithm to be optimized by referring to past stored data of the product. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input past stored data into AI to optimize the storage algorithm.

[0049] The storage unit can apply different storage methods depending on the product category. For example, the storage unit can apply a detailed storage method to expensive brand-name goods. For example, the storage unit can apply a rapid storage method to common daily necessities. For example, the storage unit can apply a specialized storage method to products in a specific category. This allows for the application of the optimal storage method according to the product category. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input a storage method according to the product category into AI and perform storage.

[0050] The storage unit can prioritize saving highly relevant data, taking into account the geographical location information of the products. For example, if a product is traded frequently in a particular region, the storage unit will prioritize saving data from that region. The storage unit can also save relevant data based on the geographical location information of the product's origin. For example, the storage unit can save data related to the sales region of a product and prioritize the analysis of highly relevant data. This allows for the priority saving of highly relevant data by considering the geographical location information of the products. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the geographical location information of the products into AI and perform data storage.

[0051] The storage unit can store information from social media and use it to determine the authenticity of a product. For example, the storage unit can store the reputation of a product on social media and use it to determine its authenticity. The storage unit can also store user comments on social media and evaluate the reliability of a product. The storage unit can also store images of a product on social media and use them to determine its authenticity. In this way, by storing information from social media, it can be used to determine the authenticity of a product. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input information from social media into AI and perform authenticity determination.

[0052] The warranty department can adjust the level of detail on the warranty seal based on the importance of the product. For example, the warranty department may affix a detailed warranty seal to expensive products. For example, the warranty department may affix a warranty seal quickly to general products. For example, the warranty department may affix a specialized warranty seal to products in a specific category. By adjusting the level of detail on the warranty seal based on the importance of the product, a more appropriate warranty seal can be affixed. Some or all of the above processing in the warranty department may be performed using AI, for example, or not using AI. For example, the warranty department can input product importance data into AI and adjust the level of detail on the warranty seal.

[0053] The warranty department can apply different warranty methods depending on the product category. For example, the warranty department may apply a detailed warranty method to expensive branded goods. For example, the warranty department may apply a quick warranty method to general daily necessities. For example, the warranty department may apply a specialized warranty method to products in a specific category. This allows for the application of the most appropriate warranty method for each product category. Some or all of the above processes in the warranty department may be performed using AI, for example, or not using AI. For example, the warranty department can input warranty methods appropriate to the product category into AI and then provide a warranty.

[0054] The warranty department can determine the priority of warranty seals based on the product submission date. For example, the warranty department may prioritize applying warranty seals to products with approaching deadlines. The warranty department may also quickly apply warranty seals to products with earlier submission dates. The warranty department may also dynamically adjust the priority of warranty seals based on the submission date. This allows for the application of more appropriate warranty seals by determining the priority of warranty seals based on the product submission date. Some or all of the above processes in the warranty department may be performed using AI, for example, or not using AI. For example, the warranty department can input product submission date data into AI to determine the priority of warranty seals.

[0055] The warranty unit can adjust the order of warranty seals based on the relevance of the products. For example, the warranty unit may prioritize applying warranty seals to highly relevant products. For example, the warranty unit may postpone applying warranty seals to less relevant products. The warranty unit may also dynamically adjust the order of warranty seals based on relevance. This allows for the application of more appropriate warranty seals by adjusting the order of warranty seals based on the relevance of the products. Some or all of the above processing in the warranty unit may be performed using AI, for example, or without AI. For example, the warranty unit can input product relevance data into AI and adjust the order of warranty seals.

[0056] The verification unit can optimize its verification algorithm by referring to the product's past tampering history. For example, the verification unit can focus on verifying products that have been frequently tampered with in the past. The verification unit can also adjust its verification algorithm based on the past tampering history. For example, the verification unit can analyze the past tampering history and apply the most suitable verification algorithm. This allows the verification algorithm to be optimized by referring to the product's past tampering history. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input past tampering history data into AI to optimize its verification algorithm.

[0057] The verification unit can apply different verification methods depending on the product category. For example, the verification unit can apply a detailed verification method to expensive brand-name goods. For example, the verification unit can apply a quick verification method to general daily necessities. For example, the verification unit can apply a specialized verification method to products in a specific category. This allows the application of the most appropriate verification method for each product category. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input a verification method appropriate to the product category into AI and perform the verification.

[0058] The verification unit can prioritize verifications that are highly relevant, taking into account the geographical location information of the product. For example, if the product is traded frequently in a particular region, the verification unit will prioritize verifications in that region. The verification unit can also perform relevant verifications based on the geographical location information of the product's origin. For example, the verification unit can perform verifications related to the product's sales region and prioritize verifications that are highly relevant. In this way, by considering the geographical location information of the product, it is possible to prioritize verifications that are highly relevant. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the geographical location information of the product into AI and perform verifications.

[0059] The verification unit can improve the accuracy of tampering verification by referring to information from social media. For example, the verification unit can improve the accuracy of tampering verification by referring to the reputation of the product on social media. The verification unit can also evaluate the reliability of the product by referring to user comments on social media. The verification unit can also improve the accuracy of tampering verification by referring to images of the product on social media. In this way, the accuracy of tampering verification can be improved by referring to information from social media. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input information from social media into AI and perform tampering verification.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The collection unit can collect external information such as product photos, descriptions, and websites. For example, the collection unit can take product photos, input descriptions, and retrieve information from websites. The collection unit can input this information into AI and use it as data for analysis. This allows the collection unit to collect the information necessary for determining the authenticity of a product. Some or all of the above processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can take product photos, input them into AI, and perform analysis.

[0062] The judgment unit can analyze the collected information to determine the authenticity of a product. For example, the judgment unit can analyze a photograph of a product using image analysis technology to determine its authenticity. The judgment unit can also analyze a description using text analysis technology to determine its authenticity. The judgment unit can also use a machine learning algorithm to determine the authenticity of a product based on the collected information. This allows the judgment unit to determine the authenticity of a product based on the collected information. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the collected information into AI and perform authenticity determination.

[0063] The storage unit can save data on the product's condition at the time of shipment. For example, the storage unit can photograph the product's appearance and packaging and save it as data. The storage unit can also save information such as the product's serial number and shipment date and time in a database. This allows the product's condition at the time of shipment to be saved as data. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can photograph the product's appearance, input it into the AI, and save the data.

[0064] The warranty department can use shipping warranty stickers or tabs. For example, the warranty department can attach a shipping warranty sticker to any location on the product and photograph it with a dedicated app. The warranty department can also attach a tab with a QR code to any location on the product and photograph it with a dedicated app. This prevents product substitution by using shipping warranty stickers or tabs. Some or all of the above processes in the warranty department may be performed using AI, for example, or without AI. For example, the warranty department can input an image of a product with a shipping warranty sticker attached into AI and perform analysis.

[0065] The verification unit can check for tampering when a product is returned. For example, the verification unit checks the condition of the shipping guarantee seal on the returned product to determine if it has been tampered with. The verification unit can also check the condition of the tab on the returned product to determine if it has been tampered with. This prevents product substitution by checking for tampering when a product is returned. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input an image of the returned product into the AI ​​to determine if it has been tampered with.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects external information such as product photos, descriptions, and websites. For example, it takes product photos, inputs descriptions, and retrieves information from websites. The data collection unit inputs this information into the AI, which can then be used as data for analysis. Step 2: The judgment unit analyzes the information collected by the collection unit to determine the authenticity of the product. For example, it can use image analysis technology to analyze a photograph of the product and determine its authenticity. It can also use text analysis technology to analyze the description and determine the authenticity of the product. Furthermore, it can use machine learning algorithms to determine the authenticity of the product based on the collected information. Step 3: The storage unit saves data on the product's condition at the time of shipment. For example, it takes photos of the product's appearance and packaging and saves them as data. It can also save information such as the product's serial number and shipping date and time in a database. Step 4: The warranty section uses a shipping warranty sticker or tab. For example, attach a shipping warranty sticker to any location on the product and photograph it with the dedicated app. Alternatively, you can attach a tab with a QR code to any location on the product and photograph it with the dedicated app. Step 5: The verification unit checks for tampering upon return. For example, it checks the condition of the shipping guarantee seal on the returned product to determine if tampering has occurred. It can also check the condition of the tab on the returned product to determine if tampering has occurred.

[0068] (Example of form 2) The authenticity verification and product substitution prevention system according to an embodiment of the present invention is a system that uses an AI agent to verify the authenticity of products and prevent product substitution. This system verifies the authenticity of shipped products by referring to external information such as photos, descriptions, and websites, and greatly contributes to improving the reliability of transactions on flea markets. It also functions as a product substitution prevention system. When shipping a product, the shipping process is photographed with a dedicated app, the AI ​​analyzes the condition of the product at the time of shipping, saves the condition as data, attaches a shipping guarantee sticker or tab (a sticker or tab with a 2D code (e.g., QR code)) to any part of the product, photographs the attached area with the dedicated app, and then ships the product to the buyer. If the buyer returns the product, they must return it without removing the shipping guarantee sticker or tab. When the seller receives the returned product, they can read the location of the shipping guarantee sticker with the dedicated app, and the AI ​​agent can verify whether or not it has been tampered with. For example, the authenticity verification and product substitution prevention system collects external information such as photos, descriptions, and websites of shipped products, and AI analyzes this information to determine the authenticity of the product. Next, the shipping process is photographed using a dedicated app, and the AI ​​analyzes the product's condition at the time of shipment and saves the data. A shipping guarantee sticker or tab is attached to any part of the product, photographed with the dedicated app, and then the product is shipped to the buyer. If the buyer returns the product, they must return it with the shipping guarantee sticker or tab still attached. When the seller receives the returned product, the dedicated app reads the location of the shipping guarantee sticker, and the AI ​​checks for tampering. This system accurately determines the authenticity of products and prevents product substitution. This improves the reliability of flea market transactions and reduces transaction problems. In short, the authenticity verification and product substitution prevention system can determine the authenticity of products and prevent product substitution.

[0069] The authenticity authentication and product substitution prevention system according to this embodiment comprises a collection unit, a determination unit, a storage unit, a guarantee unit, and a verification unit. The collection unit collects external information such as product photos, descriptions, and websites. For example, the collection unit takes product photos, inputs descriptions, and obtains information from websites. The collection unit inputs this information into an AI and can use it as data for analysis. The determination unit analyzes the information collected by the collection unit to determine the authenticity of the product. For example, the determination unit analyzes product photos using image analysis technology to determine authenticity. The determination unit can also analyze descriptions using text analysis technology to determine authenticity. Furthermore, the determination unit can use a machine learning algorithm to determine authenticity based on the collected information. The storage unit stores data of the product's condition at the time of shipment. For example, the storage unit takes photos of the product's appearance and packaging and stores them as data. The storage unit can also store information such as the product's serial number and shipment date and time in a database. The guarantee unit uses a shipment guarantee seal or tab. The warranty unit, for example, attaches a shipping warranty sticker to any location on the product and photographs it with a dedicated app. The warranty unit can also attach a tab with a 2D code (e.g., a QR code) to any location on the product and photograph it with a dedicated app. The verification unit checks for tampering when a product is returned. The verification unit, for example, checks the condition of the shipping warranty sticker on the returned product and determines whether it has been tampered with. The verification unit can also check the condition of the tab on the returned product and determine whether it has been tampered with. As a result, the authenticity authentication and product substitution prevention system according to this embodiment can determine the authenticity of a product and prevent product substitution.

[0070] The data collection unit gathers external information such as product photos, descriptions, and websites. Specifically, it takes photos of products with a high-resolution camera and acquires detailed images from multiple angles. This allows for accurate recording of even the smallest details of the product, such as scratches and markings. The descriptions include detailed information about the product's features, specifications, and manufacturer, and are entered as text data. Furthermore, information such as product sales pages, reviews, and related news articles are automatically obtained from websites using scraping technology. This information is input into the AI ​​and used as data for analysis. The AI ​​analyzes product photos using image recognition technology and extracts features. For example, it identifies features such as the product's logo, markings, shape, and color, and compares them with information on genuine products stored in the database. It also analyzes the descriptions using text analysis technology to verify whether the product's features and specifications match those of genuine products. In this way, the data collection unit can efficiently collect detailed product information and provide data for authenticity determination.

[0071] The judgment unit analyzes the information collected by the collection unit to determine the authenticity of a product. Specifically, it uses image analysis technology to analyze product photos and determine authenticity. For example, the AI ​​extracts features from product photos and compares them with the features of genuine products stored in a database. This verifies whether the product's logo, markings, shape, and color match those of a genuine product. It also uses text analysis technology to analyze the product description and verify whether the product's features and specifications match those of a genuine product. For example, it verifies whether information such as the manufacturer, manufacturing date, and serial number listed in the product description matches those of a genuine product. Furthermore, the judgment unit uses machine learning algorithms to determine the authenticity of a product based on the collected information. For example, it uses an AI model trained on past authenticity determination data to analyze product features and the content of the description to determine authenticity. This allows the judgment unit to determine the authenticity of a product with high accuracy and efficiency.

[0072] The storage unit stores data on the product's condition at the time of shipment. Specifically, it photographs the product's appearance and packaging and saves the data. For example, it uses a high-resolution camera to photograph the overall appearance and details of the product and saves the image data. It also photographs the product's packaging to accurately record its condition at the time of shipment. Furthermore, the storage unit stores information such as the product's serial number and shipment date and time in a database. This allows for a detailed record of the product's condition at the time of shipment, which can be used for later verification and troubleshooting. The saved data is stored on a cloud server and can be accessed as needed. This allows the storage unit to accurately record the product's condition at the time of shipment, which can be used for later verification and troubleshooting.

[0073] The warranty department uses either a shipping guarantee sticker or a tab. Specifically, they attach the shipping guarantee sticker to any location on the product and photograph it with a dedicated app. The shipping guarantee sticker uses special printing technology and holograms to prevent counterfeiting and cannot be easily removed. The warranty department can also attach a tab with a 2D code to any location on the product and photograph it with a dedicated app. The 2D code contains information such as the product's serial number and shipping date, and by scanning it with the dedicated app, they can verify the product's condition at the time of shipment. This allows the warranty department to accurately record the product's condition at the time of shipment, which is useful for later verification and troubleshooting.

[0074] The verification unit checks for tampering upon return. Specifically, it checks the condition of the shipping guarantee seal on the returned product to determine if tampering has occurred. For example, it checks whether the shipping guarantee seal has been peeled off or damaged. The verification unit can also check the condition of the tab on the returned product to determine if tampering has occurred. For example, it checks whether the tab with the 2D code is damaged and whether the 2D code is readable. This allows the verification unit to accurately determine whether the returned product has been tampered with and prevent product substitution. Furthermore, the verification unit takes another photograph of the returned product's condition and saves it to a database. This allows for a detailed record of the product's condition at the time of return, which can be used for subsequent verification and troubleshooting. This allows the verification unit to accurately determine whether the product has been tampered with and prevent product substitution.

[0075] The collection unit can collect external information such as product photos, descriptions, and websites. For example, the collection unit can take product photos, input descriptions, and retrieve information from websites. The collection unit can input this information into AI and use it as data for analysis. This allows the collection unit to collect the information necessary for determining the authenticity of a product. Some or all of the above processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can take product photos, input them into AI, and perform analysis.

[0076] The judgment unit can analyze the collected information to determine the authenticity of a product. For example, the judgment unit can analyze a photograph of a product using image analysis technology to determine its authenticity. The judgment unit can also analyze a description using text analysis technology to determine its authenticity. The judgment unit can also use a machine learning algorithm to determine the authenticity of a product based on the collected information. This allows the judgment unit to determine the authenticity of a product based on the collected information. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the collected information into AI and perform authenticity determination.

[0077] The storage unit can save data on the product's condition at the time of shipment. For example, the storage unit can photograph the product's appearance and packaging and save it as data. The storage unit can also save information such as the product's serial number and shipment date and time in a database. This allows the product's condition at the time of shipment to be saved as data. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can photograph the product's appearance, input it into the AI, and save the data.

[0078] The warranty department can use shipping warranty stickers or tabs. For example, the warranty department can attach a shipping warranty sticker to any location on the product and photograph it with a dedicated app. The warranty department can also attach a tab with a QR code to any location on the product and photograph it with a dedicated app. This prevents product substitution by using shipping warranty stickers or tabs. Some or all of the above processes in the warranty department may be performed using AI, for example, or without AI. For example, the warranty department can input an image of a product with a shipping warranty sticker attached into AI and perform analysis.

[0079] The verification unit can check for tampering when a product is returned. For example, the verification unit checks the condition of the shipping guarantee seal on the returned product to determine if it has been tampered with. The verification unit can also check the condition of the tab on the returned product to determine if it has been tampered with. This prevents product substitution by checking for tampering when a product is returned. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input an image of the returned product into the AI ​​to determine if it has been tampered with.

[0080] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize the collection of important information and analyze it quickly. For example, if the user is anxious, the data collection unit can also prioritize the collection of data from reliable sources. For example, if the user is relaxed, the data collection unit can collect a wide range of information and perform detailed analysis. This allows for the collection of more relevant information by prioritizing the information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI to determine the priority of information.

[0081] The data collection unit can analyze the past transaction history of a product and optimize the scope of information it collects. For example, the data collection unit can prioritize collecting information on products that have been frequently counterfeited in the past. For example, the data collection unit can focus on collecting information about specific sellers from past transaction history. For example, the data collection unit can broadly collect information about products in a specific category based on past transaction history. This allows the scope of information collected to be optimized by analyzing the past transaction history of a product. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past transaction history data into AI to optimize the scope of information.

[0082] The data collection unit can apply different collection algorithms depending on the product category. For example, the data collection unit can apply an algorithm to collect detailed information for expensive brand-name goods. For example, the data collection unit can also apply an algorithm to collect information quickly for common everyday items. For example, the data collection unit can apply an algorithm to collect data from specialized information sources for products in a specific category. By applying different collection algorithms depending on the product category, more appropriate information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input an algorithm corresponding to the product category into AI and perform information collection.

[0083] The data collection unit can estimate the user's emotions and adjust the level of detail of the information it collects based on the estimated emotions. For example, if the user is excited, the data collection unit can collect detailed information and analyze it quickly. For example, if the user is feeling anxious, the data collection unit can also collect detailed data from reliable sources. For example, if the user is relaxed, the data collection unit can also collect a wide range of information and perform detailed analysis. This allows for the collection of more appropriate information by adjusting the level of detail of the information collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI and adjust the level of detail of the information.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of the product. For example, if the product is traded frequently in a particular region, the data collection unit will prioritize the collection of information from that region. The data collection unit can also collect relevant information based on the geographical location of the product's origin. For example, the data collection unit can collect information on the sales region of the product and prioritize the analysis of highly relevant data. This allows for the priority collection of highly relevant information by considering the geographical location of the product. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of the product into AI and perform data collection.

[0085] The data collection unit can collect information from social media and use it to determine the authenticity of a product. For example, the data collection unit can collect the reputation of a product on social media and use it to determine its authenticity. The data collection unit can also analyze user comments on social media and evaluate the reliability of a product. The data collection unit can also collect images of a product on social media and use them to determine its authenticity. In this way, by collecting information from social media, it can be used to determine the authenticity of a product. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information from social media into AI and perform authenticity determination.

[0086] The judgment unit can estimate the user's emotions and adjust the way the judgment result is presented based on the estimated user emotions. For example, if the user is excited, the judgment unit provides a concise and clear judgment result. For example, if the user is feeling anxious, the judgment unit may also provide a judgment result that includes a detailed explanation. For example, if the user is relaxed, the judgment unit may also provide a visually easy-to-understand judgment result. By adjusting the way the judgment result is presented based on the user's emotions, a more appropriate judgment result 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, for example, or without AI. For example, the judgment unit can input user emotion data into AI and adjust the way the judgment result is presented.

[0087] The judgment unit can adjust the level of detail in its judgment based on the importance of the product. For example, the judgment unit can perform a detailed judgment for expensive products. For example, the judgment unit can also perform a rapid judgment for general products. For example, the judgment unit can perform a specialized judgment for products in a specific category. By adjusting the level of detail in the judgment based on the importance of the product, a more appropriate judgment can be made. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input product importance data into AI and adjust the level of detail in its judgment.

[0088] The judgment unit can apply different judgment algorithms depending on the product category. For example, the judgment unit can apply a detailed judgment algorithm to expensive brand-name goods. For example, the judgment unit can apply a rapid judgment algorithm to general daily necessities. For example, the judgment unit can apply a specialized judgment algorithm to products in a specific category. By applying different judgment algorithms depending on the product category, more appropriate judgments can be made. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input an algorithm corresponding to the product category into AI and perform the judgment.

[0089] The judgment unit can estimate the user's emotions and determine the priority of judgment results based on the estimated user emotions. For example, if the user is excited, the judgment unit will prioritize displaying important judgment results. For example, if the user is feeling anxious, the judgment unit may also prioritize displaying reliable judgment results. For example, if the user is relaxed, the judgment unit may also display a wide range of judgment results. This allows for the provision of more appropriate judgment results by prioritizing judgment results based on 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, for example, or without AI. For example, the judgment unit can input user emotion data into AI to determine the priority of judgment results.

[0090] The judgment unit can determine the priority of judgments based on the product submission date. For example, the judgment unit may prioritize judging products with approaching deadlines. The judgment unit may also quickly judge products with earlier submission dates. The judgment unit may also dynamically adjust the priority of judgments based on the submission date. This allows for more appropriate judgments by determining the priority of judgments based on the product submission date. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input product submission date data into AI to determine the priority of judgments.

[0091] The judgment unit can adjust the order of the judgment results based on the relevance of the products. For example, the judgment unit may prioritize displaying the judgment results of highly relevant products. For example, the judgment unit may also postpone displaying the judgment results of less relevant products. The judgment unit can also dynamically adjust the order of the judgment results based on relevance. This allows for the provision of more appropriate judgment results by adjusting the order of the judgment results based on the relevance of the products. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input product relevance data into AI and adjust the order of the judgment results.

[0092] The storage unit can estimate the user's emotions and determine the priority of data to store based on the estimated user emotions. For example, if the user is excited, the storage unit may prioritize storing important data. For example, if the user is feeling anxious, the storage unit may prioritize storing reliable data. For example, if the user is relaxed, the storage unit may store a wide range of data. This allows for the storage of more appropriate data by prioritizing data storage based on 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 storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input user emotion data into AI and determine the priority of data to store.

[0093] The storage unit can optimize the storage algorithm by referring to past stored data of the product. For example, the storage unit applies the optimal storage algorithm based on previously stored data. The storage unit can also adjust the data storage method by referring to past stored data. The storage unit can also optimize the storage algorithm by analyzing past stored data. This allows the storage algorithm to be optimized by referring to past stored data of the product. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input past stored data into AI to optimize the storage algorithm.

[0094] The storage unit can apply different storage methods depending on the product category. For example, the storage unit can apply a detailed storage method to expensive brand-name goods. For example, the storage unit can apply a rapid storage method to common daily necessities. For example, the storage unit can apply a specialized storage method to products in a specific category. This allows for the application of the optimal storage method according to the product category. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input a storage method according to the product category into AI and perform storage.

[0095] The storage unit can estimate the user's emotions and adjust the level of detail of the stored data based on the estimated user emotions. For example, if the user is excited, the storage unit will store detailed data. For example, if the user is feeling anxious, the storage unit may also store highly reliable data in detail. For example, if the user is relaxed, the storage unit may also store a wide range of data in detail. This allows for the storage of more appropriate data by adjusting the level of detail of the stored data based on 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 storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input user emotion data into AI and adjust the level of detail of the stored data.

[0096] The storage unit can prioritize saving highly relevant data, taking into account the geographical location information of the products. For example, if a product is traded frequently in a particular region, the storage unit will prioritize saving data from that region. The storage unit can also save relevant data based on the geographical location information of the product's origin. For example, the storage unit can save data related to the sales region of a product and prioritize the analysis of highly relevant data. This allows for the priority saving of highly relevant data by considering the geographical location information of the products. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the geographical location information of the products into AI and perform data storage.

[0097] The storage unit can store information from social media and use it to determine the authenticity of a product. For example, the storage unit can store the reputation of a product on social media and use it to determine its authenticity. The storage unit can also store user comments on social media and evaluate the reliability of a product. The storage unit can also store images of a product on social media and use them to determine its authenticity. In this way, by storing information from social media, it can be used to determine the authenticity of a product. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input information from social media into AI and perform authenticity determination.

[0098] The guarantee unit can estimate the user's emotions and adjust the placement of the guarantee sticker based on the estimated emotions. For example, if the user is excited, the guarantee unit may place the guarantee sticker in a conspicuous location. For example, if the user is feeling anxious, the guarantee unit may also place the guarantee sticker in a more reliable location. For example, if the user is relaxed, the guarantee unit may also place the guarantee sticker in any location. By adjusting the placement of the guarantee sticker based on the user's emotions, the guarantee sticker can be placed in a more appropriate location. 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 guarantee unit may be performed using AI, for example, or without AI. For example, the guarantee unit can input user emotion data into AI and adjust the placement of the guarantee sticker.

[0099] The warranty department can adjust the level of detail on the warranty seal based on the importance of the product. For example, the warranty department may affix a detailed warranty seal to expensive products. For example, the warranty department may affix a warranty seal quickly to general products. For example, the warranty department may affix a specialized warranty seal to products in a specific category. By adjusting the level of detail on the warranty seal based on the importance of the product, a more appropriate warranty seal can be affixed. Some or all of the above processing in the warranty department may be performed using AI, for example, or not using AI. For example, the warranty department can input product importance data into AI and adjust the level of detail on the warranty seal.

[0100] The warranty department can apply different warranty methods depending on the product category. For example, the warranty department may apply a detailed warranty method to expensive branded goods. For example, the warranty department may apply a quick warranty method to general daily necessities. For example, the warranty department may apply a specialized warranty method to products in a specific category. This allows for the application of the most appropriate warranty method for each product category. Some or all of the above processes in the warranty department may be performed using AI, for example, or not using AI. For example, the warranty department can input warranty methods appropriate to the product category into AI and then provide a warranty.

[0101] The assurance unit can estimate the user's emotions and determine the priority of assurance seals based on the estimated user emotions. For example, if the user is excited, the assurance unit may prioritize applying important assurance seals. For example, if the user is feeling anxious, the assurance unit may prioritize applying reliable assurance seals. For example, if the user is relaxed, the assurance unit may apply any assurance seal. This allows for the application of more appropriate assurance seals by determining the priority of assurance seals based on 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 assurance unit may be performed using AI, for example, or without AI. For example, the assurance unit can input user emotion data into AI to determine the priority of assurance seals.

[0102] The warranty department can determine the priority of warranty seals based on the product submission date. For example, the warranty department may prioritize applying warranty seals to products with approaching deadlines. The warranty department may also quickly apply warranty seals to products with earlier submission dates. The warranty department may also dynamically adjust the priority of warranty seals based on the submission date. This allows for the application of more appropriate warranty seals by determining the priority of warranty seals based on the product submission date. Some or all of the above processes in the warranty department may be performed using AI, for example, or not using AI. For example, the warranty department can input product submission date data into AI to determine the priority of warranty seals.

[0103] The warranty unit can adjust the order of warranty seals based on the relevance of the products. For example, the warranty unit may prioritize applying warranty seals to highly relevant products. For example, the warranty unit may postpone applying warranty seals to less relevant products. The warranty unit may also dynamically adjust the order of warranty seals based on relevance. This allows for the application of more appropriate warranty seals by adjusting the order of warranty seals based on the relevance of the products. Some or all of the above processing in the warranty unit may be performed using AI, for example, or without AI. For example, the warranty unit can input product relevance data into AI and adjust the order of warranty seals.

[0104] The verification unit can estimate the user's emotions and adjust the tampering verification method based on the estimated user emotions. For example, if the user is excited, the verification unit can perform a rapid tampering verification. For example, if the user is feeling anxious, the verification unit can also perform a detailed tampering verification. For example, if the user is relaxed, the verification unit can also perform a broad tampering verification. This allows for more appropriate tampering verification by adjusting the tampering verification method based on 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 verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input user emotion data into AI and adjust the tampering verification method.

[0105] The verification unit can optimize its verification algorithm by referring to the product's past tampering history. For example, the verification unit can focus on verifying products that have been frequently tampered with in the past. The verification unit can also adjust its verification algorithm based on the past tampering history. For example, the verification unit can analyze the past tampering history and apply the most suitable verification algorithm. This allows the verification algorithm to be optimized by referring to the product's past tampering history. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input past tampering history data into AI to optimize its verification algorithm.

[0106] The verification unit can apply different verification methods depending on the product category. For example, the verification unit can apply a detailed verification method to expensive brand-name goods. For example, the verification unit can apply a quick verification method to general daily necessities. For example, the verification unit can apply a specialized verification method to products in a specific category. This allows the application of the most appropriate verification method for each product category. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input a verification method appropriate to the product category into AI and perform the verification.

[0107] The verification unit can estimate the user's emotions and determine the priority of tampering checks based on the estimated user emotions. For example, if the user is excited, the verification unit may prioritize important tampering checks. For example, if the user is feeling anxious, the verification unit may also prioritize highly reliable tampering checks. For example, if the user is relaxed, the verification unit may also perform a wide range of tampering checks. This allows for more appropriate tampering checks by determining the priority of tampering checks based on 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 verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input user emotion data into AI to determine the priority of tampering checks.

[0108] The verification unit can prioritize verifications that are highly relevant, taking into account the geographical location information of the product. For example, if the product is traded frequently in a particular region, the verification unit will prioritize verifications in that region. The verification unit can also perform relevant verifications based on the geographical location information of the product's origin. For example, the verification unit can perform verifications related to the product's sales region and prioritize verifications that are highly relevant. In this way, by considering the geographical location information of the product, it is possible to prioritize verifications that are highly relevant. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the geographical location information of the product into AI and perform verifications.

[0109] The verification unit can improve the accuracy of tampering verification by referring to information from social media. For example, the verification unit can improve the accuracy of tampering verification by referring to the reputation of the product on social media. The verification unit can also evaluate the reliability of the product by referring to user comments on social media. The verification unit can also improve the accuracy of tampering verification by referring to images of the product on social media. In this way, the accuracy of tampering verification can be improved by referring to information from social media. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input information from social media into AI and perform tampering verification.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The collection unit can collect external information such as product photos, descriptions, and websites. For example, the collection unit can take product photos, input descriptions, and retrieve information from websites. The collection unit can input this information into AI and use it as data for analysis. This allows the collection unit to collect the information necessary for determining the authenticity of a product. Some or all of the above processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can take product photos, input them into AI, and perform analysis.

[0112] The judgment unit can analyze the collected information to determine the authenticity of a product. For example, the judgment unit can analyze a photograph of a product using image analysis technology to determine its authenticity. The judgment unit can also analyze a description using text analysis technology to determine its authenticity. The judgment unit can also use a machine learning algorithm to determine the authenticity of a product based on the collected information. This allows the judgment unit to determine the authenticity of a product based on the collected information. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the collected information into AI and perform authenticity determination.

[0113] The storage unit can save data on the product's condition at the time of shipment. For example, the storage unit can photograph the product's appearance and packaging and save it as data. The storage unit can also save information such as the product's serial number and shipment date and time in a database. This allows the product's condition at the time of shipment to be saved as data. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can photograph the product's appearance, input it into the AI, and save the data.

[0114] The warranty department can use shipping warranty stickers or tabs. For example, the warranty department can attach a shipping warranty sticker to any location on the product and photograph it with a dedicated app. The warranty department can also attach a tab with a QR code to any location on the product and photograph it with a dedicated app. This prevents product substitution by using shipping warranty stickers or tabs. Some or all of the above processes in the warranty department may be performed using AI, for example, or without AI. For example, the warranty department can input an image of a product with a shipping warranty sticker attached into AI and perform analysis.

[0115] The verification unit can check for tampering when a product is returned. For example, the verification unit checks the condition of the shipping guarantee seal on the returned product to determine if it has been tampered with. The verification unit can also check the condition of the tab on the returned product to determine if it has been tampered with. This prevents product substitution by checking for tampering when a product is returned. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input an image of the returned product into the AI ​​to determine if it has been tampered with.

[0116] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit can prioritize the collection of important information and analyze it quickly. For example, if the user is anxious, the data collection unit can also prioritize the collection of data from reliable sources. For example, if the user is relaxed, the data collection unit can collect a wide range of information and perform detailed analysis. This allows for the collection of more relevant information by prioritizing the information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI to determine the priority of information.

[0117] The judgment unit can estimate the user's emotions and adjust the way the judgment result is presented based on the estimated user emotions. For example, if the user is excited, the judgment unit provides a concise and clear judgment result. For example, if the user is feeling anxious, the judgment unit may also provide a judgment result that includes a detailed explanation. For example, if the user is relaxed, the judgment unit may also provide a visually easy-to-understand judgment result. By adjusting the way the judgment result is presented based on the user's emotions, a more appropriate judgment result 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, for example, or without AI. For example, the judgment unit can input user emotion data into AI and adjust the way the judgment result is presented.

[0118] The judgment unit can estimate the user's emotions and determine the priority of judgment results based on the estimated user emotions. For example, if the user is excited, the judgment unit will prioritize displaying important judgment results. For example, if the user is feeling anxious, the judgment unit may also prioritize displaying reliable judgment results. For example, if the user is relaxed, the judgment unit may also display a wide range of judgment results. This allows for the provision of more appropriate judgment results by prioritizing judgment results based on 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, for example, or without AI. For example, the judgment unit can input user emotion data into AI to determine the priority of judgment results.

[0119] The storage unit can estimate the user's emotions and determine the priority of data to store based on the estimated user emotions. For example, if the user is excited, the storage unit may prioritize storing important data. For example, if the user is feeling anxious, the storage unit may prioritize storing reliable data. For example, if the user is relaxed, the storage unit may store a wide range of data. This allows for the storage of more appropriate data by prioritizing data storage based on 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 storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input user emotion data into AI and determine the priority of data to store.

[0120] The guarantee unit can estimate the user's emotions and adjust the placement of the guarantee sticker based on the estimated emotions. For example, if the user is excited, the guarantee unit may place the guarantee sticker in a conspicuous location. For example, if the user is feeling anxious, the guarantee unit may also place the guarantee sticker in a more reliable location. For example, if the user is relaxed, the guarantee unit may also place the guarantee sticker in any location. By adjusting the placement of the guarantee sticker based on the user's emotions, the guarantee sticker can be placed in a more appropriate location. 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 guarantee unit may be performed using AI, for example, or without AI. For example, the guarantee unit can input user emotion data into AI and adjust the placement of the guarantee sticker.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects external information such as product photos, descriptions, and websites. For example, it takes product photos, inputs descriptions, and retrieves information from websites. The data collection unit inputs this information into the AI, which can then be used as data for analysis. Step 2: The judgment unit analyzes the information collected by the collection unit to determine the authenticity of the product. For example, it can use image analysis technology to analyze a photograph of the product and determine its authenticity. It can also use text analysis technology to analyze the description and determine the authenticity of the product. Furthermore, it can use machine learning algorithms to determine the authenticity of the product based on the collected information. Step 3: The storage unit saves data on the product's condition at the time of shipment. For example, it takes photos of the product's appearance and packaging and saves them as data. It can also save information such as the product's serial number and shipping date and time in a database. Step 4: The warranty section uses a shipping warranty sticker or tab. For example, attach a shipping warranty sticker to any location on the product and photograph it with the dedicated app. Alternatively, you can attach a tab with a QR code to any location on the product and photograph it with the dedicated app. Step 5: The verification unit checks for tampering upon return. For example, it checks the condition of the shipping guarantee seal on the returned product to determine if tampering has occurred. It can also check the condition of the tab on the returned product to determine if tampering has occurred.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, determination unit, storage unit, guarantee unit, and verification unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects external information such as product photos, descriptions, and websites using the camera 42 and communication I / F 44 of the smart device 14. The determination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to determine the authenticity of the product. The storage unit stores the product condition at the time of shipment as data in the storage 32 of the data processing unit 12. The guarantee unit attaches a shipping guarantee seal or tab to any part of the product using the control unit 46A of the smart device 14 and photographs it with a dedicated app. The verification unit checks for tampering at the time of return using, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, determination unit, storage unit, guarantee unit, and verification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects external information such as product photos, descriptions, and websites using the camera 42 and communication I / F 44 of the smart glasses 214. The determination unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to determine the authenticity of the product. The storage unit stores the product condition at the time of shipment as data in the storage 32 of the data processing unit 12. The guarantee unit, for example, uses the control unit 46A of the smart glasses 214 to attach a shipping guarantee sticker or tab to any part of the product and photographs it with a dedicated app. The verification unit, for example, uses the identification processing unit 290 of the data processing unit 12 to check for tampering when the product is returned. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, determination unit, storage unit, guarantee unit, and verification unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects external information such as product photos, descriptions, and websites using the camera 42 and communication I / F 44 of the headset terminal 314. The determination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to determine the authenticity of the product. The storage unit stores the product condition at the time of shipment as data in the storage 32 of the data processing unit 12. The guarantee unit, for example, uses the control unit 46A of the headset terminal 314 to attach a shipping guarantee sticker or tab to any part of the product and photographs it with a dedicated application. The verification unit, for example, uses the identification processing unit 290 of the data processing unit 12 to check for tampering when the product is returned. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, determination unit, storage unit, guarantee unit, and verification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the robot 414 to collect external information such as product photos, descriptions, and websites. The determination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to determine the authenticity of the product. The storage unit stores data of the product's condition at the time of shipment in the storage 32 of the data processing unit 12. The guarantee unit, for example, uses the control unit 46A of the robot 414 to attach a shipping guarantee seal or tab to any part of the product and photographs it with a dedicated app. The verification unit, for example, uses the identification processing unit 290 of the data processing unit 12 to check for tampering when the product is returned. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection unit that collects external information such as product photos, descriptions, and websites, A determination unit analyzes the information collected by the collection unit to determine the authenticity of the product, A storage unit that stores data on the product's condition at the time of shipment, Warranty section using a shipping guarantee seal or tab, It includes a verification unit that checks for tampering when a product is returned. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect external information such as product photos, descriptions, and websites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The determination unit, The collected information is analyzed to determine the authenticity of the product. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned storage unit is Save data on the product's condition at the time of shipment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned warranty section is, Use a shipping guarantee seal or tab. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned verification unit is Check for tampering upon return. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze the past transaction history of products and optimize the scope of information we collect. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Different collection algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and adjusts the level of detail of the information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is By considering the geographical location of the product, we prioritize the collection of highly relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We collect information from social media and use it to determine the authenticity of products. The system described in Appendix 1, characterized by the features described herein. (Note 13) The determination unit, The system estimates the user's emotions and adjusts how the judgment results are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The determination unit, Adjust the level of detail in the assessment based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, A different judgment algorithm is applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, The system estimates the user's emotions and determines the priority of the judgment results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, Prioritizing the evaluation based on the submission date of the products. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, The order of the results will be adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned storage unit is It estimates the user's emotions and determines the priority of data to store based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned storage unit is Referencing past product data optimizes the storage algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned storage unit is Apply different preservation methods depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned storage unit is It estimates the user's emotions and adjusts the level of detail in the stored data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned storage unit is Considering the geographical location of the products, prioritize saving the most relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned storage unit is We save information from social media and use it to determine the authenticity of products. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned warranty section is, The system estimates the user's emotions and adjusts the placement of the warranty seal based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned warranty section is, Adjust the level of detail on the warranty seal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned warranty section is, Different warranty methods will be applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned warranty section is, The system estimates the user's emotions and determines the priority of warranty seals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned warranty section is, The priority of warranty seals will be determined based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned warranty section is, The order of warranty seals will be adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned verification unit is We estimate the user's emotions and adjust the tampering detection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned verification unit is We optimize the verification algorithm by referring to the product's past tampering history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned verification unit is Different verification methods are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned verification unit is The system estimates user sentiment and determines the priority of tampering checks based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned verification unit is Considering the geographical location of the product, we prioritize verifications that are highly relevant. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned verification unit is We will improve the accuracy of tampering verification by referencing information from social media. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 collection unit that collects external information such as product photos, descriptions, and websites, A determination unit analyzes the information collected by the collection unit to determine the authenticity of the product, A storage unit that stores data on the product's condition at the time of shipment, Warranty section using a shipping guarantee seal or tab, It includes a verification unit that checks for tampering when a product is returned. A system characterized by the following features.

2. The aforementioned collection unit is Collect external information such as product photos, descriptions, and websites. The system according to feature 1.

3. The determination unit, The collected information is analyzed to determine the authenticity of the product. The system according to feature 1.

4. The aforementioned storage unit is Save data on the product's condition at the time of shipment. The system according to feature 1.

5. The aforementioned warranty section is, Use a shipping guarantee seal or tab. The system according to feature 1.

6. The aforementioned verification unit is Check for tampering upon return. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is We analyze the past transaction history of products and optimize the scope of information we collect. The system according to feature 1.

Images