system

Abstract

The system according to this embodiment aims to efficiently perform authenticity verification based on product images and videos. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an appraisal unit, and a provision unit. The reception unit uploads product images and videos. The analysis unit analyzes the images and videos uploaded by the reception unit. The appraisal unit determines authenticity based on the results of the analysis by the analysis unit. The provision unit provides the appraisal results obtained by the appraisal unit to the user.

Description

Technical Field

[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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes time and effort to authenticate genuine and fake products based on product images or videos, and it is difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently authenticate genuine and fake products based on product images or videos.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an authentication unit, and a provision unit. The reception unit uploads product images or videos. The analysis unit analyzes the images or videos uploaded by the reception unit. The authentication unit determines authenticity based on the results analyzed by the analysis unit. The provision unit provides the authentication result obtained by the authentication unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently perform authenticity verification based on product images and videos. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The authenticity verification system according to an embodiment of the present invention is a system that uses a generating AI to analyze product images and videos and perform authenticity verification. In this system, the user uploads product images or videos, and the generating AI analyzes them to evaluate the authenticity of the product. For example, if a user uploads an image of a Rolex watch, the generating AI analyzes the image and determines whether it matches a Rolex model. As an authentication result, information such as "Matches Rolex model. Probability of authenticity: 88%" or "Highly unlikely to be a diamond. Probability of authenticity: 22%" is provided. This system can also be used on e-commerce sites, allowing users to purchase products with confidence. The specific steps are as follows: First, the user uploads product images or videos. Next, the generating AI analyzes the uploaded images or videos. The generating AI performs image analysis and comparison with past product data to evaluate the authenticity of the product. Finally, the generating AI performs authenticity verification based on the analysis results and provides the authentication result. This allows the user to check the authentication result and purchase products with confidence. This system targets general consumers and addresses the challenge of distinguishing counterfeit branded and expensive goods. The generating AI evaluates the authenticity of products through image analysis and comparison with past product data. This allows users to purchase products with confidence and reduces the risk of buying counterfeit items. The authenticity verification system can analyze product images and videos uploaded by users to determine authenticity.

[0029] The authenticity authentication system according to this embodiment comprises a reception unit, an analysis unit, an authentication unit, and a provision unit. The reception unit accepts product images and videos uploaded by users. The reception unit can accept images and videos in formats such as JPEG, PNG, and MP4. The reception unit temporarily stores the images and videos uploaded by the user and transmits them to the analysis unit. The analysis unit analyzes the images and videos uploaded by the reception unit using a generation AI. The analysis unit extracts product features using image analysis techniques such as deep learning and edge detection. The analysis unit evaluates the authenticity of the product by comparing it with past product data. For example, the analysis unit compares the uploaded images and videos with past product data using database search and similarity calculation. The authentication unit determines the authenticity of the product based on the results analyzed by the analysis unit. The authentication unit determines the authenticity of the product using, for example, a reliability score and a judgment algorithm. The authentication unit provides authentication results including probabilities and detailed explanations regarding the authenticity of the product. The provisioning unit provides the user with the appraisal results obtained by the appraisal unit. The provisioning unit provides the user with the appraisal results, for example, using a notification method or display format. The provisioning unit ensures that the user can purchase products with confidence. As a result, the authenticity appraisal system according to the embodiment can analyze product images and videos uploaded by the user and perform authenticity appraisal.

[0030] The reception desk accepts user uploads of product images and videos. The reception desk can accept images and videos in formats such as JPEG, PNG, and MP4. Specifically, when users upload images and videos via a dedicated web portal or mobile app, the reception desk automatically recognizes these file formats and temporarily stores them in the appropriate format. The reception desk also simultaneously acquires metadata of the uploaded files (e.g., shooting date and time, resolution, file size, etc.) and prepares this information for transmission to the analysis department. Furthermore, the reception desk checks the quality of images and videos uploaded by users and has a function to prompt users to re-upload if any are unclear. For example, if an image is blurry or a video is interrupted, the reception desk automatically detects this and sends a notification to the user requesting re-upload. This allows the analysis department to receive high-quality data, improving the accuracy of authenticity verification. The reception desk also includes security features to safely manage user-uploaded data, protecting user privacy through data encryption and access control. This allows the reception department to provide an environment where users can upload product images and videos with peace of mind, thereby increasing the overall reliability of the system.

[0031] The analysis unit uses generative AI to analyze images and videos uploaded by the reception unit. Specifically, it extracts product features using image analysis techniques such as deep learning and edge detection. The generative AI is pre-trained using a large amount of training data, enabling it to detect even the finest features and patterns of products with high accuracy. For example, the deep learning model analyzes the surface texture, logo placement, and stitching patterns of the product, extracting these features as numerical data. Edge detection technology is used to clarify the contours and shapes of the product and to analyze its appearance in detail. The analysis unit combines these techniques to analyze product features from multiple angles and generate data necessary for evaluating authenticity. Furthermore, the analysis unit evaluates the authenticity of products by comparing them with past product data. Specifically, it compares uploaded images and videos with past product data using database searches and similarity calculations. For example, it checks whether the position and shape of the product's logo match past data and calculates a similarity score. This allows the analysis unit to evaluate the authenticity of products with high accuracy. Furthermore, the analysis unit has the functionality to update analysis results in real time and recalculate the evaluation whenever new data is added. This allows the analysis unit to always perform highly accurate analysis based on the latest information, thereby improving the reliability of the entire system.

[0032] The authentication department determines the authenticity of an item based on the results analyzed by the analysis department. Specifically, it uses a reliability score and a judgment algorithm to determine the authenticity of a product. The reliability score is calculated based on the feature data and similarity score provided by the analysis department and indicates the probability of the product being authentic. For example, if the product's features show a high degree of agreement with past data, the reliability score will be high, and conversely, if there is no agreement, it will be low. The judgment algorithm comprehensively evaluates these scores to finally determine the authenticity of the product. The authentication department provides authentication results that include the probability of the product's authenticity and a detailed explanation. For example, it provides explanations based on specific features such as the position and shape of the product's logo and the sewing pattern, making it easy for users to understand the authentication results. Furthermore, the authentication department has a function to continuously improve the accuracy of the judgment algorithm based on past authentication results and user feedback. As a result, the authentication department can always perform highly accurate authenticity judgments based on the latest information and technology, and provide users with reliable authentication results. Furthermore, the appraisal department is equipped with a function to conduct reviews by multiple experts, and for particularly important appraisal results, expert opinions are incorporated into the final decision. This allows the appraisal department to perform more reliable authenticity determinations, enabling users to use the service with confidence.

[0033] The service provider delivers the appraisal results obtained by the appraisal department to the user. Specifically, it provides the appraisal results to the user using notification methods and display formats. For example, it allows users to check the appraisal results through a dedicated web portal or mobile app. The service provider provides an interface for visually easy-to-understand display of the appraisal results, presenting the user with appraisal results including reliability scores and detailed explanations. Furthermore, the service provider also has a function to provide advice and recommendations to help users take appropriate action based on the appraisal results. For example, if the authenticity is questionable, it guides the user through the return or exchange procedure, and if the authenticity is confirmed, it provides advice on how to use the product with peace of mind. The service provider also has a function to collect user feedback and use it to improve the display and notification methods of appraisal results. This allows the service provider to provide users with quick and appropriate information and improve the overall usability of the system. Furthermore, the service provider can reliably transmit information using multiple notification methods. For example, in addition to notifications from the web portal and mobile app, it uses a combination of email, SMS, and push notifications to ensure that important information reaches the user. This allows the service provider to quickly and reliably provide appraisal results to users, enabling them to purchase products with confidence.

[0034] The analysis unit can perform image analysis and comparison with past product data. For example, the analysis unit can perform image analysis using deep learning. For example, the analysis unit can extract product features and compare them with past product data. The analysis unit can also analyze the contour of a product using edge detection. For example, the analysis unit can analyze the shape and design of a product and compare it with past product data. The analysis unit can also calculate the similarity to past product data using database searches. For example, the analysis unit can match product images with a database and calculate a similarity score. This allows for the evaluation of the authenticity of a product by performing image analysis and comparison with past product data. 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 product images into a generative AI, which can perform image analysis and extract product features.

[0035] The authentication unit can provide authentication results that include probabilities and detailed explanations regarding the authenticity of a product. For example, the authentication unit can calculate the probability of authenticity using Bayesian estimation. For example, the authentication unit can calculate the probability of authenticity based on the product's characteristics. The authentication unit can also evaluate the probability of authenticity using confidence intervals. For example, the authentication unit can set confidence intervals based on the product's characteristics and evaluate the probability of authenticity. The authentication unit can also provide authentication results that include detailed explanations. For example, the authentication unit can provide the product's characteristics and analysis results as text descriptions or charts. This allows users to purchase products with confidence by providing authentication results that include probabilities and detailed explanations regarding the authenticity of a product. Some or all of the above processing in the authentication unit may be performed using or without a generative AI. For example, the authentication unit can input the product's characteristics into a generative AI, which can then calculate the probability of authenticity and generate a detailed explanation.

[0036] The service provider can ensure that users can purchase products with confidence. For example, the service provider can indicate the reliability of a product to the user using a reliability score. For example, the service provider can display the probability of a product being genuine as a reliability score. The service provider can also display user reviews. For example, the service provider can display reviews posted by other users to evaluate the reliability of a product. The service provider can also provide authentication results to the user using notification methods. For example, the service provider can notify the user of the authentication results using email or push notifications. This reduces the risk of purchasing counterfeit goods by allowing users to purchase products with confidence. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can have a generative AI calculate the reliability score of a product and display it to the user.

[0037] The reception unit can accept product images and videos uploaded by users. The reception unit accepts images and videos uploaded by users, for example, by setting file format and size restrictions. For example, the reception unit accepts images and videos in formats such as JPEG, PNG, and MP4. The reception unit can also temporarily store the uploaded images and videos and send them to the analysis unit. For example, the reception unit can save the uploaded files to a server and make them accessible to the analysis unit. This allows the reception unit to obtain product information subject to authenticity verification by accepting product images and videos uploaded by users. Some or all of the above processing in the reception unit may be performed using a generation AI, or not. For example, the reception unit can have a generation AI check the format and size of the uploaded images and videos and accept only appropriate files.

[0038] The reception desk can analyze a user's past upload history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods (images, videos, etc.) that the user has frequently used in the past. For instance, if the reception desk has frequently uploaded images in the past, it will prioritize suggesting image upload methods. The reception desk can also suggest the optimal upload method for a specific time of day based on the user's past upload history. For example, if the reception desk has uploaded during a specific time period in the past, it will suggest the optimal upload method for that time period. Furthermore, the reception desk can select the optimal upload method based on the types of products the user has uploaded in the past. For example, if the reception desk has uploaded products in a specific category in the past, it will select the optimal upload method for that category. In this way, by analyzing the user's past upload history, the reception desk can provide the optimal upload method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past upload history into AI, and the AI ​​can select the optimal upload method.

[0039] The reception desk can filter uploads based on the user's current interests and purchase history. For example, the reception desk may suggest prioritizing the upload of products from brands the user has previously purchased. For instance, if the user has previously purchased products from a specific brand, the reception desk may suggest prioritizing the upload of products from that brand. The reception desk can also prompt the user to upload images and videos of related products based on their current interests. For example, the reception desk may prompt the user to prioritize the upload of images and videos related to products they are currently interested in. The reception desk can also filter uploads based on the user's purchase history to prioritize products from specific categories. For example, if the user has previously purchased products from a specific category, the reception desk will filter uploads to prioritize products from that category. This allows for the priority uploading of highly relevant product information by filtering based on the user's interests and purchase history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's interests and purchase history into an AI, which can then perform the filtering.

[0040] The reception desk can prioritize accepting product images and videos that are highly relevant to the user's geographical location during the upload process. For example, if the reception desk determines that a user is in a specific region, it will prioritize uploading product images and videos that are popular in that region. The reception desk can also suggest uploading region-specific product images and videos based on the user's geographical location. For example, if the reception desk determines that a user is in a specific region, it will suggest uploading product images and videos that are specific to that region. Furthermore, if the reception desk determines that a user is traveling, it can prioritize uploading product images and videos that are highly relevant to their travel destination. For example, if the reception desk determines that a user is traveling, it will prioritize uploading product images and videos that are highly relevant to their travel destination. This improves user convenience by prioritizing the acceptance of product images and videos that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI, which can then select highly relevant product images and videos.

[0041] The reception desk can analyze the user's social media activity during upload and accept relevant product images and videos. For example, the reception desk can prioritize uploading product images and videos that the user has shared on social media. The reception desk can also suggest uploading product images and videos that are of high interest to the user based on their social media activity. The reception desk can also upload relevant product images and videos by referring to posts from brands and influencers that the user follows. This allows the reception desk to prioritize accepting relevant product images and videos by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI, which can then select relevant product images and videos.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the product. For example, for expensive products, the analysis unit's generating AI performs a detailed analysis to check every detail. For example, for expensive products, the analysis unit's generating AI performs a detailed analysis to check every detail. The analysis unit can also perform a standard analysis using the generating AI to check the main points for general products. For example, for general products, the analysis unit's generating AI performs a standard analysis to check the main points for general products. The analysis unit can also perform a simplified analysis using the generating AI to check the basic points for low-priced products. For example, for low-priced products, the analysis unit's generating AI performs a simplified analysis using the generating AI to check the basic points. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the product. Some or all of the above processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input the importance of the product into the generating AI, and the generating AI can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, in the case of a watch, the generation AI can apply a dedicated algorithm for analyzing the characteristics of the watch. Similarly, in the case of a bag, the generation AI can apply a dedicated algorithm for analyzing the material and design of the bag. Similarly, in the case of jewelry, the generation AI can apply a dedicated algorithm for analyzing the quality and cut of the gemstones. By applying different analysis algorithms depending on the product category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the analysis unit can input the product category into the generation AI, which can then select and apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the product submission date. For example, the analysis unit may prioritize analyzing the most recent product images and videos. The analysis unit can also determine the priority of analysis based on the order in which the products were submitted. The analysis unit can also prioritize analyzing products submitted during specific events or sales periods. This allows for efficient analysis by determining the priority of analysis based on the product submission date. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the product submission date into a generation AI, which can then determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of products during the analysis process. For example, the analysis unit can group together products of the same brand. The analysis unit can also group together products of the same category. The analysis unit can also prioritize the analysis of highly relevant products based on user interests. This allows for efficient analysis by adjusting the order of analysis based on the relevance of products. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the relevance of products into a generation AI, which can then adjust the order of analysis.

[0046] The appraisal unit can select the optimal appraisal method by analyzing the user's past appraisal results during the appraisal process. For example, the appraisal unit can select the optimal appraisal method based on the results of items the user has previously appraised. The appraisal unit can also select the optimal appraisal method for a specific brand or category based on the user's past appraisal results. The appraisal unit can also analyze the user's past appraisal history and select the most efficient appraisal method. This allows the appraisal unit to provide the optimal appraisal method by analyzing the user's past appraisal results. Some or all of the above processing in the appraisal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the appraisal unit can input the user's past appraisal results into a generative AI, which can then select the optimal appraisal method.

[0047] The appraisal unit can customize its appraisal methods based on the user's current interests during the appraisal process. For example, the appraisal unit may prioritize appraising products from brands that the user is currently interested in. The appraisal unit can also customize its appraisal methods for related products based on the user's current interests. The appraisal unit can also prioritize appraising products from specific categories based on the user's interests. By customizing the appraisal methods based on the user's current interests, the appraisal unit can provide the user with the most suitable appraisal result. Some or all of the above-described processes in the appraisal unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the appraisal unit can input the user's current interests into a generative AI, which can then customize the appraisal methods.

[0048] The service provider can select the optimal display method by referring to the user's past operation history when providing appraisal results. For example, the service provider may prioritize providing display methods that the user has preferred to use in the past. The service provider can also suggest specific display methods based on the user's past operation history. For example, the service provider may analyze the user's past operation history and suggest specific display methods. The service provider can also provide the optimal display method for devices that the user has used in the past. For example, the service provider may provide the optimal display method for devices that the user has used in the past. This allows the service provider to provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past operation history into AI, and the AI ​​can select the optimal display method.

[0049] The service provider can customize the displayed content based on the user's current interests and purchase history when providing appraisal results. For example, the service provider can provide detailed appraisal results for products from brands that the user is currently interested in. The service provider can also display appraisal results for related products based on the user's purchase history. For example, the service provider can analyze the user's purchase history and display appraisal results for related products. The service provider can also provide detailed appraisal results for products in a specific category based on the user's interests. For example, the service provider can provide detailed appraisal results for products in a specific category based on the user's interests. This allows the service provider to provide the user with the most relevant information by customizing the displayed content based on the user's current interests and purchase history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's interests and purchase history into the AI, which can then customize the displayed content.

[0050] The service provider can select the optimal display method when providing appraisal results, taking into account the user's device information. 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 smartphone, the service provider can provide a display method that matches the screen size. The service provider can also provide a display method optimized for larger screens if the user is using a tablet. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into the AI, and the AI ​​can select the optimal display method.

[0051] The service provider can provide multilingual displays according to the user's language settings when providing appraisal results. For example, the service provider can automatically set the language of the appraisal results based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. The service provider can also provide appraisal results in a specific language if the user selects one. This allows the service provider to provide the user with the most relevant information by providing multilingual displays according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's language settings into the AI, which can then provide multilingual displays.

[0052] The service provider can, when providing appraisal results, refer to the user's calendar information to display results based on their schedule. For example, the service provider can refer to the schedule registered in the user's calendar and provide appraisal results for related products. The service provider can also display appraisal results for products related to a specific event based on the user's calendar information. The service provider can also provide the most suitable display method based on the user's calendar information to match their schedule. This allows the service provider to provide the most suitable display based on the user's schedule by referring to their calendar information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's calendar information into the AI, which can then perform the display based on the schedule.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can analyze a user's past upload history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods (images, videos, etc.) that the user has frequently used in the past. For instance, if the reception desk has frequently uploaded images in the past, it will prioritize suggesting image upload methods. The reception desk can also suggest the optimal upload method for a specific time of day based on the user's past upload history. For example, if the reception desk has uploaded during a specific time period in the past, it will suggest the optimal upload method for that time period. Furthermore, the reception desk can select the optimal upload method based on the types of products the user has uploaded in the past. For example, if the reception desk has uploaded products in a specific category in the past, it will select the optimal upload method for that category. In this way, by analyzing the user's past upload history, the reception desk can provide the optimal upload method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past upload history into AI, and the AI ​​can select the optimal upload method.

[0055] The reception desk can filter uploads based on the user's current interests and purchase history. For example, the reception desk may suggest prioritizing the upload of products from brands the user has previously purchased. For instance, if the user has previously purchased products from a specific brand, the reception desk may suggest prioritizing the upload of products from that brand. The reception desk can also prompt the user to upload images and videos of related products based on their current interests. For example, the reception desk may prompt the user to prioritize the upload of images and videos related to products they are currently interested in. The reception desk can also filter uploads based on the user's purchase history to prioritize products from specific categories. For example, if the user has previously purchased products from a specific category, the reception desk will filter uploads to prioritize products from that category. This allows for the priority uploading of highly relevant product information by filtering based on the user's interests and purchase history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's interests and purchase history into an AI, which can then perform the filtering.

[0056] The reception desk can prioritize accepting product images and videos that are highly relevant to the user's geographical location during the upload process. For example, if the reception desk determines that a user is in a specific region, it will prioritize uploading product images and videos that are popular in that region. The reception desk can also suggest uploading region-specific product images and videos based on the user's geographical location. For example, if the reception desk determines that a user is in a specific region, it will suggest uploading product images and videos that are specific to that region. Furthermore, if the reception desk determines that a user is traveling, it can prioritize uploading product images and videos that are highly relevant to their travel destination. For example, if the reception desk determines that a user is traveling, it will prioritize uploading product images and videos that are highly relevant to their travel destination. This improves user convenience by prioritizing the acceptance of product images and videos that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI, which can then select highly relevant product images and videos.

[0057] The reception desk can analyze the user's social media activity during upload and accept relevant product images and videos. For example, the reception desk can prioritize uploading product images and videos that the user has shared on social media. The reception desk can also suggest uploading product images and videos that are of high interest to the user based on their social media activity. The reception desk can also upload relevant product images and videos by referring to posts from brands and influencers that the user follows. This allows the reception desk to prioritize accepting relevant product images and videos by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI, which can then select relevant product images and videos.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the product. For example, for expensive products, the analysis unit's generating AI performs a detailed analysis to check every detail. For example, for expensive products, the analysis unit's generating AI performs a detailed analysis to check every detail. The analysis unit can also perform a standard analysis using the generating AI to check the main points for general products. For example, for general products, the analysis unit's generating AI performs a standard analysis to check the main points for general products. The analysis unit can also perform a simplified analysis using the generating AI to check the basic points for low-priced products. For example, for low-priced products, the analysis unit's generating AI performs a simplified analysis using the generating AI to check the basic points. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the product. Some or all of the above processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input the importance of the product into the generating AI, and the generating AI can adjust the level of detail of the analysis.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk accepts product images and videos uploaded by users. The reception desk can accept images and videos in formats such as JPEG, PNG, and MP4. The reception desk temporarily stores the images and videos uploaded by users and sends them to the analysis department. Step 2: The analysis unit uses a generation AI to analyze the images and videos uploaded by the reception unit. The analysis unit extracts product features using image analysis techniques such as deep learning and edge detection. The analysis unit compares the uploaded images and videos with past product data to evaluate their authenticity. For example, the analysis unit compares the uploaded images and videos with past product data using database searches and similarity calculations. Step 3: The appraisal unit determines the authenticity of the item based on the results analyzed by the analysis unit. The appraisal unit determines the authenticity of the item using, for example, a reliability score or a judgment algorithm. The appraisal unit provides appraisal results that include probabilities and detailed explanations regarding the authenticity of the item. Step 4: The service provider provides the user with the appraisal results obtained by the appraisal service provider. The service provider provides the user with the appraisal results, for example, using notification methods and display formats. The service provider ensures that the user can purchase the product with confidence.

[0061] (Example of form 2) The authenticity verification system according to an embodiment of the present invention is a system that uses a generating AI to analyze product images and videos and perform authenticity verification. In this system, the user uploads product images or videos, and the generating AI analyzes them to evaluate the authenticity of the product. For example, if a user uploads an image of a Rolex watch, the generating AI analyzes the image and determines whether it matches a Rolex model. As an authentication result, information such as "Matches Rolex model. Probability of authenticity: 88%" or "Highly unlikely to be a diamond. Probability of authenticity: 22%" is provided. This system can also be used on e-commerce sites, allowing users to purchase products with confidence. The specific steps are as follows: First, the user uploads product images or videos. Next, the generating AI analyzes the uploaded images or videos. The generating AI performs image analysis and comparison with past product data to evaluate the authenticity of the product. Finally, the generating AI performs authenticity verification based on the analysis results and provides the authentication result. This allows the user to check the authentication result and purchase products with confidence. This system targets general consumers and addresses the challenge of distinguishing counterfeit branded and expensive goods. The generating AI evaluates the authenticity of products through image analysis and comparison with past product data. This allows users to purchase products with confidence and reduces the risk of buying counterfeit items. The authenticity verification system can analyze product images and videos uploaded by users to determine authenticity.

[0062] The authenticity authentication system according to this embodiment comprises a reception unit, an analysis unit, an authentication unit, and a provision unit. The reception unit accepts product images and videos uploaded by users. The reception unit can accept images and videos in formats such as JPEG, PNG, and MP4. The reception unit temporarily stores the images and videos uploaded by the user and transmits them to the analysis unit. The analysis unit analyzes the images and videos uploaded by the reception unit using a generation AI. The analysis unit extracts product features using image analysis techniques such as deep learning and edge detection. The analysis unit evaluates the authenticity of the product by comparing it with past product data. For example, the analysis unit compares the uploaded images and videos with past product data using database search and similarity calculation. The authentication unit determines the authenticity of the product based on the results analyzed by the analysis unit. The authentication unit determines the authenticity of the product using, for example, a reliability score and a judgment algorithm. The authentication unit provides authentication results including probabilities and detailed explanations regarding the authenticity of the product. The provisioning unit provides the user with the appraisal results obtained by the appraisal unit. The provisioning unit provides the user with the appraisal results, for example, using a notification method or display format. The provisioning unit ensures that the user can purchase products with confidence. As a result, the authenticity appraisal system according to the embodiment can analyze product images and videos uploaded by the user and perform authenticity appraisal.

[0063] The reception desk accepts user uploads of product images and videos. The reception desk can accept images and videos in formats such as JPEG, PNG, and MP4. Specifically, when users upload images and videos via a dedicated web portal or mobile app, the reception desk automatically recognizes these file formats and temporarily stores them in the appropriate format. The reception desk also simultaneously acquires metadata of the uploaded files (e.g., shooting date and time, resolution, file size, etc.) and prepares this information for transmission to the analysis department. Furthermore, the reception desk checks the quality of images and videos uploaded by users and has a function to prompt users to re-upload if any are unclear. For example, if an image is blurry or a video is interrupted, the reception desk automatically detects this and sends a notification to the user requesting re-upload. This allows the analysis department to receive high-quality data, improving the accuracy of authenticity verification. The reception desk also includes security features to safely manage user-uploaded data, protecting user privacy through data encryption and access control. This allows the reception department to provide an environment where users can upload product images and videos with peace of mind, thereby increasing the overall reliability of the system.

[0064] The analysis unit uses generative AI to analyze images and videos uploaded by the reception unit. Specifically, it extracts product features using image analysis techniques such as deep learning and edge detection. The generative AI is pre-trained using a large amount of training data, enabling it to detect even the finest features and patterns of products with high accuracy. For example, the deep learning model analyzes the surface texture, logo placement, and stitching patterns of the product, extracting these features as numerical data. Edge detection technology is used to clarify the contours and shapes of the product and to analyze its appearance in detail. The analysis unit combines these techniques to analyze product features from multiple angles and generate data necessary for evaluating authenticity. Furthermore, the analysis unit evaluates the authenticity of products by comparing them with past product data. Specifically, it compares uploaded images and videos with past product data using database searches and similarity calculations. For example, it checks whether the position and shape of the product's logo match past data and calculates a similarity score. This allows the analysis unit to evaluate the authenticity of products with high accuracy. Furthermore, the analysis unit has the functionality to update analysis results in real time and recalculate the evaluation whenever new data is added. This allows the analysis unit to always perform highly accurate analysis based on the latest information, thereby improving the reliability of the entire system.

[0065] The authentication department determines the authenticity of an item based on the results analyzed by the analysis department. Specifically, it uses a reliability score and a judgment algorithm to determine the authenticity of a product. The reliability score is calculated based on the feature data and similarity score provided by the analysis department and indicates the probability of the product being authentic. For example, if the product's features show a high degree of agreement with past data, the reliability score will be high, and conversely, if there is no agreement, it will be low. The judgment algorithm comprehensively evaluates these scores to finally determine the authenticity of the product. The authentication department provides authentication results that include the probability of the product's authenticity and a detailed explanation. For example, it provides explanations based on specific features such as the position and shape of the product's logo and the sewing pattern, making it easy for users to understand the authentication results. Furthermore, the authentication department has a function to continuously improve the accuracy of the judgment algorithm based on past authentication results and user feedback. As a result, the authentication department can always perform highly accurate authenticity judgments based on the latest information and technology, and provide users with reliable authentication results. Furthermore, the appraisal department is equipped with a function to conduct reviews by multiple experts, and for particularly important appraisal results, expert opinions are incorporated into the final decision. This allows the appraisal department to perform more reliable authenticity determinations, enabling users to use the service with confidence.

[0066] The service provider delivers the appraisal results obtained by the appraisal department to the user. Specifically, it provides the appraisal results to the user using notification methods and display formats. For example, it allows users to check the appraisal results through a dedicated web portal or mobile app. The service provider provides an interface for visually easy-to-understand display of the appraisal results, presenting the user with appraisal results including reliability scores and detailed explanations. Furthermore, the service provider also has a function to provide advice and recommendations to help users take appropriate action based on the appraisal results. For example, if the authenticity is questionable, it guides the user through the return or exchange procedure, and if the authenticity is confirmed, it provides advice on how to use the product with peace of mind. The service provider also has a function to collect user feedback and use it to improve the display and notification methods of appraisal results. This allows the service provider to provide users with quick and appropriate information and improve the overall usability of the system. Furthermore, the service provider can reliably transmit information using multiple notification methods. For example, in addition to notifications from the web portal and mobile app, it uses a combination of email, SMS, and push notifications to ensure that important information reaches the user. This allows the service provider to quickly and reliably provide appraisal results to users, enabling them to purchase products with confidence.

[0067] The analysis unit can perform image analysis and comparison with past product data. For example, the analysis unit can perform image analysis using deep learning. For example, the analysis unit can extract product features and compare them with past product data. The analysis unit can also analyze the contour of a product using edge detection. For example, the analysis unit can analyze the shape and design of a product and compare it with past product data. The analysis unit can also calculate the similarity to past product data using database searches. For example, the analysis unit can match product images with a database and calculate a similarity score. This allows for the evaluation of the authenticity of a product by performing image analysis and comparison with past product data. 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 product images into a generative AI, which can perform image analysis and extract product features.

[0068] The authentication unit can provide authentication results that include probabilities and detailed explanations regarding the authenticity of a product. For example, the authentication unit can calculate the probability of authenticity using Bayesian estimation. For example, the authentication unit can calculate the probability of authenticity based on the product's characteristics. The authentication unit can also evaluate the probability of authenticity using confidence intervals. For example, the authentication unit can set confidence intervals based on the product's characteristics and evaluate the probability of authenticity. The authentication unit can also provide authentication results that include detailed explanations. For example, the authentication unit can provide the product's characteristics and analysis results as text descriptions or charts. This allows users to purchase products with confidence by providing authentication results that include probabilities and detailed explanations regarding the authenticity of a product. Some or all of the above processing in the authentication unit may be performed using or without a generative AI. For example, the authentication unit can input the product's characteristics into a generative AI, which can then calculate the probability of authenticity and generate a detailed explanation.

[0069] The service provider can ensure that users can purchase products with confidence. For example, the service provider can indicate the reliability of a product to the user using a reliability score. For example, the service provider can display the probability of a product being genuine as a reliability score. The service provider can also display user reviews. For example, the service provider can display reviews posted by other users to evaluate the reliability of a product. The service provider can also provide authentication results to the user using notification methods. For example, the service provider can notify the user of the authentication results using email or push notifications. This reduces the risk of purchasing counterfeit goods by allowing users to purchase products with confidence. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can have a generative AI calculate the reliability score of a product and display it to the user.

[0070] The reception unit can accept product images and videos uploaded by users. The reception unit accepts images and videos uploaded by users, for example, by setting file format and size restrictions. For example, the reception unit accepts images and videos in formats such as JPEG, PNG, and MP4. The reception unit can also temporarily store the uploaded images and videos and send them to the analysis unit. For example, the reception unit can save the uploaded files to a server and make them accessible to the analysis unit. This allows the reception unit to obtain product information subject to authenticity verification by accepting product images and videos uploaded by users. Some or all of the above processing in the reception unit may be performed using a generation AI, or not. For example, the reception unit can have a generation AI check the format and size of the uploaded images and videos and accept only appropriate files.

[0071] The reception desk can estimate the user's emotions and adjust the timing of image and video uploads based on the estimated emotions. For example, if the user is anxious, the reception desk can simplify the upload process to allow for a quick upload. For instance, if the reception desk determines that the user is anxious, it will only require the minimum necessary information and allow for a quick upload. Furthermore, if the user is relaxed, the reception desk can provide detailed guidance and carefully explain the upload process. For example, if the reception desk determines that the user is relaxed, it will display detailed instructions and carefully guide the upload. Additionally, if the user is feeling anxious, the reception desk can display support messages to help them upload with confidence. For example, if the reception desk determines that the user is feeling anxious, it will display support messages and assist with the upload process. This allows for improved user convenience by adjusting the upload timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using a generative AI, or they may not be performed using a generative AI. For example, the reception desk can input the user's facial expressions and voice data into a generative AI, which can then estimate emotions and adjust the upload timing.

[0072] The reception desk can analyze a user's past upload history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods (images, videos, etc.) that the user has frequently used in the past. For instance, if the reception desk has frequently uploaded images in the past, it will prioritize suggesting image upload methods. The reception desk can also suggest the optimal upload method for a specific time of day based on the user's past upload history. For example, if the reception desk has uploaded during a specific time period in the past, it will suggest the optimal upload method for that time period. Furthermore, the reception desk can select the optimal upload method based on the types of products the user has uploaded in the past. For example, if the reception desk has uploaded products in a specific category in the past, it will select the optimal upload method for that category. In this way, by analyzing the user's past upload history, the reception desk can provide the optimal upload method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past upload history into AI, and the AI ​​can select the optimal upload method.

[0073] The reception desk can filter uploads based on the user's current interests and purchase history. For example, the reception desk may suggest prioritizing the upload of products from brands the user has previously purchased. For instance, if the user has previously purchased products from a specific brand, the reception desk may suggest prioritizing the upload of products from that brand. The reception desk can also prompt the user to upload images and videos of related products based on their current interests. For example, the reception desk may prompt the user to prioritize the upload of images and videos related to products they are currently interested in. The reception desk can also filter uploads based on the user's purchase history to prioritize products from specific categories. For example, if the user has previously purchased products from a specific category, the reception desk will filter uploads to prioritize products from that category. This allows for the priority uploading of highly relevant product information by filtering based on the user's interests and purchase history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's interests and purchase history into an AI, which can then perform the filtering.

[0074] The reception desk can estimate the user's emotions and prioritize the images and videos to be uploaded based on the estimated emotions. For example, if the reception desk determines that the user is excited, it will prioritize uploading the latest product images and videos. The reception desk can also suggest re-uploading previously uploaded images and videos if the user is calm. The reception desk can also prioritize uploading images and videos of highly reliable products if the user is feeling anxious. This improves user convenience by prioritizing the images and videos to be uploaded according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using a generative AI, or they may not be performed using a generative AI. For example, the reception desk can input the user's facial expressions and voice data into a generative AI, which can then estimate emotions and determine the priority of images and videos to be uploaded.

[0075] The reception desk can prioritize accepting product images and videos that are highly relevant to the user's geographical location during the upload process. For example, if the reception desk determines that a user is in a specific region, it will prioritize uploading product images and videos that are popular in that region. The reception desk can also suggest uploading region-specific product images and videos based on the user's geographical location. For example, if the reception desk determines that a user is in a specific region, it will suggest uploading product images and videos that are specific to that region. Furthermore, if the reception desk determines that a user is traveling, it can prioritize uploading product images and videos that are highly relevant to their travel destination. For example, if the reception desk determines that a user is traveling, it will prioritize uploading product images and videos that are highly relevant to their travel destination. This improves user convenience by prioritizing the acceptance of product images and videos that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI, which can then select highly relevant product images and videos.

[0076] The reception desk can analyze the user's social media activity during upload and accept relevant product images and videos. For example, the reception desk can prioritize uploading product images and videos that the user has shared on social media. The reception desk can also suggest uploading product images and videos that are of high interest to the user based on their social media activity. The reception desk can also upload relevant product images and videos by referring to posts from brands and influencers that the user follows. This allows the reception desk to prioritize accepting relevant product images and videos by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI, which can then select relevant product images and videos.

[0077] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can generate an AI that produces visually stimulating analysis results. For example, if the analysis unit determines that the user is excited, the AI ​​will produce a visually stimulating analysis result. The analysis unit can also generate an AI that produces detailed and thorough analysis results if the user is relaxed. For example, if the analysis unit determines that the user is relaxed, the AI ​​will produce a detailed and thorough analysis result. The analysis unit can also generate an AI that produces concise and easy-to-understand analysis results if the user is feeling anxious. For example, if the analysis unit determines that the user is feeling anxious, the AI ​​will produce a concise and easy-to-understand analysis result. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input user facial expressions and voice data into a generating AI, which can then estimate emotions and adjust the method of expression for the analysis.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the product. For example, for expensive products, the analysis unit's generating AI performs a detailed analysis to check every detail. For example, for expensive products, the analysis unit's generating AI performs a detailed analysis to check every detail. The analysis unit can also perform a standard analysis using the generating AI to check the main points for general products. For example, for general products, the analysis unit's generating AI performs a standard analysis to check the main points for general products. The analysis unit can also perform a simplified analysis using the generating AI to check the basic points for low-priced products. For example, for low-priced products, the analysis unit's generating AI performs a simplified analysis using the generating AI to check the basic points. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the product. Some or all of the above processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input the importance of the product into the generating AI, and the generating AI can adjust the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, in the case of a watch, the generation AI can apply a dedicated algorithm for analyzing the characteristics of the watch. Similarly, in the case of a bag, the generation AI can apply a dedicated algorithm for analyzing the material and design of the bag. Similarly, in the case of jewelry, the generation AI can apply a dedicated algorithm for analyzing the quality and cut of the gemstones. By applying different analysis algorithms depending on the product category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the analysis unit can input the product category into the generation AI, which can then select and apply an appropriate analysis algorithm.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can generate a short, concise analysis result using the generating AI. For example, if the analysis unit determines that the user is in a hurry, the generating AI will provide a short, concise analysis result using the generating AI. The analysis unit can also generate a longer analysis result with detailed explanations if the user is relaxed. For example, if the analysis unit determines that the user is relaxed, the generating AI will provide a longer analysis result with detailed explanations. The analysis unit can also generate an analysis result with visually stimulating effects if the user is excited. For example, if the analysis unit determines that the user is excited, the generating AI will provide an analysis result with visually stimulating effects. In this way, by adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis result. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generating AI. Generating AIs include, but are not limited to, text generation AIs (e.g., LLMs) or multimodal generation AIs. Some or all of the above-described processing in the analysis unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input the user's facial expressions and voice data into a generating AI, which can then estimate emotions and adjust the length of the analysis.

[0081] The analysis unit can determine the priority of analysis based on the product submission date. For example, the analysis unit may prioritize analyzing the most recent product images and videos. The analysis unit can also determine the priority of analysis based on the order in which the products were submitted. The analysis unit can also prioritize analyzing products submitted during specific events or sales periods. This allows for efficient analysis by determining the priority of analysis based on the product submission date. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the product submission date into a generation AI, which can then determine the priority of analysis.

[0082] The analysis unit can adjust the order of analysis based on the relevance of products during the analysis process. For example, the analysis unit can group together products of the same brand. The analysis unit can also group together products of the same category. The analysis unit can also prioritize the analysis of highly relevant products based on user interests. This allows for efficient analysis by adjusting the order of analysis based on the relevance of products. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the relevance of products into a generation AI, which can then adjust the order of analysis.

[0083] The assessment unit can estimate the user's emotions and adjust the assessment method based on the estimated emotions. For example, if the assessment unit determines that the user is excited, it can generate an AI that produces a visually stimulating assessment result. For example, if the assessment unit determines that the user is excited, it can generate an AI that produces a visually stimulating assessment result. The assessment unit can also generate an AI that produces a detailed and thorough assessment result if the user is relaxed. For example, if the assessment unit determines that the user is relaxed, it can generate an AI that produces a detailed and thorough assessment result. The assessment unit can also generate an AI that produces a concise and easy-to-understand assessment result if the user is feeling anxious. For example, if the assessment unit determines that the user is feeling anxious, it can generate an AI that produces a concise and easy-to-understand assessment result. In this way, by adjusting the assessment method according to the user's emotions, the system can provide the user with the most optimal assessment result. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the appraisal unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the appraisal unit can input the user's facial expressions and voice data into a generative AI, which can then estimate emotions and adjust the appraisal method.

[0084] The appraisal unit can select the optimal appraisal method by analyzing the user's past appraisal results during the appraisal process. For example, the appraisal unit can select the optimal appraisal method based on the results of items the user has previously appraised. The appraisal unit can also select the optimal appraisal method for a specific brand or category based on the user's past appraisal results. The appraisal unit can also analyze the user's past appraisal history and select the most efficient appraisal method. This allows the appraisal unit to provide the optimal appraisal method by analyzing the user's past appraisal results. Some or all of the above processing in the appraisal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the appraisal unit can input the user's past appraisal results into a generative AI, which can then select the optimal appraisal method.

[0085] The appraisal unit can customize its appraisal methods based on the user's current interests during the appraisal process. For example, the appraisal unit may prioritize appraising products from brands that the user is currently interested in. The appraisal unit can also customize its appraisal methods for related products based on the user's current interests. The appraisal unit can also prioritize appraising products from specific categories based on the user's interests. By customizing the appraisal methods based on the user's current interests, the appraisal unit can provide the user with the most suitable appraisal result. Some or all of the above-described processes in the appraisal unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the appraisal unit can input the user's current interests into a generative AI, which can then customize the appraisal methods.

[0086] The service provider can estimate the user's emotions and adjust the display method of the assessment results based on the estimated emotions. For example, if the service provider determines that the user is excited, it can provide a visually stimulating display method. For example, if the service provider determines that the user is excited, it can provide a visually stimulating display method. The service provider can also provide a detailed and polite display method if the user is relaxed. For example, if the service provider determines that the user is relaxed, it can provide a detailed and polite display method. The service provider can also provide a concise and easy-to-understand display method if the user is feeling anxious. For example, if the service provider determines that the user is feeling anxious, it can provide a concise and easy-to-understand display method. By adjusting the display method of the assessment results according to the user's emotions, it becomes possible to provide the optimal display for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using a generative AI or not using a generative AI. For example, the service provider can input the user's facial expressions and voice data into a generating AI, which can then estimate emotions and adjust the display method.

[0087] The service provider can select the optimal display method by referring to the user's past operation history when providing appraisal results. For example, the service provider may prioritize providing display methods that the user has preferred to use in the past. The service provider can also suggest specific display methods based on the user's past operation history. For example, the service provider may analyze the user's past operation history and suggest specific display methods. The service provider can also provide the optimal display method for devices that the user has used in the past. For example, the service provider may provide the optimal display method for devices that the user has used in the past. This allows the service provider to provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past operation history into AI, and the AI ​​can select the optimal display method.

[0088] The service provider can customize the displayed content based on the user's current interests and purchase history when providing appraisal results. For example, the service provider can provide detailed appraisal results for products from brands that the user is currently interested in. The service provider can also display appraisal results for related products based on the user's purchase history. For example, the service provider can analyze the user's purchase history and display appraisal results for related products. The service provider can also provide detailed appraisal results for products in a specific category based on the user's interests. For example, the service provider can provide detailed appraisal results for products in a specific category based on the user's interests. This allows the service provider to provide the user with the most relevant information by customizing the displayed content based on the user's current interests and purchase history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's interests and purchase history into the AI, which can then customize the displayed content.

[0089] The service provider can estimate the user's emotions and adjust the operation procedure of the assessment results based on the estimated emotions of the user. For example, if the service provider determines that the user is nervous, it can provide a simple and intuitive operation procedure. For example, if the service provider determines that the user is nervous, it can provide a simple and intuitive operation procedure. The service provider can also provide a detailed operation procedure if the user is relaxed. For example, if the service provider determines that the user is relaxed, it can provide a detailed operation procedure. The service provider can also provide a procedure that allows for quick operation if the user is in a hurry. For example, if the service provider determines that the user is in a hurry, it can provide a procedure that allows for quick operation. In this way, by adjusting the operation procedure according to the user's emotions, the optimal operation for the user can be achieved. 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 service provider may be performed using generative AI or not using generative AI. For example, the service provider can input the user's facial expressions and voice data into a generating AI, which can then estimate emotions and adjust the operating procedures accordingly.

[0090] The service provider can select the optimal display method when providing appraisal results, taking into account the user's device information. 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 smartphone, the service provider can provide a display method that matches the screen size. The service provider can also provide a display method optimized for larger screens if the user is using a tablet. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into the AI, and the AI ​​can select the optimal display method.

[0091] The service provider can provide multilingual displays according to the user's language settings when providing appraisal results. For example, the service provider can automatically set the language of the appraisal results based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. The service provider can also provide appraisal results in a specific language if the user selects one. This allows the service provider to provide the user with the most relevant information by providing multilingual displays according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's language settings into the AI, which can then provide multilingual displays.

[0092] The service provider can, when providing appraisal results, refer to the user's calendar information to display results based on their schedule. For example, the service provider can refer to the schedule registered in the user's calendar and provide appraisal results for related products. The service provider can also display appraisal results for products related to a specific event based on the user's calendar information. The service provider can also provide the most suitable display method based on the user's calendar information to match their schedule. This allows the service provider to provide the most suitable display based on the user's schedule by referring to their calendar information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's calendar information into the AI, which can then perform the display based on the schedule.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The reception desk can estimate the user's emotions and adjust the timing of image and video uploads based on the estimated emotions. For example, if the user is anxious, the reception desk can simplify the upload process to allow for a quick upload. For instance, if the reception desk determines that the user is anxious, it will only require the minimum necessary information and allow for a quick upload. Furthermore, if the user is relaxed, the reception desk can provide detailed guidance and carefully explain the upload process. For example, if the reception desk determines that the user is relaxed, it will display detailed instructions and carefully guide the upload. Additionally, if the user is feeling anxious, the reception desk can display support messages to help them upload with confidence. For example, if the reception desk determines that the user is feeling anxious, it will display support messages and assist with the upload process. This allows for improved user convenience by adjusting the upload timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using a generative AI, or they may not be performed using a generative AI. For example, the reception desk can input the user's facial expressions and voice data into a generative AI, which can then estimate emotions and adjust the upload timing.

[0095] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can generate an AI that produces visually stimulating analysis results. For example, if the analysis unit determines that the user is excited, the AI ​​will produce a visually stimulating analysis result. The analysis unit can also generate an AI that produces detailed and thorough analysis results if the user is relaxed. For example, if the analysis unit determines that the user is relaxed, the AI ​​will produce a detailed and thorough analysis result. The analysis unit can also generate an AI that produces concise and easy-to-understand analysis results if the user is feeling anxious. For example, if the analysis unit determines that the user is feeling anxious, the AI ​​will produce a concise and easy-to-understand analysis result. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input user facial expressions and voice data into a generating AI, which can then estimate emotions and adjust the method of expression for the analysis.

[0096] The assessment unit can estimate the user's emotions and adjust the assessment method based on the estimated emotions. For example, if the assessment unit determines that the user is excited, it can generate an AI that produces a visually stimulating assessment result. For example, if the assessment unit determines that the user is excited, it can generate an AI that produces a visually stimulating assessment result. The assessment unit can also generate an AI that produces a detailed and thorough assessment result if the user is relaxed. For example, if the assessment unit determines that the user is relaxed, it can generate an AI that produces a detailed and thorough assessment result. The assessment unit can also generate an AI that produces a concise and easy-to-understand assessment result if the user is feeling anxious. For example, if the assessment unit determines that the user is feeling anxious, it can generate an AI that produces a concise and easy-to-understand assessment result. In this way, by adjusting the assessment method according to the user's emotions, the system can provide the user with the most optimal assessment result. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the appraisal unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the appraisal unit can input the user's facial expressions and voice data into a generative AI, which can then estimate emotions and adjust the appraisal method.

[0097] The service provider can estimate the user's emotions and adjust the display method of the assessment results based on the estimated emotions. For example, if the service provider determines that the user is excited, it can provide a visually stimulating display method. For example, if the service provider determines that the user is excited, it can provide a visually stimulating display method. The service provider can also provide a detailed and polite display method if the user is relaxed. For example, if the service provider determines that the user is relaxed, it can provide a detailed and polite display method. The service provider can also provide a concise and easy-to-understand display method if the user is feeling anxious. For example, if the service provider determines that the user is feeling anxious, it can provide a concise and easy-to-understand display method. By adjusting the display method of the assessment results according to the user's emotions, it becomes possible to provide the optimal display for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using a generative AI or not using a generative AI. For example, the service provider can input the user's facial expressions and voice data into a generating AI, which can then estimate emotions and adjust the display method.

[0098] The service provider can estimate the user's emotions and adjust the operation procedure of the assessment results based on the estimated emotions of the user. For example, if the service provider determines that the user is nervous, it can provide a simple and intuitive operation procedure. For example, if the service provider determines that the user is nervous, it can provide a simple and intuitive operation procedure. The service provider can also provide a detailed operation procedure if the user is relaxed. For example, if the service provider determines that the user is relaxed, it can provide a detailed operation procedure. The service provider can also provide a procedure that allows for quick operation if the user is in a hurry. For example, if the service provider determines that the user is in a hurry, it can provide a procedure that allows for quick operation. In this way, by adjusting the operation procedure according to the user's emotions, the optimal operation for the user can be achieved. 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 service provider may be performed using generative AI or not using generative AI. For example, the service provider can input the user's facial expressions and voice data into a generating AI, which can then estimate emotions and adjust the operating procedures accordingly.

[0099] The reception desk can analyze a user's past upload history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods (images, videos, etc.) that the user has frequently used in the past. For instance, if the reception desk has frequently uploaded images in the past, it will prioritize suggesting image upload methods. The reception desk can also suggest the optimal upload method for a specific time of day based on the user's past upload history. For example, if the reception desk has uploaded during a specific time period in the past, it will suggest the optimal upload method for that time period. Furthermore, the reception desk can select the optimal upload method based on the types of products the user has uploaded in the past. For example, if the reception desk has uploaded products in a specific category in the past, it will select the optimal upload method for that category. In this way, by analyzing the user's past upload history, the reception desk can provide the optimal upload method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past upload history into AI, and the AI ​​can select the optimal upload method.

[0100] The reception desk can filter uploads based on the user's current interests and purchase history. For example, the reception desk may suggest prioritizing the upload of products from brands the user has previously purchased. For instance, if the user has previously purchased products from a specific brand, the reception desk may suggest prioritizing the upload of products from that brand. The reception desk can also prompt the user to upload images and videos of related products based on their current interests. For example, the reception desk may prompt the user to prioritize the upload of images and videos related to products they are currently interested in. The reception desk can also filter uploads based on the user's purchase history to prioritize products from specific categories. For example, if the user has previously purchased products from a specific category, the reception desk will filter uploads to prioritize products from that category. This allows for the priority uploading of highly relevant product information by filtering based on the user's interests and purchase history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's interests and purchase history into an AI, which can then perform the filtering.

[0101] The reception desk can prioritize accepting product images and videos that are highly relevant to the user's geographical location during the upload process. For example, if the reception desk determines that a user is in a specific region, it will prioritize uploading product images and videos that are popular in that region. The reception desk can also suggest uploading region-specific product images and videos based on the user's geographical location. For example, if the reception desk determines that a user is in a specific region, it will suggest uploading product images and videos that are specific to that region. Furthermore, if the reception desk determines that a user is traveling, it can prioritize uploading product images and videos that are highly relevant to their travel destination. For example, if the reception desk determines that a user is traveling, it will prioritize uploading product images and videos that are highly relevant to their travel destination. This improves user convenience by prioritizing the acceptance of product images and videos that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI, which can then select highly relevant product images and videos.

[0102] The reception desk can analyze the user's social media activity during upload and accept relevant product images and videos. For example, the reception desk can prioritize uploading product images and videos that the user has shared on social media. The reception desk can also suggest uploading product images and videos that are of high interest to the user based on their social media activity. The reception desk can also upload relevant product images and videos by referring to posts from brands and influencers that the user follows. This allows the reception desk to prioritize accepting relevant product images and videos by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI, which can then select relevant product images and videos.

[0103] The analysis unit can adjust the level of detail of the analysis based on the importance of the product. For example, for expensive products, the analysis unit's generating AI performs a detailed analysis to check every detail. For example, for expensive products, the analysis unit's generating AI performs a detailed analysis to check every detail. The analysis unit can also perform a standard analysis using the generating AI to check the main points for general products. For example, for general products, the analysis unit's generating AI performs a standard analysis to check the main points for general products. The analysis unit can also perform a simplified analysis using the generating AI to check the basic points for low-priced products. For example, for low-priced products, the analysis unit's generating AI performs a simplified analysis using the generating AI to check the basic points. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the product. Some or all of the above processes in the analysis unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the analysis unit can input the importance of the product into the generating AI, and the generating AI can adjust the level of detail of the analysis.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The reception desk accepts product images and videos uploaded by users. The reception desk can accept images and videos in formats such as JPEG, PNG, and MP4. The reception desk temporarily stores the images and videos uploaded by users and sends them to the analysis department. Step 2: The analysis unit uses a generation AI to analyze the images and videos uploaded by the reception unit. The analysis unit extracts product features using image analysis techniques such as deep learning and edge detection. The analysis unit compares the uploaded images and videos with past product data to evaluate their authenticity. For example, the analysis unit compares the uploaded images and videos with past product data using database searches and similarity calculations. Step 3: The appraisal unit determines the authenticity of the item based on the results analyzed by the analysis unit. The appraisal unit determines the authenticity of the item using, for example, a reliability score or a judgment algorithm. The appraisal unit provides appraisal results that include probabilities and detailed explanations regarding the authenticity of the item. Step 4: The service provider provides the user with the appraisal results obtained by the appraisal service provider. The service provider provides the user with the appraisal results, for example, using notification methods and display formats. The service provider ensures that the user can purchase the product with confidence.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the reception unit, analysis unit, appraisal unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts the uploading of product images and videos by the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the images and videos using generating AI. The appraisal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines the authenticity of the product based on the analysis results. The provision unit is implemented by, for example, the output device 40 of the smart device 14 and provides the appraisal results to the user. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the reception unit, analysis unit, appraisal unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and accepts the upload of product images and videos by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the images and videos using generating AI. The appraisal unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the authenticity of the product based on the analysis results. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the appraisal results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the reception unit, analysis unit, appraisal unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and accepts the uploading of product images and videos by the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the images and videos using generation AI. The appraisal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines the authenticity of the product based on the analysis results. The provision unit is implemented by, for example, the display 343 of the headset terminal 314 and provides the appraisal results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In 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.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 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.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, appraisal unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and accepts the upload of product images and videos by the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the images and videos using generating AI. The appraisal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines the authenticity of the product based on the analysis results. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the appraisal results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) A reception area for uploading product images and videos, An analysis unit analyzes images and videos uploaded by the aforementioned reception unit, An authentication unit that determines authenticity based on the results of the analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the user with the appraisal results obtained by the appraisal unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Perform image analysis and compare with past product data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned appraisal department, We provide appraisal results that include probabilities and detailed explanations regarding the authenticity of the product. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, To enable users to purchase products with peace of mind. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is We accept user uploads of product images and videos. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of image and video uploads based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past upload history and select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is During the upload process, filtering is performed based on the user's current interests and purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes the images and videos to upload based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is During upload, the system prioritizes accepting product images and videos that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During the upload process, the system analyzes the user's social media activity and accepts relevant product images and videos. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) 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 14) 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 15) 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 16) 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 17) 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 18) The aforementioned appraisal department, The system estimates the user's emotions and adjusts the assessment method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned appraisal department, During the appraisal process, the system analyzes the user's past appraisal results to select the most suitable appraisal method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned appraisal department, During the appraisal process, the appraisal method is customized based on the user's current interests. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the assessment results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing appraisal 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 23) The aforementioned supply unit is, When providing appraisal results, the displayed content will be customized based on the user's current interests and purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the operation procedure of the assessment results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing appraisal results, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing appraisal results, the system will offer multilingual support according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing appraisal results, the system will refer to the user's calendar information to display the results based on their schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0178] 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 reception area for uploading product images and videos, An analysis unit analyzes images and videos uploaded by the aforementioned reception unit, An authentication unit that determines authenticity based on the results of the analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the user with the appraisal results obtained by the appraisal unit. A system characterized by the following features.

2. The aforementioned analysis unit, Perform image analysis and compare with past product data. The system according to feature 1.

3. The aforementioned appraisal department, We provide appraisal results that include probabilities and detailed explanations regarding the authenticity of the product. The system according to feature 1.

4. The aforementioned supply unit is, To enable users to purchase products with peace of mind. The system according to feature 1.

5. The aforementioned reception unit is We accept user uploads of product images and videos. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of image and video uploads based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past upload history and select the optimal upload method. The system according to feature 1.

8. The aforementioned reception unit is During the upload process, filtering is performed based on the user's current interests and purchase history. The system according to feature 1.

Images