system

Abstract

The system according to this embodiment aims to enable ordinary users to easily authenticate and evaluate the market value of luxury goods and branded items. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a determination unit, an evaluation unit, and a provision unit. The collection unit collects images and detailed information of products. The analysis unit analyzes the information collected by the collection unit. The determination unit determines the authenticity of the products based on the information analyzed by the analysis unit. The evaluation unit evaluates the market value of the products determined by the determination unit. The provision unit provides the results evaluated by the evaluation unit to the user.

Description

Technical Field

[0006] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the authentication of genuine and fake products and the evaluation of market value of high-end and brand-name products require specialized knowledge and are difficult for ordinary users.

[0005] The system according to the embodiment aims to enable ordinary users to easily authenticate genuine and fake products and evaluate the market value of high-end and brand-name products.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a determination unit, an evaluation unit, and a provision unit. The collection unit collects images and detailed information of products. The analysis unit analyzes the information collected by the collection unit. The determination unit determines the authenticity of the products based on the information analyzed by the analysis unit. The evaluation unit evaluates the market value of the products determined by the determination unit. The provision unit provides the results evaluated by the evaluation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment makes it possible for ordinary users to easily authenticate the authenticity of luxury goods and branded items and evaluate their market value. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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 online trading platform according to an embodiment of the present invention is a system that utilizes generative AI to authenticate and value luxury goods, branded goods, antiques, and second-hand goods, providing safe and reliable online trading. This system works by having the user input images and detailed information of the item they wish to have authenticated, and the generative AI analyzes this information to determine its authenticity and then evaluates its market value, providing this information to the user. For example, a user uploads images of luxury watches, branded bags, or antique furniture, and inputs detailed information about the item. This information is input into the generative AI, which learns from past data and patterns to analyze the item's characteristics in detail and determine its authenticity. For example, it analyzes the markings on luxury watches or the stitching characteristics of branded bags to determine authenticity. Furthermore, the generative AI evaluates the market value of the item based on past transaction data and market trends, and provides this information to the user. For example, it evaluates the market value of antique furniture or the transaction price of second-hand goods and provides this information to the user. This mechanism allows users to conduct online transactions with peace of mind. The generative AI's authentication and valuation reduces the risk of being sold counterfeit goods and allows users to accurately grasp the market value. For example, when purchasing a luxury watch, users can trade with peace of mind by undergoing authenticity verification and valuation by the AI-generated data. This system also functions as an online trading platform. Users can trade items that have undergone authenticity verification and valuation by the AI-generated data on the platform. This ensures safe and reliable transactions. For instance, a user wanting to sell a branded handbag can have it authenticated by the AI-generated data and then sell it on the platform based on the results. Thus, the online trading platform utilizes AI-generated data to perform authenticity verification and valuation of luxury goods, branded goods, antiques, and second-hand items, providing a safe and reliable online trading experience. Users can trade with peace of mind by undergoing authenticity verification and valuation by the AI-generated data. This allows users to trade with confidence on the online trading platform.

[0029] The online trading platform according to the embodiment comprises a collection unit, an analysis unit, a determination unit, an evaluation unit, and a provision unit. The collection unit collects images and detailed information of products that the user wishes to have authenticated. The collection unit collects, for example, images and detailed information of products uploaded by the user. The collection unit allows, for example, the user to upload images of luxury watches, designer bags, antique furniture, etc., and input detailed information about the products. The collection unit allows, for example, the user to upload information using a smartphone or personal computer. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected information using, for example, a generative AI. The analysis unit analyzes the characteristics of the products in detail. The analysis unit analyzes, for example, the engraving on luxury watches or the stitching characteristics of designer bags. The determination unit determines the authenticity of the products based on the information analyzed by the analysis unit. The determination unit determines the authenticity of the products using, for example, a generative AI. The determination unit determines authenticity based on the characteristics of the products. The determination unit determines authenticity based on, for example, the engraving on luxury watches or the stitching characteristics of designer bags. The evaluation unit evaluates the market value of the product determined by the judgment unit. The evaluation unit evaluates the market value of the product using, for example, generative AI. The evaluation unit evaluates the market value of the product based on, for example, past transaction data and market trends. The evaluation unit evaluates, for example, the market value of antique furniture or the transaction price of used goods. The provision unit provides the user with the results evaluated by the evaluation unit. The provision unit provides the evaluation results to the user using, for example, generative AI. The provision unit includes, for example, an interface for providing the evaluation results to the user. The provision unit provides the evaluation results through, for example, a web application or a mobile application. As a result, the online trading platform according to the embodiment allows users to conduct transactions with peace of mind.

[0030] The data collection unit collects images and detailed information of items that users wish to have appraised. Specifically, users can upload images of luxury watches, designer bags, antique furniture, etc., and input detailed information about the items. Users can upload information using smartphones or personal computers. The data collection unit receives image and text data sent from these devices and stores it in a central database. For example, in the case of a luxury watch, the user uploads images of the entire watch, the engraving, and detailed images of the movement. For designer bags, they provide images of the exterior, the inner tags, and detailed stitching. For antique furniture, they upload images of the entire piece, decorative parts, and the year of manufacture and manufacturer's markings. In addition to this image data, the data collection unit also collects detailed information such as the brand name, model name, year of manufacture, purchase price, and current condition of the item. This allows the data collection unit to centrally manage the diverse information provided by users and secure the data necessary for subsequent analysis and evaluation. Furthermore, the data collection unit provides an interface for users to upload information, improving ease of use. For example, it includes features to guide the image upload procedure and an auto-completion function for input forms, enabling users to provide information smoothly. This allows the data collection unit to efficiently and effectively collect information from users, thereby improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the collection unit. Specifically, it uses generative AI to analyze the collected information and analyze the characteristics of the products in detail. For example, it analyzes the engravings on luxury watches and the stitching characteristics of brand-name bags. The generative AI uses image recognition technology to analyze product images and extract features unique to specific brands and models. For example, in the case of luxury watches, it analyzes the font and position of the engraving and the structure of the movement, and in the case of brand-name bags, it analyzes the stitching pattern, the texture of the material, and the position of the tag. Furthermore, the generative AI also analyzes text data and compares the detailed product information with image data. For example, it checks whether the brand name and model name entered by the user match the image data and checks for any inconsistencies. Based on these analysis results, the analysis unit grasps the characteristics of the products in detail and provides the data necessary for subsequent authenticity determination and market value assessment. Furthermore, the analysis unit can analyze product characteristics more accurately by comparing them with past data and information collected from other users. For example, it compares them with other products of the same brand or model and evaluates the degree of characteristic agreement. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to quickly and accurately analyze the collected information, improving the reliability and safety of the entire system.

[0032] The judgment unit determines the authenticity of a product based on the information analyzed by the analysis unit. Specifically, it uses a generative AI to determine the authenticity of a product. The generative AI is pre-trained on a large amount of authenticity data to determine authenticity based on the characteristics of the product. For example, it determines authenticity based on the markings on luxury watches or the stitching characteristics of brand-name bags. The generative AI uses image recognition technology to extract the characteristics of the product and compares them with the training data. For example, in the case of luxury watches, it checks whether the font and position of the markings and the structure of the movement match those of a genuine product. In the case of brand-name bags, it checks whether the stitching pattern, the texture of the material, and the position of the tag match those of a genuine product. Furthermore, the generative AI also analyzes text data and compares the detailed product information with image data. For example, it checks whether the brand name and model name entered by the user match the image data and checks for any inconsistencies. Based on these analysis results, the judgment unit can determine the authenticity of a product with high accuracy. Furthermore, the judgment unit can improve the accuracy of its authenticity determination by comparing it with past data and information collected from other users. For example, it can compare the product with other products of the same brand or model to evaluate the degree of matching characteristics. Furthermore, the judgment unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing warnings early. This allows the judgment unit to quickly and accurately determine the authenticity of a product, improving the overall reliability and security of the system.

[0033] The evaluation unit assesses the market value of the product determined by the judgment unit. Specifically, it uses generative AI to evaluate the market value of the product. The generative AI is pre-trained on a large amount of transaction data to evaluate the market value of a product based on past transaction data and market trends. For example, it evaluates the market value of antique furniture and the transaction price of used goods. The generative AI analyzes past transaction data and market trends to evaluate the market value based on the characteristics of the product. For example, in the case of antique furniture, the market value is evaluated based on characteristics such as the year of manufacture, manufacturer, and condition. In the case of used goods, the market value is evaluated based on characteristics such as brand name, model name, years of use, and condition. Furthermore, the generative AI analyzes current market trends in real time and evaluates the market value of the product based on the latest information. For example, if the demand for a particular brand or model is surging, the market value is evaluated taking that impact into account. Based on these analysis results, the evaluation unit can evaluate the market value of the product with high accuracy. Furthermore, the evaluation unit can improve the accuracy of market value evaluation by comparing it with past data and information collected from other users. For example, it can compare it with other products of the same brand or model and evaluate the degree of characteristic agreement. Furthermore, the evaluation unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the evaluation unit to quickly and accurately assess the market value of products, improving the reliability and safety of the entire system.

[0034] The service provider delivers the evaluation results, assessed by the evaluation provider, to the user. Specifically, it uses generative AI to provide evaluation results to the user. The service provider has an interface for delivering evaluation results to the user. For example, it provides evaluation results through web applications and mobile applications. The service provider provides an intuitive and easy-to-use interface so that users can easily check the evaluation results. For example, it visually displays evaluation results in graphs and charts so that users can understand the market value of a product and the results of authenticity judgment at a glance. The service provider also provides support for users to take the next action based on the evaluation results. For example, it has a function to suggest appropriate trading partners and platforms to users who wish to sell a product based on the evaluation results. Furthermore, the service provider can collect feedback from users and continuously improve the accuracy of the evaluation results and the usability of the interface. For example, it updates the training data of the generative AI based on the feedback that users provide on the evaluation results to improve evaluation accuracy. In addition, the service provider can reliably transmit information using multiple communication methods. For example, it uses email and SMS in addition to notifications from web and mobile applications to ensure that important information is delivered reliably. This allows the service provider to quickly and reliably provide evaluation results to users, supporting them in conducting transactions with peace of mind.

[0035] The collection unit can collect images and detailed information of products uploaded by users. For example, users can upload images and detailed information of products using their smartphones or personal computers. For example, users can upload images of luxury watches, designer bags, antique furniture, etc., and input detailed information about the products. This allows the collection unit to efficiently collect information provided by users. Some or all of the above processing in the collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the collection unit can input the images and detailed information uploaded by the user into a generative AI, which can then analyze and collect the information.

[0036] The analysis unit can analyze the characteristics of a product based on the collected information. The analysis unit analyzes the collected information using, for example, a generative AI. The analysis unit analyzes the characteristics of a product in detail, for example, the engraving on a luxury watch or the stitching characteristics of a brand-name bag. This allows the analysis unit to analyze the characteristics of a product in detail. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the collected information into a generative AI, which can then analyze the information and extract characteristics.

[0037] The determination unit can determine the authenticity of a product based on the analyzed features. The determination unit can determine the authenticity of a product using, for example, a generative AI. The determination unit can determine authenticity based on, for example, the features of a product. The determination unit can determine authenticity based on, for example, the engraving on a luxury watch or the stitching features of a brand-name bag. This allows the determination unit to accurately determine the authenticity of a product. Some or all of the above-described processes in the determination unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the determination unit can input the analyzed features into a generative AI, and the generative AI can determine authenticity.

[0038] The evaluation unit can assess the market value of a product based on past transaction data and market trends. The evaluation unit can assess the market value of a product, for example, using a generative AI. The evaluation unit can assess the market value of a product, for example, based on past transaction data and market trends. The evaluation unit can assess the market value of antique furniture, for example, or the transaction price of used goods. This allows the evaluation unit to accurately assess the market value of a product. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the evaluation unit can input past transaction data and market trends into a generative AI, and the generative AI can assess the market value.

[0039] The service provider can provide the evaluated results to the user. The service provider can provide the evaluated results to the user, for example, using a generative AI. The service provider can provide an interface for providing the evaluated results to the user, for example. The service provider can provide the evaluated results through a web application or a mobile application, for example. This allows the service provider to provide the evaluated results to the user. Some or all of the above processing in the service provider may be performed using a generative AI or not. For example, the service provider can input the evaluated results into a generative AI, and the generative AI can provide the evaluated results to the user.

[0040] The data collection unit can analyze the user's past data collection history and select the optimal data collection method. For example, the data collection unit can analyze the user's past data collection history using generative AI. For example, the data collection unit can prioritize suggesting data collection methods that the user has frequently used in the past (such as image uploads and text input). For example, the data collection unit can perform data collection at specific time periods based on the user's past data collection history. For example, the data collection unit can analyze the user's past data collection history and suggest the most efficient data collection method. This enables efficient information collection by allowing the data collection unit to select the optimal data collection method based on past data collection history. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input the user's past data collection history into the generative AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter product images and detailed information based on the user's current areas of interest. For example, the data collection unit can analyze the user's current areas of interest using generative AI. For example, the data collection unit can prioritize collecting products from brands or categories that the user is currently interested in. For example, the data collection unit can filter related product images and detailed information based on the user's current areas of interest. For example, the data collection unit can analyze the user's current areas of interest and collect information on the most relevant products. In this way, the data collection unit can collect highly relevant information by filtering information based on the user's areas of interest. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input the user's areas of interest into the generative AI, which can then perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting product images and detailed information. For example, the data collection unit analyzes the user's geographical location using a generative AI. For example, the data collection unit prioritizes the collection of information from stores and vendors in the user's current location. For example, the data collection unit collects information on products available in the nearest location based on the user's geographical location. For example, the data collection unit analyzes the user's geographical location and collects information on highly relevant products. This allows the data collection unit to prioritize the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generative AI, or without one. For example, the data collection unit can input the user's geographical location into a generative AI, which can then prioritize the collection of highly relevant information.

[0043] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting product images and detailed information. For example, the data collection unit can analyze the user's social media activity using generative AI. For example, the data collection unit can prioritize collecting information on products that the user has shown interest in on social media. For example, the data collection unit can collect relevant product information from the user's social media activity. For example, the data collection unit can analyze the user's social media activity and collect the most relevant product information. In this way, the data collection unit can collect highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input the user's social media activity into generative AI, and the generative AI can collect relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the product during the analysis. For example, the analysis unit uses a generative AI to analyze the importance of the product. For example, the analysis unit performs a detailed analysis for expensive products. For example, the analysis unit performs a basic analysis for general products. For example, the analysis unit performs a detailed analysis for products that the user is particularly interested in. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the product. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the product into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, the analysis unit uses a generative AI to analyze the product category. For example, in the case of a luxury watch, the analysis unit analyzes the engravings and mechanical parts. For example, in the case of a branded bag, the analysis unit analyzes the stitching and materials. For example, in the case of antique furniture, the analysis unit analyzes the type of wood and the manufacturing date. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the product category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the product category into a generative AI, and the generative AI can apply different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the product submission date during the analysis process. For example, the analysis unit uses a generative AI to analyze the product submission date. For example, the analysis unit prioritizes the analysis of recently submitted products. For example, the analysis unit postpones the analysis of older products. For example, the analysis unit adjusts the analysis schedule based on the submission date. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the product submission date. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input the product submission date into a generative AI, and the generative AI can determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the products during the analysis. For example, the analysis unit uses a generative AI to analyze the relevance of the products. For example, the analysis unit prioritizes the analysis of highly relevant products. For example, the analysis unit postpones the analysis of less relevant products. For example, the analysis unit adjusts the order of analysis based on relevance. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the products. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the relevance of the products into a generative AI, and the generative AI can adjust the order of analysis.

[0048] The judgment unit can improve the accuracy of its judgment by considering the interrelationships between products when determining authenticity. For example, the judgment unit analyzes the interrelationships between products using a generative AI. For example, the judgment unit determines authenticity by comparing the characteristics of products of the same brand. For example, the judgment unit determines authenticity by comparing the characteristics of products of the same category. For example, the judgment unit improves the accuracy of its judgment by referring to past authenticity judgment results. In this way, the judgment unit improves the accuracy of its judgment by considering the interrelationships between products. Some or all of the above processing in the judgment unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the judgment unit can input the interrelationships between products into a generative AI, and the generative AI can improve the accuracy of its judgment.

[0049] The judgment unit can make a judgment by considering the attribute information of the product submitter when determining authenticity. For example, the judgment unit can analyze the attribute information of the product submitter using a generating AI. For example, the judgment unit can determine authenticity by referring to the submitter's past transaction history. For example, the judgment unit can determine authenticity by considering the submitter's reliability. For example, the judgment unit can determine authenticity by analyzing the submitter's attribute information. As a result, the judgment unit can make a more accurate judgment by considering the attribute information of the product submitter. Some or all of the above processing in the judgment unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the judgment unit can input the submitter's attribute information into a generating AI, and the generating AI can make the judgment.

[0050] The judgment unit can make a judgment regarding the authenticity of a product by considering its geographical distribution. For example, the judgment unit can analyze the geographical distribution of the product using a generative AI. For example, the judgment unit can determine authenticity by considering the characteristics of products manufactured in geographically close locations. For example, the judgment unit can determine authenticity by considering the characteristics of products manufactured in geographically distant locations. For example, the judgment unit can adjust the criteria for determining authenticity based on the geographical distribution. This allows the judgment unit to make more accurate judgments by considering the geographical distribution of the product. Some or all of the above-described processes in the judgment unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the judgment unit can input the geographical distribution of the product into a generative AI, and the generative AI can perform the judgment.

[0051] The judgment unit can improve the accuracy of its judgment by referring to relevant literature on the product when determining authenticity. For example, the judgment unit analyzes relevant literature on the product using a generative AI. For example, the judgment unit analyzes the characteristics of the product in detail based on the relevant literature. For example, the judgment unit adjusts the criteria for determining authenticity by referring to relevant literature. For example, the judgment unit refers to past judgment results based on relevant literature. In this way, the judgment unit improves the accuracy of its judgment by referring to relevant literature. Some or all of the above processes in the judgment unit may be performed using a generative AI or not. For example, the judgment unit can input relevant literature into a generative AI, and the generative AI can improve the accuracy of the judgment.

[0052] The valuation unit can optimize its valuation algorithm by referring to past transaction data when determining market value. For example, the valuation unit analyzes past transaction data using a generative AI. For example, the valuation unit adjusts the market value valuation algorithm based on past transaction data. For example, the valuation unit improves the accuracy of the valuation by referring to past transaction data. For example, the valuation unit analyzes past transaction data and applies the optimal valuation algorithm. In this way, the valuation unit improves the accuracy of its valuation algorithm by referring to past transaction data. Some or all of the above processes in the valuation unit may be performed using a generative AI or not. For example, the valuation unit can input past transaction data into a generative AI, and the generative AI can optimize the valuation algorithm.

[0053] The valuation unit can apply different valuation methods to each product category when determining market value. For example, the valuation unit can analyze product categories using generative AI. For example, in the case of luxury watches, the valuation unit will evaluate based on past transaction data and market trends. For example, in the case of branded bags, the valuation unit will evaluate based on the characteristics of the materials and design. For example, in the case of antique furniture, the valuation unit will evaluate based on the manufacturing date and condition. This allows the valuation unit to determine market value more accurately by applying valuation methods appropriate to the product category. Some or all of the above processing in the valuation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the valuation unit can input product categories into the generative AI, and the generative AI can apply different valuation methods.

[0054] The valuation unit can analyze changes in valuation based on the product's submission date when conducting market value assessments. For example, the valuation unit can use a generative AI to analyze the product's submission date. For example, the valuation unit can prioritize the evaluation of recently submitted products. For example, the valuation unit can postpone the evaluation of older products. For example, the valuation unit can adjust the evaluation schedule based on the submission date. This allows the valuation unit to conduct more accurate market value assessments by analyzing changes in valuation based on the product's submission date. Some or all of the above processes in the valuation unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the valuation unit can input the product's submission date into a generative AI, which can then analyze the changes in valuation.

[0055] The evaluation unit can perform market value assessments by referring to relevant market data for the product. For example, the evaluation unit can analyze the relevant market data using a generative AI. For example, the evaluation unit can assess the market value of the product based on the relevant market data. For example, the evaluation unit can improve the accuracy of the assessment by referring to the relevant market data. For example, the evaluation unit can perform the optimal assessment by analyzing the relevant market data. As a result, the accuracy of the assessment is improved by the evaluation unit referring to the relevant market data. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the evaluation unit can input relevant market data into a generative AI, and the generative AI can perform the assessment.

[0056] The service provider can select the optimal display method by referring to the user's past operation history when providing evaluation results. The service provider can, for example, analyze the user's past operation history using a generation AI. The service provider can, for example, prioritize providing display methods that the user has preferred to use in the past. The service provider can, for example, propose the optimal display method from the user's past operation history. The service provider can, for example, analyze the user's past operation history to provide the most efficient display method. In this way, the service provider can provide the optimal display method by referring to past operation history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's past operation history into a generation AI, and the generation AI can select the optimal display method.

[0057] The service provider can select the optimal display method when providing evaluation results, taking into account the user's device information. For example, the service provider can analyze the user's device information using a generative AI. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by taking device information into consideration. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the user's device information into a generative AI, and the generative AI can select the optimal display method.

[0058] The service provider can select the optimal display method when providing evaluation results, taking into account the user's device information. For example, the service provider can analyze the user's device information using a generative AI. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by taking device information into consideration. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the user's device information into a generative AI, and the generative AI can select the optimal display method.

[0059] The service provider can select the optimal display method by referring to the user's past operation history when providing evaluation results. The service provider can, for example, analyze the user's past operation history using a generation AI. The service provider can, for example, prioritize providing display methods that the user has preferred to use in the past. The service provider can, for example, propose the optimal display method from the user's past operation history. The service provider can, for example, analyze the user's past operation history to provide the most efficient display method. In this way, the service provider can provide the optimal display method by referring to past operation history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's past operation history into a generation AI, and the generation AI can select the optimal display method.

[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] Online trading platforms can further analyze users' purchase history and perform authentication and valuation of related items based on the trends of products the user has purchased in the past. For example, if a user has purchased multiple luxury watches in the past, the authentication and valuation of similar luxury watches can be prioritized. Similarly, if a user frequently purchases bags of a particular brand, the authentication and valuation of bags of that brand can be enhanced. Furthermore, if a user purchases antique furniture, the authentication and valuation of similar antique furniture can be performed. This enables more personalized authentication and valuation based on the user's purchase history.

[0062] When providing evaluation results to a user, the service provider can select the most appropriate display method by referring to the user's past viewing history of evaluation results. For example, if a user has previously preferred to view detailed evaluation results, the service provider can display detailed evaluation results. If a user has previously preferred to view concise evaluation results, the service provider can display concise evaluation results. Furthermore, if a user has previously preferred to view evaluation results in a visually easy-to-understand format, the service provider can display evaluation results in a visually easy-to-understand format. This allows the service provider to provide more appropriate information by selecting the most appropriate display method based on the user's past viewing history.

[0063] The data collection unit can collect product images and detailed information based on regional characteristics, taking into account the user's geographical location. For example, if a user is in a specific region, it can prioritize collecting information on products popular in that region. If a user is traveling, it can collect information on products available in their destination region. Furthermore, if a user is staying in a specific region for an extended period, it can collect product information based on market trends in that region. This allows the data collection unit to collect more relevant information by considering the user's geographical location.

[0064] The analysis unit can analyze product characteristics while considering information about the product's manufacturer and country of manufacture. For example, if a particular brand has different manufacturers, it can analyze the characteristics of each manufacturer. Furthermore, if the product is manufactured in different countries, it can analyze while considering the manufacturing technology and quality standards of each country. In addition, it can refer to past data based on manufacturer and country of manufacture information to perform more accurate analysis. As a result, the analysis unit can perform more accurate characteristic analysis by considering manufacturer and country of manufacture information.

[0065] The authentication unit can determine the authenticity of a product by considering information such as the year of manufacture and the manufacturing lot. For example, if there is variation in quality within a particular year or lot, the unit can use this information to determine authenticity. It can also analyze the characteristics of each year and lot and compare them with past data to determine authenticity. Furthermore, it can consider the characteristics of products manufactured during a specific period based on the year and lot information. As a result, the authentication unit can determine authenticity more accurately by considering information such as the year and lot.

[0066] The evaluation unit can take into account the storage condition and frequency of use of a product when assessing its market value. For example, if a product is stored in good condition, its market value can be assessed higher. Similarly, if a product is used infrequently, its market value can also be assessed higher. Furthermore, by comparing the current market value with past transaction data based on the product's storage condition and frequency of use, a more accurate market value assessment can be made. As a result, the evaluation unit can perform a more accurate market value assessment by considering the product's storage condition and frequency of use.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit gathers images and detailed information of the items that the user wishes to have appraised. For example, users can upload images of luxury watches, designer bags, antique furniture, etc., using their smartphones or personal computers, and enter detailed information about the items. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it uses a generation AI to analyze the characteristics of products in detail, such as the engravings on luxury watches or the stitching characteristics of brand-name bags. Step 3: The judgment unit determines the authenticity of the product based on the information analyzed by the analysis unit. For example, it may use a generating AI to determine authenticity based on the product's characteristics, such as the engraving on a luxury watch or the stitching characteristics of a brand-name bag. Step 4: The evaluation unit evaluates the market value of the product determined by the judgment unit. For example, it uses a generation AI to evaluate the market value of a product based on past transaction data and market trends, and evaluates the market value of antique furniture, the transaction price of used goods, etc. Step 5: The delivery unit provides the user with the results evaluated by the evaluation unit. For example, it may provide an interface for delivering evaluation results to the user using a generation AI, and deliver the evaluation results through a web application or mobile application.

[0069] (Example of form 2) The online trading platform according to an embodiment of the present invention is a system that utilizes generative AI to authenticate and value luxury goods, branded goods, antiques, and second-hand goods, providing safe and reliable online trading. This system works by having the user input images and detailed information of the item they wish to have authenticated, and the generative AI analyzes this information to determine its authenticity and then evaluates its market value, providing this information to the user. For example, a user uploads images of luxury watches, branded bags, or antique furniture, and inputs detailed information about the item. This information is input into the generative AI, which learns from past data and patterns to analyze the item's characteristics in detail and determine its authenticity. For example, it analyzes the markings on luxury watches or the stitching characteristics of branded bags to determine authenticity. Furthermore, the generative AI evaluates the market value of the item based on past transaction data and market trends, and provides this information to the user. For example, it evaluates the market value of antique furniture or the transaction price of second-hand goods and provides this information to the user. This mechanism allows users to conduct online transactions with peace of mind. The generative AI's authentication and valuation reduces the risk of being sold counterfeit goods and allows users to accurately grasp the market value. For example, when purchasing a luxury watch, users can trade with peace of mind by undergoing authenticity verification and valuation by the AI-generated data. This system also functions as an online trading platform. Users can trade items that have undergone authenticity verification and valuation by the AI-generated data on the platform. This ensures safe and reliable transactions. For instance, a user wanting to sell a branded handbag can have it authenticated by the AI-generated data and then sell it on the platform based on the results. Thus, the online trading platform utilizes AI-generated data to perform authenticity verification and valuation of luxury goods, branded goods, antiques, and second-hand items, providing a safe and reliable online trading experience. Users can trade with peace of mind by undergoing authenticity verification and valuation by the AI-generated data. This allows users to trade with confidence on the online trading platform.

[0070] The online trading platform according to the embodiment comprises a collection unit, an analysis unit, a determination unit, an evaluation unit, and a provision unit. The collection unit collects images and detailed information of products that the user wishes to have authenticated. The collection unit collects, for example, images and detailed information of products uploaded by the user. The collection unit allows, for example, the user to upload images of luxury watches, designer bags, antique furniture, etc., and input detailed information about the products. The collection unit allows, for example, the user to upload information using a smartphone or personal computer. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected information using, for example, a generative AI. The analysis unit analyzes the characteristics of the products in detail. The analysis unit analyzes, for example, the engraving on luxury watches or the stitching characteristics of designer bags. The determination unit determines the authenticity of the products based on the information analyzed by the analysis unit. The determination unit determines the authenticity of the products using, for example, a generative AI. The determination unit determines authenticity based on the characteristics of the products. The determination unit determines authenticity based on, for example, the engraving on luxury watches or the stitching characteristics of designer bags. The evaluation unit evaluates the market value of the product determined by the judgment unit. The evaluation unit evaluates the market value of the product using, for example, generative AI. The evaluation unit evaluates the market value of the product based on, for example, past transaction data and market trends. The evaluation unit evaluates, for example, the market value of antique furniture or the transaction price of used goods. The provision unit provides the user with the results evaluated by the evaluation unit. The provision unit provides the evaluation results to the user using, for example, generative AI. The provision unit includes, for example, an interface for providing the evaluation results to the user. The provision unit provides the evaluation results through, for example, a web application or a mobile application. As a result, the online trading platform according to the embodiment allows users to conduct transactions with peace of mind.

[0071] The data collection unit collects images and detailed information of items that users wish to have appraised. Specifically, users can upload images of luxury watches, designer bags, antique furniture, etc., and input detailed information about the items. Users can upload information using smartphones or personal computers. The data collection unit receives image and text data sent from these devices and stores it in a central database. For example, in the case of a luxury watch, the user uploads images of the entire watch, the engraving, and detailed images of the movement. For designer bags, they provide images of the exterior, the inner tags, and detailed stitching. For antique furniture, they upload images of the entire piece, decorative parts, and the year of manufacture and manufacturer's markings. In addition to this image data, the data collection unit also collects detailed information such as the brand name, model name, year of manufacture, purchase price, and current condition of the item. This allows the data collection unit to centrally manage the diverse information provided by users and secure the data necessary for subsequent analysis and evaluation. Furthermore, the data collection unit provides an interface for users to upload information, improving ease of use. For example, it includes features to guide the image upload procedure and an auto-completion function for input forms, enabling users to provide information smoothly. This allows the data collection unit to efficiently and effectively collect information from users, thereby improving the overall performance of the system.

[0072] The analysis unit analyzes the information collected by the collection unit. Specifically, it uses generative AI to analyze the collected information and analyze the characteristics of the products in detail. For example, it analyzes the engravings on luxury watches and the stitching characteristics of brand-name bags. The generative AI uses image recognition technology to analyze product images and extract features unique to specific brands and models. For example, in the case of luxury watches, it analyzes the font and position of the engraving and the structure of the movement, and in the case of brand-name bags, it analyzes the stitching pattern, the texture of the material, and the position of the tag. Furthermore, the generative AI also analyzes text data and compares the detailed product information with image data. For example, it checks whether the brand name and model name entered by the user match the image data and checks for any inconsistencies. Based on these analysis results, the analysis unit grasps the characteristics of the products in detail and provides the data necessary for subsequent authenticity determination and market value assessment. Furthermore, the analysis unit can analyze product characteristics more accurately by comparing them with past data and information collected from other users. For example, it compares them with other products of the same brand or model and evaluates the degree of characteristic agreement. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to quickly and accurately analyze the collected information, improving the reliability and safety of the entire system.

[0073] The judgment unit determines the authenticity of a product based on the information analyzed by the analysis unit. Specifically, it uses a generative AI to determine the authenticity of a product. The generative AI is pre-trained on a large amount of authenticity data to determine authenticity based on the characteristics of the product. For example, it determines authenticity based on the markings on luxury watches or the stitching characteristics of brand-name bags. The generative AI uses image recognition technology to extract the characteristics of the product and compares them with the training data. For example, in the case of luxury watches, it checks whether the font and position of the markings and the structure of the movement match those of a genuine product. In the case of brand-name bags, it checks whether the stitching pattern, the texture of the material, and the position of the tag match those of a genuine product. Furthermore, the generative AI also analyzes text data and compares the detailed product information with image data. For example, it checks whether the brand name and model name entered by the user match the image data and checks for any inconsistencies. Based on these analysis results, the judgment unit can determine the authenticity of a product with high accuracy. Furthermore, the judgment unit can improve the accuracy of its authenticity determination by comparing it with past data and information collected from other users. For example, it can compare the product with other products of the same brand or model to evaluate the degree of matching characteristics. Furthermore, the judgment unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing warnings early. This allows the judgment unit to quickly and accurately determine the authenticity of a product, improving the overall reliability and security of the system.

[0074] The evaluation unit assesses the market value of the product determined by the judgment unit. Specifically, it uses generative AI to evaluate the market value of the product. The generative AI is pre-trained on a large amount of transaction data to evaluate the market value of a product based on past transaction data and market trends. For example, it evaluates the market value of antique furniture and the transaction price of used goods. The generative AI analyzes past transaction data and market trends to evaluate the market value based on the characteristics of the product. For example, in the case of antique furniture, the market value is evaluated based on characteristics such as the year of manufacture, manufacturer, and condition. In the case of used goods, the market value is evaluated based on characteristics such as brand name, model name, years of use, and condition. Furthermore, the generative AI analyzes current market trends in real time and evaluates the market value of the product based on the latest information. For example, if the demand for a particular brand or model is surging, the market value is evaluated taking that impact into account. Based on these analysis results, the evaluation unit can evaluate the market value of the product with high accuracy. Furthermore, the evaluation unit can improve the accuracy of market value evaluation by comparing it with past data and information collected from other users. For example, it can compare it with other products of the same brand or model and evaluate the degree of characteristic agreement. Furthermore, the evaluation unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the evaluation unit to quickly and accurately assess the market value of products, improving the reliability and safety of the entire system.

[0075] The service provider delivers the evaluation results, assessed by the evaluation provider, to the user. Specifically, it uses generative AI to provide evaluation results to the user. The service provider has an interface for delivering evaluation results to the user. For example, it provides evaluation results through web applications and mobile applications. The service provider provides an intuitive and easy-to-use interface so that users can easily check the evaluation results. For example, it visually displays evaluation results in graphs and charts so that users can understand the market value of a product and the results of authenticity judgment at a glance. The service provider also provides support for users to take the next action based on the evaluation results. For example, it has a function to suggest appropriate trading partners and platforms to users who wish to sell a product based on the evaluation results. Furthermore, the service provider can collect feedback from users and continuously improve the accuracy of the evaluation results and the usability of the interface. For example, it updates the training data of the generative AI based on the feedback that users provide on the evaluation results to improve evaluation accuracy. In addition, the service provider can reliably transmit information using multiple communication methods. For example, it uses email and SMS in addition to notifications from web and mobile applications to ensure that important information is delivered reliably. This allows the service provider to quickly and reliably provide evaluation results to users, supporting them in conducting transactions with peace of mind.

[0076] The collection unit can collect images and detailed information of products uploaded by users. For example, users can upload images and detailed information of products using their smartphones or personal computers. For example, users can upload images of luxury watches, designer bags, antique furniture, etc., and input detailed information about the products. This allows the collection unit to efficiently collect information provided by users. Some or all of the above processing in the collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the collection unit can input the images and detailed information uploaded by the user into a generative AI, which can then analyze and collect the information.

[0077] The analysis unit can analyze the characteristics of a product based on the collected information. The analysis unit analyzes the collected information using, for example, a generative AI. The analysis unit analyzes the characteristics of a product in detail, for example, the engraving on a luxury watch or the stitching characteristics of a brand-name bag. This allows the analysis unit to analyze the characteristics of a product in detail. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the collected information into a generative AI, which can then analyze the information and extract characteristics.

[0078] The determination unit can determine the authenticity of a product based on the analyzed features. The determination unit can determine the authenticity of a product using, for example, a generative AI. The determination unit can determine authenticity based on, for example, the features of a product. The determination unit can determine authenticity based on, for example, the engraving on a luxury watch or the stitching features of a brand-name bag. This allows the determination unit to accurately determine the authenticity of a product. Some or all of the above-described processes in the determination unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the determination unit can input the analyzed features into a generative AI, and the generative AI can determine authenticity.

[0079] The evaluation unit can assess the market value of a product based on past transaction data and market trends. The evaluation unit can assess the market value of a product, for example, using a generative AI. The evaluation unit can assess the market value of a product, for example, based on past transaction data and market trends. The evaluation unit can assess the market value of antique furniture, for example, or the transaction price of used goods. This allows the evaluation unit to accurately assess the market value of a product. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the evaluation unit can input past transaction data and market trends into a generative AI, and the generative AI can assess the market value.

[0080] The service provider can provide the evaluated results to the user. The service provider can provide the evaluated results to the user, for example, using a generative AI. The service provider can provide an interface for providing the evaluated results to the user, for example. The service provider can provide the evaluated results through a web application or a mobile application, for example. This allows the service provider to provide the evaluated results to the user. Some or all of the above processing in the service provider may be performed using a generative AI or not. For example, the service provider can input the evaluated results into a generative AI, and the generative AI can provide the evaluated results to the user.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of collecting product images and detailed information based on the estimated emotions. The data collection unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, if the user is excited, the data collection unit immediately collects product images and detailed information. For example, if the user is relaxed, the data collection unit collects product images and detailed information after the user has calmed down. For example, if the user is stressed, the data collection unit delays collection until the user has relaxed. This allows the data collection unit to collect more appropriate information by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using the generative AI or not. For example, the data collection unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the collection timing.

[0082] The data collection unit can analyze the user's past data collection history and select the optimal data collection method. For example, the data collection unit can analyze the user's past data collection history using generative AI. For example, the data collection unit can prioritize suggesting data collection methods that the user has frequently used in the past (such as image uploads and text input). For example, the data collection unit can perform data collection at specific time periods based on the user's past data collection history. For example, the data collection unit can analyze the user's past data collection history and suggest the most efficient data collection method. This enables efficient information collection by allowing the data collection unit to select the optimal data collection method based on past data collection history. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input the user's past data collection history into the generative AI, which can then select the optimal data collection method.

[0083] The data collection unit can filter product images and detailed information based on the user's current areas of interest. For example, the data collection unit can analyze the user's current areas of interest using generative AI. For example, the data collection unit can prioritize collecting products from brands or categories that the user is currently interested in. For example, the data collection unit can filter related product images and detailed information based on the user's current areas of interest. For example, the data collection unit can analyze the user's current areas of interest and collect information on the most relevant products. In this way, the data collection unit can collect highly relevant information by filtering information based on the user's areas of interest. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input the user's areas of interest into the generative AI, which can then perform the filtering.

[0084] The data collection unit can estimate the user's emotions and determine the priority of products to collect based on the estimated user emotions. The data collection unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, if the user is excited, the data collection unit prioritizes collecting information on products that the user is most interested in. For example, if the user is relaxed, the data collection unit collects information on products that the user is likely to be interested in. For example, if the user is stressed, the data collection unit delays collection until the user is relaxed. This allows the data collection unit to collect more appropriate information by determining the priority of products according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using the generative AI or not. For example, the data collection unit can input user emotion data into the generative AI, which can estimate the emotions and determine the priority of products.

[0085] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting product images and detailed information. For example, the data collection unit analyzes the user's geographical location using a generative AI. For example, the data collection unit prioritizes the collection of information from stores and vendors in the user's current location. For example, the data collection unit collects information on products available in the nearest location based on the user's geographical location. For example, the data collection unit analyzes the user's geographical location and collects information on highly relevant products. This allows the data collection unit to prioritize the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generative AI, or without one. For example, the data collection unit can input the user's geographical location into a generative AI, which can then prioritize the collection of highly relevant information.

[0086] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting product images and detailed information. For example, the data collection unit can analyze the user's social media activity using generative AI. For example, the data collection unit can prioritize collecting information on products that the user has shown interest in on social media. For example, the data collection unit can collect relevant product information from the user's social media activity. For example, the data collection unit can analyze the user's social media activity and collect the most relevant product information. In this way, the data collection unit can collect highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input the user's social media activity into generative AI, and the generative AI can collect relevant information.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. The analysis unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit provides visually stimulating analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's emotion data into the generative AI, which can estimate the emotions and adjust the presentation of the analysis.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the product during the analysis. For example, the analysis unit uses a generative AI to analyze the importance of the product. For example, the analysis unit performs a detailed analysis for expensive products. For example, the analysis unit performs a basic analysis for general products. For example, the analysis unit performs a detailed analysis for products that the user is particularly interested in. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the product. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the product into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, the analysis unit uses a generative AI to analyze the product category. For example, in the case of a luxury watch, the analysis unit analyzes the engravings and mechanical parts. For example, in the case of a branded bag, the analysis unit analyzes the stitching and materials. For example, in the case of antique furniture, the analysis unit analyzes the type of wood and the manufacturing date. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the product category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the product category into a generative AI, and the generative AI can apply different analysis algorithms.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, if the user is in a hurry, the analysis unit performs a short, concise analysis. For example, if the user is relaxed, the analysis unit performs a detailed analysis. For example, if the user is excited, the analysis unit performs a visually stimulating analysis. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI, and the generative AI can estimate the emotions and adjust the length of the analysis.

[0091] The analysis unit can determine the priority of analysis based on the product submission date during the analysis process. For example, the analysis unit uses a generative AI to analyze the product submission date. For example, the analysis unit prioritizes the analysis of recently submitted products. For example, the analysis unit postpones the analysis of older products. For example, the analysis unit adjusts the analysis schedule based on the submission date. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the product submission date. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input the product submission date into a generative AI, and the generative AI can determine the priority of analysis.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the products during the analysis. For example, the analysis unit uses a generative AI to analyze the relevance of the products. For example, the analysis unit prioritizes the analysis of highly relevant products. For example, the analysis unit postpones the analysis of less relevant products. For example, the analysis unit adjusts the order of analysis based on relevance. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the products. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the relevance of the products into a generative AI, and the generative AI can adjust the order of analysis.

[0093] The judgment unit can estimate the user's emotions and adjust the criteria for authenticity determination based on the estimated user emotions. The judgment unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, if the user is relaxed, the judgment unit performs a detailed authenticity determination. For example, if the user is in a hurry, the judgment unit performs a concise authenticity determination. For example, if the user is excited, the judgment unit performs a visually stimulating authenticity determination. In this way, the judgment unit can make more appropriate judgments by adjusting the criteria for authenticity determination according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using a generative AI or not using a generative AI. For example, the judgment unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the criteria for determining authenticity.

[0094] The judgment unit can improve the accuracy of its judgment by considering the interrelationships between products when determining authenticity. For example, the judgment unit analyzes the interrelationships between products using a generative AI. For example, the judgment unit determines authenticity by comparing the characteristics of products of the same brand. For example, the judgment unit determines authenticity by comparing the characteristics of products of the same category. For example, the judgment unit improves the accuracy of its judgment by referring to past authenticity judgment results. In this way, the judgment unit improves the accuracy of its judgment by considering the interrelationships between products. Some or all of the above processing in the judgment unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the judgment unit can input the interrelationships between products into a generative AI, and the generative AI can improve the accuracy of its judgment.

[0095] The judgment unit can make a judgment by considering the attribute information of the product submitter when determining authenticity. For example, the judgment unit can analyze the attribute information of the product submitter using a generating AI. For example, the judgment unit can determine authenticity by referring to the submitter's past transaction history. For example, the judgment unit can determine authenticity by considering the submitter's reliability. For example, the judgment unit can determine authenticity by analyzing the submitter's attribute information. As a result, the judgment unit can make a more accurate judgment by considering the attribute information of the product submitter. Some or all of the above processing in the judgment unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the judgment unit can input the submitter's attribute information into a generating AI, and the generating AI can make the judgment.

[0096] The judgment unit can estimate the user's emotions and adjust the order in which it displays the authenticity judgment results based on the estimated user emotions. The judgment unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, if the user is relaxed, the judgment unit displays detailed results first. For example, if the user is in a hurry, the judgment unit displays the main points first. For example, if the user is excited, the judgment unit displays visually stimulating results first. In this way, the judgment unit can provide more appropriate information by adjusting the order in which results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using the generative AI or not. For example, the judgment unit can input the user's emotion data into the generative AI, and the generative AI can estimate the emotions and adjust the order in which results are displayed.

[0097] The judgment unit can make a judgment regarding the authenticity of a product by considering its geographical distribution. For example, the judgment unit can analyze the geographical distribution of the product using a generative AI. For example, the judgment unit can determine authenticity by considering the characteristics of products manufactured in geographically close locations. For example, the judgment unit can determine authenticity by considering the characteristics of products manufactured in geographically distant locations. For example, the judgment unit can adjust the criteria for determining authenticity based on the geographical distribution. This allows the judgment unit to make more accurate judgments by considering the geographical distribution of the product. Some or all of the above-described processes in the judgment unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the judgment unit can input the geographical distribution of the product into a generative AI, and the generative AI can perform the judgment.

[0098] The judgment unit can improve the accuracy of its judgment by referring to relevant literature on the product when determining authenticity. For example, the judgment unit analyzes relevant literature on the product using a generative AI. For example, the judgment unit analyzes the characteristics of the product in detail based on the relevant literature. For example, the judgment unit adjusts the criteria for determining authenticity by referring to relevant literature. For example, the judgment unit refers to past judgment results based on relevant literature. In this way, the judgment unit improves the accuracy of its judgment by referring to relevant literature. Some or all of the above processes in the judgment unit may be performed using a generative AI or not. For example, the judgment unit can input relevant literature into a generative AI, and the generative AI can improve the accuracy of the judgment.

[0099] The evaluation unit can estimate the user's emotions and adjust the market value evaluation method based on the estimated user emotions. The evaluation unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, if the user is relaxed, the evaluation unit performs a detailed market value evaluation. For example, if the user is in a hurry, the evaluation unit performs a concise market value evaluation. For example, if the user is excited, the evaluation unit performs a visually stimulating market value evaluation. This allows the evaluation unit to perform a more appropriate market value evaluation by adjusting the evaluation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using the generative AI or not. For example, the evaluation unit can input user emotion data into the generative AI, and the generative AI can estimate the emotions and adjust the evaluation method.

[0100] The valuation unit can optimize its valuation algorithm by referring to past transaction data when determining market value. For example, the valuation unit analyzes past transaction data using a generative AI. For example, the valuation unit adjusts the market value valuation algorithm based on past transaction data. For example, the valuation unit improves the accuracy of the valuation by referring to past transaction data. For example, the valuation unit analyzes past transaction data and applies the optimal valuation algorithm. In this way, the valuation unit improves the accuracy of its valuation algorithm by referring to past transaction data. Some or all of the above processes in the valuation unit may be performed using a generative AI or not. For example, the valuation unit can input past transaction data into a generative AI, and the generative AI can optimize the valuation algorithm.

[0101] The valuation unit can apply different valuation methods to each product category when determining market value. For example, the valuation unit can analyze product categories using generative AI. For example, in the case of luxury watches, the valuation unit will evaluate based on past transaction data and market trends. For example, in the case of branded bags, the valuation unit will evaluate based on the characteristics of the materials and design. For example, in the case of antique furniture, the valuation unit will evaluate based on the manufacturing date and condition. This allows the valuation unit to determine market value more accurately by applying valuation methods appropriate to the product category. Some or all of the above processing in the valuation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the valuation unit can input product categories into the generative AI, and the generative AI can apply different valuation methods.

[0102] The evaluation unit can estimate the user's emotions and determine the priority of market value evaluations based on the estimated user emotions. The evaluation unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, if the user is relaxed, the evaluation unit prioritizes detailed market value evaluations. For example, if the user is in a hurry, the evaluation unit prioritizes concise market value evaluations. For example, if the user is excited, the evaluation unit prioritizes visually stimulating market value evaluations. This allows the evaluation unit to determine the priority of evaluations according to the user's emotions, enabling more appropriate market value evaluations. 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 evaluation unit may be performed using a generation AI or not. For example, the evaluation unit can input user emotion data into a generation AI, which can estimate emotions and determine the priority of evaluations.

[0103] The valuation unit can analyze changes in valuation based on the product's submission date when conducting market value assessments. For example, the valuation unit can use a generative AI to analyze the product's submission date. For example, the valuation unit can prioritize the evaluation of recently submitted products. For example, the valuation unit can postpone the evaluation of older products. For example, the valuation unit can adjust the evaluation schedule based on the submission date. This allows the valuation unit to conduct more accurate market value assessments by analyzing changes in valuation based on the product's submission date. Some or all of the above processes in the valuation unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the valuation unit can input the product's submission date into a generative AI, which can then analyze the changes in valuation.

[0104] The evaluation unit can perform market value assessments by referring to relevant market data for the product. For example, the evaluation unit can analyze the relevant market data using a generative AI. For example, the evaluation unit can assess the market value of the product based on the relevant market data. For example, the evaluation unit can improve the accuracy of the assessment by referring to the relevant market data. For example, the evaluation unit can perform the optimal assessment by analyzing the relevant market data. As a result, the accuracy of the assessment is improved by the evaluation unit referring to the relevant market data. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the evaluation unit can input relevant market data into a generative AI, and the generative AI can perform the assessment.

[0105] The service provider can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. The service provider estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, if the user is relaxed, the service provider displays detailed evaluation results. For example, if the user is in a hurry, the service provider displays concise evaluation results. For example, if the user is excited, the service provider displays visually stimulating evaluation results. This allows the service provider to provide more appropriate information by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI or not. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the display method.

[0106] The service provider can select the optimal display method by referring to the user's past operation history when providing evaluation results. The service provider can, for example, analyze the user's past operation history using a generation AI. The service provider can, for example, prioritize providing display methods that the user has preferred to use in the past. The service provider can, for example, propose the optimal display method from the user's past operation history. The service provider can, for example, analyze the user's past operation history to provide the most efficient display method. In this way, the service provider can provide the optimal display method by referring to past operation history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's past operation history into a generation AI, and the generation AI can select the optimal display method.

[0107] The service provider can select the optimal display method when providing evaluation results, taking into account the user's device information. For example, the service provider can analyze the user's device information using a generative AI. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by taking device information into consideration. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the user's device information into a generative AI, and the generative AI can select the optimal display method.

[0108] The service provider can estimate the user's emotions and adjust the operation procedures of the evaluation results based on the estimated user emotions. The service provider estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or generative AI. For example, if the user is relaxed, the service provider provides detailed operation procedures. For example, if the user is in a hurry, the service provider provides concise operation procedures. For example, if the user is excited, the service provider provides visually stimulating operation procedures. This allows the service provider to provide more appropriate information by adjusting the operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI or not. For example, the service provider can input user emotion data into a generative AI, and the generative AI can estimate the emotions and adjust the operation procedures.

[0109] The service provider can select the optimal display method when providing evaluation results, taking into account the user's device information. For example, the service provider can analyze the user's device information using a generative AI. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, the service provider can provide the optimal display method by taking device information into consideration. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the user's device information into a generative AI, and the generative AI can select the optimal display method.

[0110] The service provider can select the optimal display method by referring to the user's past operation history when providing evaluation results. The service provider can, for example, analyze the user's past operation history using a generation AI. The service provider can, for example, prioritize providing display methods that the user has preferred to use in the past. The service provider can, for example, propose the optimal display method from the user's past operation history. The service provider can, for example, analyze the user's past operation history to provide the most efficient display method. In this way, the service provider can provide the optimal display method by referring to past operation history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's past operation history into a generation AI, and the generation AI can select the optimal display method.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] Online trading platforms can further analyze users' purchase history and perform authentication and valuation of related items based on the trends of products the user has purchased in the past. For example, if a user has purchased multiple luxury watches in the past, the authentication and valuation of similar luxury watches can be prioritized. Similarly, if a user frequently purchases bags of a particular brand, the authentication and valuation of bags of that brand can be enhanced. Furthermore, if a user purchases antique furniture, the authentication and valuation of similar antique furniture can be performed. This enables more personalized authentication and valuation based on the user's purchase history.

[0113] The data collection unit can estimate the user's emotions and adjust how it collects product images and detailed information based on those emotions. For example, if the user is excited, the unit can provide a simple interface to make it easy for the user to provide information. If the user is relaxed, the unit can provide an interface for entering detailed information. Furthermore, if the user is stressed, the unit can temporarily suspend information collection and wait until the user relaxes. This allows the unit to collect more appropriate information by adjusting its information collection method according to the user's emotions.

[0114] The analysis unit can estimate the user's emotions when analyzing product features and adjust the accuracy of the analysis based on those emotions. For example, if the user is in a hurry, the analysis unit can perform a quick analysis and extract basic features. If the user is relaxed, the analysis unit can perform a more detailed analysis and extract more features. Furthermore, if the user is excited, the analysis unit can provide the analysis results in a visually easy-to-understand format. In this way, the analysis unit can provide more appropriate analysis results by adjusting the accuracy of the analysis according to the user's emotions.

[0115] The judgment unit can estimate the user's emotions when determining the authenticity of a product and adjust the display method of the judgment result based on the estimated emotions. For example, if the user is relaxed, the judgment unit can display a detailed judgment result. If the user is in a hurry, the judgment unit can display a concise judgment result. Furthermore, if the user is excited, the judgment unit can display the judgment result in a visually stimulating format. In this way, the judgment unit can provide more appropriate information by adjusting the display method of the judgment result according to the user's emotions.

[0116] The evaluation unit can estimate the user's emotions when assessing the market value of a product and adjust the level of detail in the evaluation based on those emotions. For example, if the user is relaxed, the evaluation unit can provide a detailed market value assessment. If the user is in a hurry, the evaluation unit can provide a concise market value assessment. Furthermore, if the user is excited, the evaluation unit can provide the market value assessment in a visually easy-to-understand format. In this way, the evaluation unit can provide a more accurate market value assessment by adjusting the level of detail in the evaluation according to the user's emotions.

[0117] When providing evaluation results to a user, the service provider can select the most appropriate display method by referring to the user's past viewing history of evaluation results. For example, if a user has previously preferred to view detailed evaluation results, the service provider can display detailed evaluation results. If a user has previously preferred to view concise evaluation results, the service provider can display concise evaluation results. Furthermore, if a user has previously preferred to view evaluation results in a visually easy-to-understand format, the service provider can display evaluation results in a visually easy-to-understand format. This allows the service provider to provide more appropriate information by selecting the most appropriate display method based on the user's past viewing history.

[0118] The data collection unit can collect product images and detailed information based on regional characteristics, taking into account the user's geographical location. For example, if a user is in a specific region, it can prioritize collecting information on products popular in that region. If a user is traveling, it can collect information on products available in their destination region. Furthermore, if a user is staying in a specific region for an extended period, it can collect product information based on market trends in that region. This allows the data collection unit to collect more relevant information by considering the user's geographical location.

[0119] The analysis unit can analyze product characteristics while considering information about the product's manufacturer and country of manufacture. For example, if a particular brand has different manufacturers, it can analyze the characteristics of each manufacturer. Furthermore, if the product is manufactured in different countries, it can analyze while considering the manufacturing technology and quality standards of each country. In addition, it can refer to past data based on manufacturer and country of manufacture information to perform more accurate analysis. As a result, the analysis unit can perform more accurate characteristic analysis by considering manufacturer and country of manufacture information.

[0120] The authentication unit can determine the authenticity of a product by considering information such as the year of manufacture and the manufacturing lot. For example, if there is variation in quality within a particular year or lot, the unit can use this information to determine authenticity. It can also analyze the characteristics of each year and lot and compare them with past data to determine authenticity. Furthermore, it can consider the characteristics of products manufactured during a specific period based on the year and lot information. As a result, the authentication unit can determine authenticity more accurately by considering information such as the year and lot.

[0121] The evaluation unit can take into account the storage condition and frequency of use of a product when assessing its market value. For example, if a product is stored in good condition, its market value can be assessed higher. Similarly, if a product is used infrequently, its market value can also be assessed higher. Furthermore, by comparing the current market value with past transaction data based on the product's storage condition and frequency of use, a more accurate market value assessment can be made. As a result, the evaluation unit can perform a more accurate market value assessment by considering the product's storage condition and frequency of use.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The collection unit gathers images and detailed information of the items that the user wishes to have appraised. For example, users can upload images of luxury watches, designer bags, antique furniture, etc., using their smartphones or personal computers, and enter detailed information about the items. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it uses a generation AI to analyze the characteristics of products in detail, such as the engravings on luxury watches or the stitching characteristics of brand-name bags. Step 3: The judgment unit determines the authenticity of the product based on the information analyzed by the analysis unit. For example, it may use a generating AI to determine authenticity based on the product's characteristics, such as the engraving on a luxury watch or the stitching characteristics of a brand-name bag. Step 4: The evaluation unit evaluates the market value of the product determined by the judgment unit. For example, it uses a generation AI to evaluate the market value of a product based on past transaction data and market trends, and evaluates the market value of antique furniture, the transaction price of used goods, etc. Step 5: The delivery unit provides the user with the results evaluated by the evaluation unit. For example, it may provide an interface for delivering evaluation results to the user using a generation AI, and deliver the evaluation results through a web application or mobile application.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, evaluation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects images and detailed information of products uploaded by the user using the camera 42 and reception device 38 of the smart device 14. The analysis unit analyzes the collected information using AI generated by the identification processing unit 290 of the data processing unit 12. The determination unit determines the authenticity of the product using the identification processing unit 290 of the data processing unit 12. The evaluation unit evaluates the market value of the product using the identification processing unit 290 of the data processing unit 12. The provision unit provides the evaluation results to the user through the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, evaluation unit, and provision 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 images and detailed information of products uploaded by the user using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected information using AI generated by the identification processing unit 290 of the data processing unit 12, for example. The determination unit determines the authenticity of the product using the identification processing unit 290 of the data processing unit 12, for example. The evaluation unit evaluates the market value of the product using the identification processing unit 290 of the data processing unit 12, for example. The provision unit provides the evaluation results to the user through the speaker 240 of the smart glasses 214, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, evaluation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects images and detailed information of products uploaded by the user using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the collected information using AI generated by the identification processing unit 290 of the data processing unit 12. The determination unit determines the authenticity of the product using the identification processing unit 290 of the data processing unit 12. The evaluation unit evaluates the market value of the product using the identification processing unit 290 of the data processing unit 12. The provision unit provides the evaluation results to the user through the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, evaluation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects images and detailed information of products uploaded by the user using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected information using AI generated by the identification processing unit 290 of the data processing unit 12, for example. The determination unit determines the authenticity of the product using the identification processing unit 290 of the data processing unit 12, for example. The evaluation unit evaluates the market value of the product using the identification processing unit 290 of the data processing unit 12, for example. The provision unit provides the evaluation results to the user through the speaker 240 of the robot 414, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A collection unit that collects product images and detailed information, An analysis unit analyzes the information collected by the aforementioned collection unit, A determination unit that determines the authenticity of a product based on the information analyzed by the aforementioned analysis unit, An evaluation unit that evaluates the market value of the product determined by the determination unit, The system includes a providing unit that provides the user with the results evaluated by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects images and detailed information of products uploaded by users. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected information, we analyze the product's characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 4) The determination unit, The authenticity of a product is determined based on the analyzed characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, The market value of a product is assessed based on past transaction data and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the evaluated results to the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting product images and detailed information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting product images and detailed information, filtering is performed based on the user's current areas of interest. 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 determines the priority of products to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting product images and detailed information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting product images and detailed information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the timing of product submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, The system estimates the user's emotions and adjusts the criteria for determining authenticity based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, When determining authenticity, we improve the accuracy of the determination by considering the interrelationships between products. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, When determining authenticity, the attribute information of the person who submitted the product is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, The system estimates the user's emotions and adjusts the order in which the authenticity determination results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, When determining authenticity, the geographical distribution of the goods is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The determination unit, When determining authenticity, we improve the accuracy of the determination by referring to relevant literature on the product. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, We estimate user sentiment and adjust the market value assessment method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit, When valuing a market value, the valuation algorithm is optimized by referring to past transaction data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit, When valuing a product at market value, different valuation methods are applied to each product category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit, It estimates user sentiment and determines the priority of market value valuation based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The evaluation unit, When valuing a product in the market, analyze how the valuation changes based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The evaluation unit, When valuing a product, the valuation is performed by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing evaluation results, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing evaluation results, the optimal display method will be selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the operation procedures based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing evaluation results, the optimal display method will be selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing evaluation results, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 product images and detailed information, An analysis unit analyzes the information collected by the aforementioned collection unit, A determination unit that determines the authenticity of a product based on the information analyzed by the aforementioned analysis unit, An evaluation unit that evaluates the market value of the product determined by the determination unit, The system includes a providing unit that provides the user with the results evaluated by the evaluation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collects images and detailed information of products uploaded by users. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected information, we analyze the product's characteristics. The system according to feature 1.

4. The determination unit, The authenticity of a product is determined based on the analyzed characteristics. The system according to feature 1.

5. The evaluation unit, The market value of a product is assessed based on past transaction data and market trends. The system according to feature 1.

6. The aforementioned supply unit is, Provide the evaluated results to the user. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting product images and detailed information based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting product images and detailed information, filtering is performed based on the user's current areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of products to collect based on the estimated user emotions. The system according to feature 1.

Images