system

Abstract

The system according to this embodiment aims to simplify the process and improve reliability when users purchase goods from overseas. [Solution] The system according to the embodiment comprises a reception unit, a search unit, a supply unit, a purchase unit, and a determination unit. The reception unit receives input from the user about the product they want. The search unit searches for the product from online shopping apps around the world based on the information entered by the reception unit. The supply unit presents the price of the product found by the search unit, including customs duties and shipping costs. The purchase unit allows the user to decide whether to purchase the product based on the price presented by the supply unit. The determination unit determines whether the product found by the search unit is genuine or counterfeit.

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 are problems that the calculation of customs duties and transportation costs is complicated in purchasing goods from overseas, and it is difficult to determine the authenticity and imitation of goods.

[0005] The system according to the embodiment aims to simplify the procedures when a user purchases goods from overseas and improve reliability.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a search unit, a supply unit, a purchase unit, and a determination unit. The reception unit receives input from the user regarding the desired product. The search unit searches for the product from online shopping apps around the world based on the information entered by the reception unit. The supply unit displays the price of the product found by the search unit, including customs duties and shipping costs. The purchase unit allows the user to decide whether to purchase the product based on the price displayed by the supply unit. The determination unit determines whether the product found by the search unit is genuine or counterfeit. [Effects of the Invention]

[0007] The system according to this embodiment can simplify the process and improve reliability when users purchase goods from overseas. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that supports product search and purchase by searching for desired products from online shopping apps worldwide and displaying prices including customs duties and shipping costs. This AI agent system reduces the gross profit of intermediaries by purchasing imported goods directly from overseas shopping sites, instead of through trading companies and other businesses, making direct purchases cheaper. Furthermore, by performing authenticity and counterfeit detection using AI, it lowers the distrust and barriers to overseas shopping, realizing easy international shopping. For example, a user inputs the product they want. Next, the AI ​​agent searches for that product from online shopping apps around the world. The search results display prices including customs duties and shipping costs. The user can check the displayed price and decide to purchase. In addition, the AI ​​agent performs authenticity and counterfeit detection on the products included in the search results. This allows the user to purchase products with peace of mind. For example, the AI ​​agent analyzes the product images and descriptions to determine authenticity. It also displays a warning to the user for products that may be counterfeit. This system allows users to easily search for desired products from online shopping apps worldwide, check prices including customs duties and shipping costs, and purchase with confidence. This lowers the barrier to international shopping, making it more accessible. The AI ​​agent system can then search for products from online shopping apps around the world, display prices including customs duties and shipping costs, and support the user's purchase.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, a search unit, a supply unit, a purchase unit, and a decision unit. The reception unit receives input from the user for the desired product. Specific types and formats of products entered by the user include, but are not limited to, electronic devices, clothing, and books. The reception unit, for example, stores the product information entered by the user in a database. The reception unit can also transmit the product information entered by the user to the search unit. The search unit searches for products from online shopping apps around the world based on the information entered by the reception unit. The search unit can, for example, send search queries to multiple online shopping apps simultaneously and obtain search results. The search unit can also filter the search results to select products that meet the user's needs. The supply unit presents the price of the product found by the search unit, including customs duties and shipping costs. The supply unit calculates the price based on information such as customs duty rates, shipping distance, and weight. The supply unit can also provide an interface for presenting the calculated price to the user. The purchase unit allows the user to decide on a purchase based on the price presented by the supply unit. The purchase unit initiates the purchase process, for example, when the user clicks a purchase button. The purchase unit can also notify the user of the progress of the purchase process. The judgment unit determines the authenticity and counterfeit status of products found by the search unit. The judgment unit analyzes product images and descriptions to determine authenticity, for example. The judgment unit can also display a warning to the user about products that may be counterfeit. As a result, the AI ​​agent system according to this embodiment can support users in searching for products from online shopping apps around the world, displaying prices including customs duties and shipping costs, and assisting them in making purchases.

[0030] The reception desk receives input from the user about the product they want. The specific types and formats of products entered by the user include, but are not limited to, electronic devices, clothing, and books. The reception desk, for example, stores the user's entered product information in a database. When entering information, users can provide detailed information such as product name, brand, price range, and desired functions and features. This allows for more accurate searches of products that meet the user's needs. The reception desk can also send the user's entered product information to the search department. For example, if a user enters "latest smartphone," the reception desk saves that information in the database and sends it to the search department. Furthermore, the reception desk can learn the user's preferences based on their past search and purchase history, and use this information for future searches and suggestions. This saves the user the trouble of searching for the same product again, providing a smoother shopping experience. The reception desk also supports voice and image input, allowing users to search for products more intuitively by specifying products by voice or uploading product images. For example, if a user uploads an image taken with their smartphone camera, the reception desk analyzes the image and uses it as information to search for similar products. This allows users to search for products based on visual information rather than relying on text input.

[0031] The search unit searches for products from online shopping apps around the world based on information entered by the reception unit. For example, the search unit can simultaneously send search queries to multiple online shopping apps and retrieve search results. The search unit can obtain product information in real time using the APIs of each online shopping app. For example, if a user searches for "latest smartphones," the search unit simultaneously sends queries to major online shopping apps and collects search results from each app. The search unit can also filter search results to select products that meet the user's needs. Filtering criteria include price, ratings, seller reliability, and shipping options. For example, if a user specifies sorting by "lowest price," the search unit sorts the retrieved search results by price and displays the cheapest items first. Furthermore, the search unit can use AI to learn user preferences and past search history to provide more personalized search results. For example, if a user has frequently searched for products of a particular brand in the past, the search unit will prioritize displaying products from that brand. The search unit also provides an interface for users to refine search results, allowing them to easily change their search criteria. This allows the search function to quickly and accurately find the products users are looking for, improving the shopping experience.

[0032] The service provider displays the price of the product found by the search function, including customs duties and shipping costs. The service provider calculates the price based on information such as customs duty rates, shipping distance, and weight. Specifically, it accesses customs duty databases for various countries and calculates customs duties according to the product category and country of origin. For shipping costs, it refers to the shipping carrier's price list and calculates the accurate shipping cost based on the product's weight, size, and destination distance. For example, if a user purchases electronic equipment from the United States to Japan, the service provider calculates the US customs duty rate and shipping costs to Japan, and displays the total price. Furthermore, the service provider can provide an interface to display the calculated price to the user. The user can review the price displayed by the service provider and proceed with the purchase only after they are satisfied. The service provider also displays a detailed breakdown of the price, allowing users to understand how customs duties and shipping costs are calculated. This ensures users receive transparent pricing information and can confidently make purchase decisions. Additionally, the service provider offers multiple shipping options, allowing users to choose the most suitable method. For example, it may offer options such as standard shipping, express shipping, and economy shipping, allowing users to compare the costs and delivery times of each. This allows the service provider to offer users flexible options and deliver a highly satisfying shopping experience.

[0033] The purchasing department allows users to make purchase decisions based on prices presented by the offering department. The purchasing department initiates the purchase process, for example, by the user clicking a purchase button. This process includes the user entering payment and shipping information. The purchasing department securely manages the information entered by the user and provides an interface to facilitate the payment process. For example, when a user enters credit card information, SSL encryption is used to protect the information and prevent unauthorized access by third parties. The purchasing department can also notify the user of the progress of the purchase process. For example, it can send a confirmation email when the order is finalized and a shipping notification when the product is ready for shipment. Furthermore, the purchasing department provides an interface that allows users to check their order history, making it easy to refer to past purchase information. This allows users to track the progress of their purchase in real time and wait for their products with peace of mind. The purchasing department also includes features to support return and exchange procedures, ensuring a smooth process even if the user is not satisfied with the product. For example, if a user wishes to return an item, the purchasing department provides return guidelines and guides them through the necessary documents and procedures. This allows the purchasing department to provide users with a safe and convenient purchasing experience and realize a highly reliable shopping system.

[0034] The judgment unit determines the authenticity and counterfeit status of products found by the search unit. For example, the judgment unit analyzes product images and descriptions to determine authenticity. Specifically, it uses AI to analyze product images and compare the characteristics of genuine and counterfeit products. For example, it checks the position and shape of brand logos and the design details of the product, and displays a warning if there is a possibility that it is a counterfeit. It also analyzes product descriptions using natural language processing technology to determine whether they match genuine products. For example, it checks whether the product specifications and manufacturer information are accurate, and displays a warning to the user if there is a possibility that it is a counterfeit. Furthermore, the judgment unit refers to past databases to check whether similar products have been reported as counterfeit in the past. This allows the judgment unit to support users in purchasing products with confidence. In addition, the judgment unit can collect feedback from users and continuously improve the accuracy of its judgment algorithm. For example, if a product purchased by a user is found to be counterfeit, that information is registered in the database and used for future judgments. This allows the judgment unit to always provide highly accurate authenticity judgments based on the latest information and gain the trust of users. Furthermore, the detection unit also has a function to suggest alternative products to the user if there is a high probability that the product is a counterfeit, thus supporting users in enjoying shopping with peace of mind.

[0035] The judgment unit can determine the authenticity of a product by analyzing its images and descriptions. For example, the judgment unit can analyze the product images using image recognition technology to determine authenticity. For example, the judgment unit can analyze the product's logo and design features to determine authenticity. The judgment unit can also analyze the product's descriptions using natural language processing technology to determine authenticity. For example, the judgment unit can analyze keywords and phrases contained in the product descriptions to determine authenticity. Furthermore, the judgment unit can improve the accuracy of its authenticity determination by referring to product reviews and ratings. For example, the judgment unit can analyze information contained in product reviews to determine authenticity. This improves the accuracy of authenticity determination by analyzing the product images and descriptions. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input product image data and description data into a generating AI and have the generating AI perform the authenticity determination.

[0036] The detection unit can display a warning to the user about products that may be counterfeit. For example, the detection unit can identify products that may be counterfeit by analyzing product images and descriptions. For example, the detection unit can identify products that may be counterfeit by analyzing the logo and design features of the product. The detection unit can also identify products that may be counterfeit by analyzing keywords and phrases contained in the product description. Furthermore, the detection unit can identify products that may be counterfeit by referring to product reviews and ratings. For example, the detection unit can identify products that may be counterfeit by analyzing the information contained in product reviews. This allows users to purchase products with confidence by displaying a warning about products that may be counterfeit. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input product image data and description data into a generating AI and have the generating AI perform the process of determining the possibility of counterfeiting.

[0037] The service provider can provide a price that includes customs duties and shipping costs. The service provider calculates the price based on information such as customs duty rates, shipping distance, and weight. For example, the service provider can obtain the customs duty rate for a product from a database and calculate shipping costs based on shipping distance and weight. The service provider can also provide an interface for presenting the calculated price to the user. For example, the service provider can provide an interface that displays price information so that the user can check the price. Furthermore, the service provider can update price information in real time and present the user with the latest price. For example, the service provider updates price information in accordance with changes in customs duty rates and shipping costs and presents it to the user. This makes it easier for the user to understand the total price by presenting a price that includes customs duties and shipping costs. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generating AI perform the calculation of the customs duty rate and shipping costs for a product.

[0038] The purchasing unit allows the user to decide on a purchase based on the price presented. The purchasing unit can initiate the purchase process, for example, by having the user click a purchase button. For example, the purchasing unit can allow the user to confirm the presented price and proceed with the purchase process by clicking a purchase button. The purchasing unit can also notify the user of the progress of the purchase process. For example, the purchasing unit can notify the user of each step of the purchase process so that they can check the progress. Furthermore, the purchasing unit can send a confirmation email to the user after the purchase process is completed. For example, the purchasing unit can send a confirmation email to the user to inform them that the purchase process is complete. This makes the purchase process smoother by allowing the user to decide on a purchase based on the presented price. Some or all of the above processes in the purchasing unit may be performed using AI, for example, or not using AI. For example, the purchasing unit can have a generating AI execute the user's purchase process.

[0039] The reception desk can analyze the user's past purchase history and suggest the most suitable products to input. For example, the reception desk can automatically display products that the user has frequently purchased in the past as candidates. For example, the reception desk can retrieve the user's past purchase history from a database and display frequently purchased products as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can analyze the input methods the user has used in the past and suggest the most suitable input method. Furthermore, the reception desk can predict and suggest products that the user will purchase at a specific time based on their past purchase history. For example, the reception desk analyzes the user's past purchase history and predicts and suggests products that will be purchased at a specific time. This makes it possible to suggest the most suitable products by analyzing the user's past purchase history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past purchase history into a generating AI and have the generating AI execute suggestions for the most suitable products to input.

[0040] The input system can suggest input options based on the user's current interests and trends when the user enters a product. For example, the input system can display relevant products as suggestions based on products or categories the user has recently searched for. For example, the input system can analyze the user's recent search history and display relevant products as suggestions. The input system can also analyze the user's social media activity and suggest products of interest. For example, the input system can analyze the user's social media posts and suggest products of interest. Furthermore, the input system can suggest products recommended to the user based on current trends and popular products. For example, the input system can retrieve current trends and popular products from a database and suggest products recommended to the user. This improves user convenience by suggesting input options based on the user's current interests and trends. Some or all of the above processing in the input system may be performed using AI, for example, or not. For example, the input system can input the user's interests and trends into a generating AI and have the generating AI perform the task of suggesting input options.

[0041] The reception desk can prioritize inputting highly relevant products based on the user's geographical location information when inputting products. For example, the reception desk can prioritize displaying products that can be purchased from nearby stores based on the user's current location. For example, the reception desk can obtain the user's current location and prioritize displaying products that can be purchased from nearby stores. The reception desk can also prioritize displaying products that are popular in the user's region. For example, the reception desk can obtain popular products in the user's region from a database and prioritize displaying them. Furthermore, the reception desk can prioritize displaying products that can be delivered quickly based on the user's geographical location information. For example, the reception desk can obtain the user's geographical location information and prioritize displaying products that can be delivered quickly. This improves user convenience by prioritizing the input of highly relevant products based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize the input of highly relevant products.

[0042] The reception desk can analyze the user's social media activity when inputting products and input related products. For example, the reception desk can display products that the user has "liked" or commented on on social media as suggestions. The reception desk can also prioritize displaying products introduced by influencers that the user follows. For example, the reception desk can analyze posts from influencers that the user follows and prioritize displaying products they have introduced. Furthermore, the reception desk can analyze the content of the user's social media posts and suggest products of interest. For example, the reception desk can analyze the content of the user's social media posts and suggest products of interest. In this way, by analyzing the user's social media activity, related products can be input. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity into a generating AI and have the generating AI input related products.

[0043] The search function can adjust the priority of search results based on product popularity and ratings during a search. For example, the search function may prioritize displaying highly-rated products. For example, it may prioritize displaying highly-rated products based on their rating score and number of reviews. The search function can also prioritize displaying highly popular products. For example, it may prioritize displaying highly popular products based on their sales figures and popularity. Furthermore, the search function may prioritize displaying highly relevant products based on the user's past rating history. For example, it may analyze the user's past rating history and prioritize displaying highly relevant products. This improves user convenience by adjusting the priority of search results based on product popularity and ratings. Some or all of the above processing in the search function may be performed using AI, for example, or without AI. For example, the search function can input product popularity and ratings into a generating AI and have the generating AI adjust the priority of search results.

[0044] The search unit can apply different search algorithms depending on the product category during a search. For example, in the electronics category, the search unit displays search results based on specifications and functions. For example, the search unit displays search results based on the specifications and functions of electronic devices. The search unit can also display search results based on trends and styles in the fashion category. For example, the search unit displays search results based on fashion trends and styles. Furthermore, in the food category, the search unit can display search results based on expiration dates and origins. For example, the search unit displays search results based on the expiration dates and origins of food products. By applying different search algorithms depending on the product category, the accuracy of the search results is improved. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input product category information into a generating AI and have the generating AI execute the application of the search algorithm.

[0045] The search unit can display search results while considering the geographical distribution of products. For example, the search unit can prioritize displaying products available for purchase from stores close to the user's current location. For example, the search unit can obtain the user's current location and prioritize displaying products available for purchase from nearby stores. The search unit can also prioritize displaying products popular in the user's region. For example, the search unit can obtain popular products in the user's region from a database and prioritize displaying them. Furthermore, the search unit can prioritize displaying products that can be delivered quickly based on the user's geographical location information. For example, the search unit can obtain the user's geographical location information and prioritize displaying products that can be delivered quickly. This improves user convenience by displaying search results while considering the geographical distribution of products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the geographical distribution of products into a generating AI and have the generating AI execute the display of search results.

[0046] The search unit can improve the accuracy of search results by referring to related literature on products during a search. For example, the search unit can display highly relevant products based on product reviews and ratings. For example, the search unit can retrieve product reviews and ratings from a database and display highly relevant products. The search unit can also refer to product technical literature and display search results based on specifications and functions. For example, the search unit can refer to product technical literature and display search results based on specifications and functions. Furthermore, the search unit can display highly relevant products based on product usage examples and case studies. For example, the search unit can refer to product usage examples and case studies and display highly relevant products. This improves the accuracy of search results by referring to related literature on products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input product related literature into a generating AI and have the generating AI perform the task of improving the accuracy of search results.

[0047] The service provider can adjust the level of detail in pricing based on the importance of the product when presenting prices. For example, the service provider may present detailed pricing information for expensive products. The service provider may also present concise pricing information for low-priced products. Furthermore, the service provider may present detailed pricing information for products of particular interest to the user. This improves user convenience by adjusting the level of detail in pricing based on the importance of the product. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the product into a generating AI and have the generating AI perform the adjustment of the level of detail in pricing.

[0048] The pricing unit can apply different pricing algorithms depending on the product category when providing prices. For example, in the electronics category, the pricing unit can provide prices based on specifications and functions. For example, in the fashion category, the pricing unit can provide prices based on brands and designs. For example, in the food category, the pricing unit can provide prices based on origin and expiration dates. For example, in the food category, the pricing unit can provide prices based on origin and expiration dates. This improves the accuracy of price quotes by applying different pricing algorithms depending on the product category. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input product category information into a generating AI and have the generating AI apply the pricing algorithm.

[0049] The service provider can determine price priority based on the product's submission date when displaying prices. For example, the service provider may prioritize displaying new or limited-edition products. For example, the service provider may prioritize displaying price information for new or limited-edition products. The service provider can also prioritize displaying products during sales periods. For example, the service provider may retrieve products during sales periods from its database and prioritize displaying them. Furthermore, the service provider may prioritize displaying seasonal or in-season products. For example, the service provider may retrieve seasonal or in-season products from its database and prioritize displaying them. This improves user convenience by determining price priority based on the product's submission date. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the product's submission date into a generating AI and have the generating AI determine the price priority.

[0050] The service provider can adjust the order of prices based on the relevance of the products when presenting prices. For example, the service provider can prioritize displaying products that are highly relevant to products that the user has previously purchased. For example, the service provider can analyze the user's past purchase history and prioritize displaying highly relevant products. The service provider can also prioritize displaying highly relevant products based on the user's current interests and trends. For example, the service provider can analyze the user's current interests and trends and prioritize displaying highly relevant products. Furthermore, the service provider can analyze the user's social media activity and prioritize displaying highly relevant products. For example, the service provider can analyze the user's social media activity and prioritize displaying highly relevant products. This improves user convenience by adjusting the order of prices based on the relevance of the products. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of products into a generating AI and have the generating AI perform the adjustment of the price order.

[0051] The purchasing department can analyze the user's past purchase history to select the optimal purchase method at the time of purchase. For example, the purchasing department can prioritize suggesting purchase methods the user has used in the past (credit card, electronic money, etc.). For example, the purchasing department can analyze the user's past purchase history and prioritize suggesting purchase methods used in the past. The purchasing department can also predict and suggest purchase methods to be used at specific times based on the user's past purchase history. For example, the purchasing department can analyze the user's past purchase history and predict and suggest purchase methods to be used at specific times. Furthermore, the purchasing department can analyze the user's past purchase history and suggest the most efficient purchase method. For example, the purchasing department can analyze the user's past purchase history and suggest the most efficient purchase method. In this way, the optimal purchase method can be selected by analyzing the user's past purchase history. Some or all of the above processing in the purchasing department may be performed using AI, for example, or without AI. For example, the purchasing department can input the user's past purchase history into a generating AI and have the generating AI select the optimal purchase method.

[0052] The purchasing department can customize the purchase method based on the user's current lifestyle at the time of purchase. For example, if the user is busy, the purchasing department can suggest a quick purchase procedure. For example, the purchasing department can analyze that the user is busy and suggest a quick purchase procedure. The purchasing department can also suggest a detailed purchase procedure if the user is relaxed. For example, the purchasing department can analyze that the user is relaxed and suggest a detailed purchase procedure. Furthermore, if the user is traveling, the purchasing department can suggest the option to change the delivery address. For example, the purchasing department can analyze that the user is traveling and suggest the option to change the delivery address. This improves user convenience by customizing the purchase method based on the user's current lifestyle. Some or all of the above processing in the purchasing department may be performed using AI, for example, or not using AI. For example, the purchasing department can input the user's lifestyle into a generating AI and have the generating AI perform the customization of the purchase method.

[0053] The purchasing function can select the optimal purchasing method at the time of purchase, taking into account the user's geographical location. For example, the purchasing function can prioritize displaying products available from nearby stores based on the user's current location. For example, the purchasing function can obtain the user's current location and prioritize displaying products available from nearby stores. The purchasing function can also prioritize displaying products popular in the user's region. For example, the purchasing function can obtain popular products in the user's region from a database and prioritize displaying them. Furthermore, the purchasing function can prioritize displaying products that can be delivered quickly, based on the user's geographical location. For example, the purchasing function can obtain the user's geographical location and prioritize displaying products that can be delivered quickly. This improves user convenience by selecting the optimal purchasing method considering the user's geographical location. Some or all of the above processing in the purchasing function may be performed using AI, for example, or without AI. For example, the purchasing function can input the user's geographical location information into a generating AI and have the generating AI select the optimal purchasing method.

[0054] The purchasing function can analyze a user's social media activity and suggest purchasing options at the time of purchase. For example, the purchasing function can display products that the user has "liked" or commented on on social media as potential purchase options. The purchasing function can also prioritize displaying products introduced by influencers that the user follows. For example, the purchasing function can analyze posts from influencers that the user follows and prioritize displaying products they have introduced. Furthermore, the purchasing function can analyze the content of the user's social media posts and suggest products of interest. For example, the purchasing function can analyze the content of the user's social media posts and suggest products of interest. In this way, by analyzing the user's social media activity, the optimal purchasing method can be suggested. Some or all of the above processing in the purchasing function may be performed using AI, for example, or without AI. For example, the purchasing function can input the user's social media activity into a generating AI and have the generating AI execute the suggestion of purchasing methods.

[0055] The judgment unit can improve the accuracy of its judgment based on the level of detail in the product images and descriptions when determining authenticity. For example, the judgment unit can use high-resolution images to check the details of the product. For example, the judgment unit can analyze high-resolution images to check the details of the product. The judgment unit can also analyze detailed descriptions to determine the authenticity of the product. For example, the judgment unit can analyze detailed descriptions to determine the authenticity of the product. Furthermore, the judgment unit can refer to product reviews and ratings to improve the accuracy of its authenticity determination. For example, the judgment unit can refer to product reviews and ratings to improve the accuracy of its authenticity determination. This improves user convenience by improving the accuracy of the judgment based on the level of detail in the product images and descriptions. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the level of detail in the product images and descriptions into a generating AI and have the generating AI perform the improvement of the judgment accuracy.

[0056] The judgment unit can apply different judgment algorithms depending on the product category when determining authenticity. For example, in the electronics category, the judgment unit can determine authenticity based on specifications and functions. For example, the judgment unit can determine authenticity based on the specifications and functions of electronic devices. In the fashion category, the judgment unit can also determine authenticity based on brand and design. For example, the judgment unit can determine authenticity based on the brand and design of fashion items. Furthermore, in the food category, the judgment unit can also determine authenticity based on the place of origin and expiration date. For example, the judgment unit can determine authenticity based on the place of origin and expiration date of food items. By applying different judgment algorithms depending on the product category, the accuracy of the judgment is improved. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input product category information into a generating AI and have the generating AI execute the application of the judgment algorithm.

[0057] The judgment unit can perform authenticity determination by considering the geographical distribution of the goods. For example, the judgment unit can determine authenticity based on the region information of the goods' origin. For example, the judgment unit can obtain the region information of the goods' origin and determine authenticity. The judgment unit can also determine authenticity based on the region information of the goods' delivery destination. For example, the judgment unit can obtain the region information of the goods' delivery destination and determine authenticity. Furthermore, the judgment unit can analyze the goods' distribution route and determine authenticity. For example, the judgment unit can analyze the goods' distribution route and determine authenticity. By considering the geographical distribution of the goods, the accuracy of the determination is improved. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the geographical distribution of the goods into a generating AI and have the generating AI perform the determination.

[0058] The judgment unit can improve the accuracy of its judgment by referring to relevant literature on the product when determining authenticity. For example, the judgment unit can refer to the product's technical literature and determine authenticity based on its specifications and functions. The judgment unit can also determine authenticity based on product usage examples and case studies. For example, the judgment unit can refer to product usage examples and case studies to determine authenticity. Furthermore, the judgment unit can improve the accuracy of its authenticity judgment by referring to product reviews and ratings. For example, the judgment unit can refer to product reviews and ratings to improve the accuracy of its authenticity judgment. In this way, the accuracy of the judgment is improved by referring to relevant literature on the product. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input relevant literature on the product into a generating AI and have the generating AI perform the improvement of the judgment accuracy.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The input system can be equipped with a function to automatically complete detailed product information entered by the user. For example, if the user only enters the product name, the input system can automatically complete and present related product details (e.g., manufacturer name, model number, specifications, etc.) to the user. The input system can also display suggested related products based on the information entered by the user. For example, if the user enters "smartphone," the input system can display the latest smartphone models or popular models as suggestions. Furthermore, the input system can display product reviews and ratings based on the information entered by the user. For example, if the user enters the model number of a specific product, the system can automatically display reviews and ratings for that product for the user to refer to. This improves user convenience by automatically completing detailed product information based on the information entered by the user.

[0061] The authentication unit can refer to the user's past purchase history when determining the authenticity of a product. For example, it can perform authenticity checks on similar products based on the authenticity check results of products the user has purchased in the past. The authentication unit can also refer to reviews and ratings of products the user has purchased in the past to improve the accuracy of authenticity checks. For example, it can perform authenticity checks on similar products based on reviews of products the user has purchased in the past. Furthermore, the authentication unit can analyze trends in authenticity checks for products of specific brands or manufacturers from the user's past purchase history to improve the accuracy of authenticity checks. For example, if all products of a particular brand that the user has purchased in the past have been genuine, it can quickly perform authenticity checks on products of that brand. In this way, by referring to the user's past purchase history, the accuracy of authenticity checks is improved, and user convenience is enhanced.

[0062] The detection unit can suggest alternative products to users for items that may be counterfeit. For example, if the detection unit identifies an item that may be counterfeit, it can suggest a genuine product in the same category to the user. The detection unit can also display a warning to the user about items that may be counterfeit and recommend purchasing from a reliable vendor. For example, if the detection unit identifies an item that may be counterfeit, it can show the user a reliable vendor that handles that item. Furthermore, the detection unit can provide users with detailed information about items that may be counterfeit and explain the risks of counterfeit goods. For example, the detection unit can provide users with information about the characteristics and risks of items that may be counterfeit, allowing them to make informed purchasing decisions. This improves user convenience by suggesting alternative products for items that may be counterfeit.

[0063] The service provider can offer a filtering function based on the user's budget when presenting prices that include customs duties and shipping costs. For example, the service provider can display only products that are within the user's set budget. Furthermore, the service provider can suggest adjustments to customs duties and shipping costs to bring products exceeding the user's budget within that budget. For example, if a user selects a product that exceeds their budget, the service provider can suggest alternative shipping methods to reduce customs duties and shipping costs. Additionally, the service provider can offer installment payment options for products exceeding the user's budget. For example, if a user selects a product that exceeds their budget, the service provider can offer installment payment options to support the purchase. This improves user convenience by providing a filtering function based on the user's budget when presenting prices that include customs duties and shipping costs.

[0064] The purchasing department can provide users with information about the timing of their purchase when they are deciding to buy something. For example, the purchasing department can notify users that a particular product is on sale and suggest the best time to buy it. The purchasing department can also provide users with information about the likelihood of a particular product going out of stock and encourage them to buy it sooner. For example, the purchasing department can notify users that a particular product is likely to go out of stock and suggest the best time to buy it. Furthermore, the purchasing department can provide users with information about the timing of their purchase when they are deciding to buy something, thereby improving user convenience.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives the product the user wants. The specific type and format of the product the user enters may include, but is not limited to, electronic devices, clothing, books, etc. The reception desk can also save the product information entered by the user to a database and send it to the search unit. Step 2: The search unit searches for products from online shopping apps around the world based on the information entered by the reception unit. The search unit simultaneously sends search queries to multiple online shopping apps and retrieves search results. The search unit can also filter the search results to select products that meet the user's needs. Step 3: The service provider displays the price of the product found by the search service provider, including customs duties and shipping costs. The service provider can also calculate the price based on information such as customs duty rates, shipping distance, and weight, and provide an interface to display the calculated price to the user. Step 4: The purchasing department allows the user to decide on a purchase based on the price presented by the offering department. The purchasing department can initiate the purchase process when the user clicks the purchase button and can also notify the user of the progress of the purchase process. Step 5: The judgment unit determines the authenticity and counterfeit status of the products found by the search unit. The judgment unit analyzes the product images and descriptions to determine authenticity. It can also display a warning to the user for products that may be counterfeit.

[0067] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that supports product search and purchase by searching for desired products from online shopping apps worldwide and displaying prices including customs duties and shipping costs. This AI agent system reduces the gross profit of intermediaries by purchasing imported goods directly from overseas shopping sites, instead of through trading companies and other businesses, making direct purchases cheaper. Furthermore, by performing authenticity and counterfeit detection using AI, it lowers the distrust and barriers to overseas shopping, realizing easy international shopping. For example, a user inputs the product they want. Next, the AI ​​agent searches for that product from online shopping apps around the world. The search results display prices including customs duties and shipping costs. The user can check the displayed price and decide to purchase. In addition, the AI ​​agent performs authenticity and counterfeit detection on the products included in the search results. This allows the user to purchase products with peace of mind. For example, the AI ​​agent analyzes the product images and descriptions to determine authenticity. It also displays a warning to the user for products that may be counterfeit. This system allows users to easily search for desired products from online shopping apps worldwide, check prices including customs duties and shipping costs, and purchase with confidence. This lowers the barrier to international shopping, making it more accessible. The AI ​​agent system can then search for products from online shopping apps around the world, display prices including customs duties and shipping costs, and support the user's purchase.

[0068] The AI ​​agent system according to this embodiment comprises a reception unit, a search unit, a supply unit, a purchase unit, and a decision unit. The reception unit receives input from the user for the desired product. Specific types and formats of products entered by the user include, but are not limited to, electronic devices, clothing, and books. The reception unit, for example, stores the product information entered by the user in a database. The reception unit can also transmit the product information entered by the user to the search unit. The search unit searches for products from online shopping apps around the world based on the information entered by the reception unit. The search unit can, for example, send search queries to multiple online shopping apps simultaneously and obtain search results. The search unit can also filter the search results to select products that meet the user's needs. The supply unit presents the price of the product found by the search unit, including customs duties and shipping costs. The supply unit calculates the price based on information such as customs duty rates, shipping distance, and weight. The supply unit can also provide an interface for presenting the calculated price to the user. The purchase unit allows the user to decide on a purchase based on the price presented by the supply unit. The purchase unit initiates the purchase process, for example, when the user clicks a purchase button. The purchase unit can also notify the user of the progress of the purchase process. The judgment unit determines the authenticity and counterfeit status of products found by the search unit. The judgment unit analyzes product images and descriptions to determine authenticity, for example. The judgment unit can also display a warning to the user about products that may be counterfeit. As a result, the AI ​​agent system according to this embodiment can support users in searching for products from online shopping apps around the world, displaying prices including customs duties and shipping costs, and assisting them in making purchases.

[0069] The reception desk receives input from the user about the product they want. The specific types and formats of products entered by the user include, but are not limited to, electronic devices, clothing, and books. The reception desk, for example, stores the user's entered product information in a database. When entering information, users can provide detailed information such as product name, brand, price range, and desired functions and features. This allows for more accurate searches of products that meet the user's needs. The reception desk can also send the user's entered product information to the search department. For example, if a user enters "latest smartphone," the reception desk saves that information in the database and sends it to the search department. Furthermore, the reception desk can learn the user's preferences based on their past search and purchase history, and use this information for future searches and suggestions. This saves the user the trouble of searching for the same product again, providing a smoother shopping experience. The reception desk also supports voice and image input, allowing users to search for products more intuitively by specifying products by voice or uploading product images. For example, if a user uploads an image taken with their smartphone camera, the reception desk analyzes the image and uses it as information to search for similar products. This allows users to search for products based on visual information rather than relying on text input.

[0070] The search unit searches for products from online shopping apps around the world based on information entered by the reception unit. For example, the search unit can simultaneously send search queries to multiple online shopping apps and retrieve search results. The search unit can obtain product information in real time using the APIs of each online shopping app. For example, if a user searches for "latest smartphones," the search unit simultaneously sends queries to major online shopping apps and collects search results from each app. The search unit can also filter search results to select products that meet the user's needs. Filtering criteria include price, ratings, seller reliability, and shipping options. For example, if a user specifies sorting by "lowest price," the search unit sorts the retrieved search results by price and displays the cheapest items first. Furthermore, the search unit can use AI to learn user preferences and past search history to provide more personalized search results. For example, if a user has frequently searched for products of a particular brand in the past, the search unit will prioritize displaying products from that brand. The search unit also provides an interface for users to refine search results, allowing them to easily change their search criteria. This allows the search function to quickly and accurately find the products users are looking for, improving the shopping experience.

[0071] The service provider displays the price of the product found by the search function, including customs duties and shipping costs. The service provider calculates the price based on information such as customs duty rates, shipping distance, and weight. Specifically, it accesses customs duty databases for various countries and calculates customs duties according to the product category and country of origin. For shipping costs, it refers to the shipping carrier's price list and calculates the accurate shipping cost based on the product's weight, size, and destination distance. For example, if a user purchases electronic equipment from the United States to Japan, the service provider calculates the US customs duty rate and shipping costs to Japan, and displays the total price. Furthermore, the service provider can provide an interface to display the calculated price to the user. The user can review the price displayed by the service provider and proceed with the purchase only after they are satisfied. The service provider also displays a detailed breakdown of the price, allowing users to understand how customs duties and shipping costs are calculated. This ensures users receive transparent pricing information and can confidently make purchase decisions. Additionally, the service provider offers multiple shipping options, allowing users to choose the most suitable method. For example, it may offer options such as standard shipping, express shipping, and economy shipping, allowing users to compare the costs and delivery times of each. This allows the service provider to offer users flexible options and deliver a highly satisfying shopping experience.

[0072] The purchasing department allows users to make purchase decisions based on prices presented by the offering department. The purchasing department initiates the purchase process, for example, by the user clicking a purchase button. This process includes the user entering payment and shipping information. The purchasing department securely manages the information entered by the user and provides an interface to facilitate the payment process. For example, when a user enters credit card information, SSL encryption is used to protect the information and prevent unauthorized access by third parties. The purchasing department can also notify the user of the progress of the purchase process. For example, it can send a confirmation email when the order is finalized and a shipping notification when the product is ready for shipment. Furthermore, the purchasing department provides an interface that allows users to check their order history, making it easy to refer to past purchase information. This allows users to track the progress of their purchase in real time and wait for their products with peace of mind. The purchasing department also includes features to support return and exchange procedures, ensuring a smooth process even if the user is not satisfied with the product. For example, if a user wishes to return an item, the purchasing department provides return guidelines and guides them through the necessary documents and procedures. This allows the purchasing department to provide users with a safe and convenient purchasing experience and realize a highly reliable shopping system.

[0073] The judgment unit determines the authenticity and counterfeit status of products found by the search unit. For example, the judgment unit analyzes product images and descriptions to determine authenticity. Specifically, it uses AI to analyze product images and compare the characteristics of genuine and counterfeit products. For example, it checks the position and shape of brand logos and the design details of the product, and displays a warning if there is a possibility that it is a counterfeit. It also analyzes product descriptions using natural language processing technology to determine whether they match genuine products. For example, it checks whether the product specifications and manufacturer information are accurate, and displays a warning to the user if there is a possibility that it is a counterfeit. Furthermore, the judgment unit refers to past databases to check whether similar products have been reported as counterfeit in the past. This allows the judgment unit to support users in purchasing products with confidence. In addition, the judgment unit can collect feedback from users and continuously improve the accuracy of its judgment algorithm. For example, if a product purchased by a user is found to be counterfeit, that information is registered in the database and used for future judgments. This allows the judgment unit to always provide highly accurate authenticity judgments based on the latest information and gain the trust of users. Furthermore, the detection unit also has a function to suggest alternative products to the user if there is a high probability that the product is a counterfeit, thus supporting users in enjoying shopping with peace of mind.

[0074] The judgment unit can determine the authenticity of a product by analyzing its images and descriptions. For example, the judgment unit can analyze the product images using image recognition technology to determine authenticity. For example, the judgment unit can analyze the product's logo and design features to determine authenticity. The judgment unit can also analyze the product's descriptions using natural language processing technology to determine authenticity. For example, the judgment unit can analyze keywords and phrases contained in the product descriptions to determine authenticity. Furthermore, the judgment unit can improve the accuracy of its authenticity determination by referring to product reviews and ratings. For example, the judgment unit can analyze information contained in product reviews to determine authenticity. This improves the accuracy of authenticity determination by analyzing the product images and descriptions. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input product image data and description data into a generating AI and have the generating AI perform the authenticity determination.

[0075] The detection unit can display a warning to the user about products that may be counterfeit. For example, the detection unit can identify products that may be counterfeit by analyzing product images and descriptions. For example, the detection unit can identify products that may be counterfeit by analyzing the logo and design features of the product. The detection unit can also identify products that may be counterfeit by analyzing keywords and phrases contained in the product description. Furthermore, the detection unit can identify products that may be counterfeit by referring to product reviews and ratings. For example, the detection unit can identify products that may be counterfeit by analyzing the information contained in product reviews. This allows users to purchase products with confidence by displaying a warning about products that may be counterfeit. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input product image data and description data into a generating AI and have the generating AI perform the process of determining the possibility of counterfeiting.

[0076] The service provider can provide a price that includes customs duties and shipping costs. The service provider calculates the price based on information such as customs duty rates, shipping distance, and weight. For example, the service provider can obtain the customs duty rate for a product from a database and calculate shipping costs based on shipping distance and weight. The service provider can also provide an interface for presenting the calculated price to the user. For example, the service provider can provide an interface that displays price information so that the user can check the price. Furthermore, the service provider can update price information in real time and present the user with the latest price. For example, the service provider updates price information in accordance with changes in customs duty rates and shipping costs and presents it to the user. This makes it easier for the user to understand the total price by presenting a price that includes customs duties and shipping costs. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generating AI perform the calculation of the customs duty rate and shipping costs for a product.

[0077] The purchasing unit allows the user to decide on a purchase based on the price presented. The purchasing unit can initiate the purchase process, for example, by having the user click a purchase button. For example, the purchasing unit can allow the user to confirm the presented price and proceed with the purchase process by clicking a purchase button. The purchasing unit can also notify the user of the progress of the purchase process. For example, the purchasing unit can notify the user of each step of the purchase process so that they can check the progress. Furthermore, the purchasing unit can send a confirmation email to the user after the purchase process is completed. For example, the purchasing unit can send a confirmation email to the user to inform them that the purchase process is complete. This makes the purchase process smoother by allowing the user to decide on a purchase based on the presented price. Some or all of the above processes in the purchasing unit may be performed using AI, for example, or not using AI. For example, the purchasing unit can have a generating AI execute the user's purchase process.

[0078] The reception desk can estimate the user's emotions and adjust the product input method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, the reception desk can estimate that the user is stressed using an emotion estimation function such as an emotion engine or generative AI, and then provide a simple interface. Also, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, the reception desk can estimate that the user is relaxed using an emotion estimation function such as an emotion engine or generative AI, and then provide detailed input options. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick product input. For example, the reception desk can estimate that the user is in a hurry using an emotion estimation function such as an emotion engine or generative AI, and then prioritize voice input. This improves user convenience by adjusting the product input method according to the user's emotions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can have a generative AI perform emotion estimation on the user and adjust the input method based on the estimation results.

[0079] The reception desk can analyze the user's past purchase history and suggest the most suitable products to input. For example, the reception desk can automatically display products that the user has frequently purchased in the past as candidates. For example, the reception desk can retrieve the user's past purchase history from a database and display frequently purchased products as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can analyze the input methods the user has used in the past and suggest the most suitable input method. Furthermore, the reception desk can predict and suggest products that the user will purchase at a specific time based on their past purchase history. For example, the reception desk analyzes the user's past purchase history and predicts and suggests products that will be purchased at a specific time. This makes it possible to suggest the most suitable products by analyzing the user's past purchase history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past purchase history into a generating AI and have the generating AI execute suggestions for the most suitable products to input.

[0080] The input system can suggest input options based on the user's current interests and trends when the user enters a product. For example, the input system can display relevant products as suggestions based on products or categories the user has recently searched for. For example, the input system can analyze the user's recent search history and display relevant products as suggestions. The input system can also analyze the user's social media activity and suggest products of interest. For example, the input system can analyze the user's social media posts and suggest products of interest. Furthermore, the input system can suggest products recommended to the user based on current trends and popular products. For example, the input system can retrieve current trends and popular products from a database and suggest products recommended to the user. This improves user convenience by suggesting input options based on the user's current interests and trends. Some or all of the above processing in the input system may be performed using AI, for example, or not. For example, the input system can input the user's interests and trends into a generating AI and have the generating AI perform the task of suggesting input options.

[0081] The reception desk can estimate the user's emotions and determine the priority of the entered products based on the estimated emotions. For example, if the user is excited, the reception desk will prioritize displaying highly relevant products. For example, the reception desk will estimate that the user is excited using an emotion estimation function such as an emotion engine or generative AI, and then prioritize displaying highly relevant products. The reception desk can also prioritize displaying products containing detailed information if the user is relaxed. For example, the reception desk will estimate that the user is relaxed using an emotion estimation function such as an emotion engine or generative AI, and then prioritize displaying products containing detailed information. Furthermore, if the user is in a hurry, the reception desk can prioritize displaying products that can be purchased immediately. For example, the reception desk will estimate that the user is in a hurry using an emotion estimation function such as an emotion engine or generative AI, and then prioritize displaying products that can be purchased immediately. This improves user convenience by determining product priorities according to the user's emotions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can have a generative AI perform user emotion estimation and determine product priorities based on the estimation results.

[0082] The reception desk can prioritize inputting highly relevant products based on the user's geographical location information when inputting products. For example, the reception desk can prioritize displaying products that can be purchased from nearby stores based on the user's current location. For example, the reception desk can obtain the user's current location and prioritize displaying products that can be purchased from nearby stores. The reception desk can also prioritize displaying products that are popular in the user's region. For example, the reception desk can obtain popular products in the user's region from a database and prioritize displaying them. Furthermore, the reception desk can prioritize displaying products that can be delivered quickly based on the user's geographical location information. For example, the reception desk can obtain the user's geographical location information and prioritize displaying products that can be delivered quickly. This improves user convenience by prioritizing the input of highly relevant products based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize the input of highly relevant products.

[0083] The reception desk can analyze the user's social media activity when inputting products and input related products. For example, the reception desk can display products that the user has "liked" or commented on on social media as suggestions. The reception desk can also prioritize displaying products introduced by influencers that the user follows. For example, the reception desk can analyze posts from influencers that the user follows and prioritize displaying products they have introduced. Furthermore, the reception desk can analyze the content of the user's social media posts and suggest products of interest. For example, the reception desk can analyze the content of the user's social media posts and suggest products of interest. In this way, by analyzing the user's social media activity, related products can be input. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity into a generating AI and have the generating AI input related products.

[0084] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is relaxed, the search unit can display search results containing detailed information. For example, the search unit can estimate that the user is relaxed using an emotion estimation function such as an emotion engine or generative AI, and then display search results containing detailed information. The search unit can also display concise search results that get straight to the point if the user is in a hurry. For example, the search unit can estimate that the user is in a hurry using an emotion estimation function such as an emotion engine or generative AI, and then display concise search results that get straight to the point. Furthermore, if the user is excited, the search unit can display search results with visually stimulating effects. For example, the search unit can estimate that the user is excited using an emotion estimation function such as an emotion engine or generative AI, and then display search results with visually stimulating effects. By adjusting how search results are displayed according to the user's emotions, user convenience is improved. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can have a generating AI perform user sentiment estimation and adjust how search results are displayed based on the estimation results.

[0085] The search function can adjust the priority of search results based on product popularity and ratings during a search. For example, the search function may prioritize displaying highly-rated products. For example, it may prioritize displaying highly-rated products based on their rating score and number of reviews. The search function can also prioritize displaying highly popular products. For example, it may prioritize displaying highly popular products based on their sales figures and popularity. Furthermore, the search function may prioritize displaying highly relevant products based on the user's past rating history. For example, it may analyze the user's past rating history and prioritize displaying highly relevant products. This improves user convenience by adjusting the priority of search results based on product popularity and ratings. Some or all of the above processing in the search function may be performed using AI, for example, or without AI. For example, the search function can input product popularity and ratings into a generating AI and have the generating AI adjust the priority of search results.

[0086] The search unit can apply different search algorithms depending on the product category during a search. For example, in the electronics category, the search unit displays search results based on specifications and functions. For example, the search unit displays search results based on the specifications and functions of electronic devices. The search unit can also display search results based on trends and styles in the fashion category. For example, the search unit displays search results based on fashion trends and styles. Furthermore, in the food category, the search unit can display search results based on expiration dates and origins. For example, the search unit displays search results based on the expiration dates and origins of food products. By applying different search algorithms depending on the product category, the accuracy of the search results is improved. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input product category information into a generating AI and have the generating AI execute the application of the search algorithm.

[0087] The search unit can estimate the user's emotions and adjust the display order of search results based on the estimated emotions. For example, if the user is relaxed, the search unit can display search results containing detailed information at the top. For example, the search unit can estimate that the user is relaxed using an emotion estimation function such as an emotion engine or generative AI, and then display search results containing detailed information at the top. The search unit can also display concise search results that get straight to the point at the top if the user is in a hurry. For example, the search unit can estimate that the user is in a hurry using an emotion estimation function such as an emotion engine or generative AI, and then display concise search results that get straight to the point at the top. Furthermore, if the user is excited, the search unit can also display search results with visually stimulating effects at the top. For example, the search unit can estimate that the user is excited using an emotion estimation function such as an emotion engine or generative AI, and then display search results with visually stimulating effects at the top. By adjusting the display order of search results according to the user's emotions, user convenience is improved. Some or all of the above processing in the search unit may be performed using AI, for example, or without using AI. For example, the search unit can have a generating AI perform user sentiment estimation and adjust the display order of search results based on the estimation results.

[0088] The search unit can display search results while considering the geographical distribution of products. For example, the search unit can prioritize displaying products available for purchase from stores close to the user's current location. For example, the search unit can obtain the user's current location and prioritize displaying products available for purchase from nearby stores. The search unit can also prioritize displaying products popular in the user's region. For example, the search unit can obtain popular products in the user's region from a database and prioritize displaying them. Furthermore, the search unit can prioritize displaying products that can be delivered quickly based on the user's geographical location information. For example, the search unit can obtain the user's geographical location information and prioritize displaying products that can be delivered quickly. This improves user convenience by displaying search results while considering the geographical distribution of products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the geographical distribution of products into a generating AI and have the generating AI execute the display of search results.

[0089] The search unit can improve the accuracy of search results by referring to related literature on products during a search. For example, the search unit can display highly relevant products based on product reviews and ratings. For example, the search unit can retrieve product reviews and ratings from a database and display highly relevant products. The search unit can also refer to product technical literature and display search results based on specifications and functions. For example, the search unit can refer to product technical literature and display search results based on specifications and functions. Furthermore, the search unit can display highly relevant products based on product usage examples and case studies. For example, the search unit can refer to product usage examples and case studies and display highly relevant products. This improves the accuracy of search results by referring to related literature on products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input product related literature into a generating AI and have the generating AI perform the task of improving the accuracy of search results.

[0090] The service provider can estimate the user's emotions and adjust the pricing method based on the estimated emotions. For example, if the user is relaxed, the service provider can present detailed pricing information. For example, the service provider can estimate that the user is relaxed using an emotion estimation function such as an emotion engine or generative AI, and then present detailed pricing information. The service provider can also present concise pricing information if the user is in a hurry. For example, the service provider can estimate that the user is in a hurry using an emotion estimation function such as an emotion engine or generative AI, and then present concise pricing information. Furthermore, if the user is excited, the service provider can present pricing information with visually stimulating effects. For example, the service provider can estimate that the user is excited using an emotion estimation function such as an emotion engine or generative AI, and then present pricing information with visually stimulating effects. By adjusting the pricing method according to the user's emotions, user convenience is improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have a generating AI perform user sentiment estimation and adjust the pricing method based on the estimation results.

[0091] The service provider can adjust the level of detail in pricing based on the importance of the product when presenting prices. For example, the service provider may present detailed pricing information for expensive products. The service provider may also present concise pricing information for low-priced products. Furthermore, the service provider may present detailed pricing information for products of particular interest to the user. This improves user convenience by adjusting the level of detail in pricing based on the importance of the product. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the product into a generating AI and have the generating AI perform the adjustment of the level of detail in pricing.

[0092] The pricing unit can apply different pricing algorithms depending on the product category when providing prices. For example, in the electronics category, the pricing unit can provide prices based on specifications and functions. For example, in the fashion category, the pricing unit can provide prices based on brands and designs. For example, in the food category, the pricing unit can provide prices based on origin and expiration dates. For example, in the food category, the pricing unit can provide prices based on origin and expiration dates. This improves the accuracy of price quotes by applying different pricing algorithms depending on the product category. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input product category information into a generating AI and have the generating AI apply the pricing algorithm.

[0093] The service provider can estimate the user's emotions and adjust the length of the price presentation based on the estimated emotions. For example, if the user is relaxed, the service provider can present detailed price information for a longer duration. For example, the service provider can estimate that the user is relaxed using an emotion estimation function such as an emotion engine or generative AI, and then present detailed price information for a longer duration. The service provider can also present concise price information for a shorter duration if the user is in a hurry. For example, the service provider can estimate that the user is in a hurry using an emotion estimation function such as an emotion engine or generative AI, and then present concise price information for a shorter duration. Furthermore, if the user is excited, the service provider can present price information for a longer duration with visually stimulating effects. For example, the service provider can estimate that the user is excited using an emotion estimation function such as an emotion engine or generative AI, and then present price information for a longer duration with visually stimulating effects. By adjusting the length of the price presentation according to the user's emotions, user convenience is improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have a generating AI perform user sentiment estimation and adjust the length of the price presentation based on the estimation results.

[0094] The service provider can determine price priority based on the product's submission date when displaying prices. For example, the service provider may prioritize displaying new or limited-edition products. For example, the service provider may prioritize displaying price information for new or limited-edition products. The service provider can also prioritize displaying products during sales periods. For example, the service provider may retrieve products during sales periods from its database and prioritize displaying them. Furthermore, the service provider may prioritize displaying seasonal or in-season products. For example, the service provider may retrieve seasonal or in-season products from its database and prioritize displaying them. This improves user convenience by determining price priority based on the product's submission date. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the product's submission date into a generating AI and have the generating AI determine the price priority.

[0095] The service provider can adjust the order of prices based on the relevance of the products when presenting prices. For example, the service provider can prioritize displaying products that are highly relevant to products that the user has previously purchased. For example, the service provider can analyze the user's past purchase history and prioritize displaying highly relevant products. The service provider can also prioritize displaying highly relevant products based on the user's current interests and trends. For example, the service provider can analyze the user's current interests and trends and prioritize displaying highly relevant products. Furthermore, the service provider can analyze the user's social media activity and prioritize displaying highly relevant products. For example, the service provider can analyze the user's social media activity and prioritize displaying highly relevant products. This improves user convenience by adjusting the order of prices based on the relevance of the products. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of products into a generating AI and have the generating AI perform the adjustment of the price order.

[0096] The purchasing unit can estimate the user's emotions and adjust the purchase decision-making process based on those emotions. For example, if the user is relaxed, the purchasing unit can present detailed purchase information. For example, the purchasing unit can estimate that the user is relaxed using an emotion estimation function such as an emotion engine or generative AI, and then present detailed purchase information. The purchasing unit can also present concise purchase information if the user is in a hurry. For example, the purchasing unit can estimate that the user is in a hurry using an emotion estimation function such as an emotion engine or generative AI, and then present concise purchase information. Furthermore, if the user is excited, the purchasing unit can present purchase information with visually stimulating effects. For example, the purchasing unit can estimate that the user is excited using an emotion estimation function such as an emotion engine or generative AI, and then present purchase information with visually stimulating effects. This improves user convenience by adjusting the purchase decision-making process according to the user's emotions. Some or all of the above processing in the purchasing unit may be performed using AI, for example, or without AI. For example, the purchasing department can have a generative AI perform user sentiment estimation and adjust the purchasing decision-making process based on the estimation results.

[0097] The purchasing department can analyze the user's past purchase history to select the optimal purchase method at the time of purchase. For example, the purchasing department can prioritize suggesting purchase methods the user has used in the past (credit card, electronic money, etc.). For example, the purchasing department can analyze the user's past purchase history and prioritize suggesting purchase methods used in the past. The purchasing department can also predict and suggest purchase methods to be used at specific times based on the user's past purchase history. For example, the purchasing department can analyze the user's past purchase history and predict and suggest purchase methods to be used at specific times. Furthermore, the purchasing department can analyze the user's past purchase history and suggest the most efficient purchase method. For example, the purchasing department can analyze the user's past purchase history and suggest the most efficient purchase method. In this way, the optimal purchase method can be selected by analyzing the user's past purchase history. Some or all of the above processing in the purchasing department may be performed using AI, for example, or without AI. For example, the purchasing department can input the user's past purchase history into a generating AI and have the generating AI select the optimal purchase method.

[0098] The purchasing department can customize the purchase method based on the user's current lifestyle at the time of purchase. For example, if the user is busy, the purchasing department can suggest a quick purchase procedure. For example, the purchasing department can analyze that the user is busy and suggest a quick purchase procedure. The purchasing department can also suggest a detailed purchase procedure if the user is relaxed. For example, the purchasing department can analyze that the user is relaxed and suggest a detailed purchase procedure. Furthermore, if the user is traveling, the purchasing department can suggest the option to change the delivery address. For example, the purchasing department can analyze that the user is traveling and suggest the option to change the delivery address. This improves user convenience by customizing the purchase method based on the user's current lifestyle. Some or all of the above processing in the purchasing department may be performed using AI, for example, or not using AI. For example, the purchasing department can input the user's lifestyle into a generating AI and have the generating AI perform the customization of the purchase method.

[0099] The purchasing function can estimate the user's emotions and determine purchase priorities based on those emotions. For example, if the user is excited, the purchasing function will prioritize displaying highly relevant products. For example, the purchasing function will estimate the user's excitement using an emotion estimation function such as an emotion engine or generative AI, and then prioritize displaying highly relevant products. The purchasing function can also prioritize displaying products containing detailed information if the user is relaxed. For example, the purchasing function will estimate the user's relaxation using an emotion estimation function such as an emotion engine or generative AI, and then prioritize displaying products containing detailed information. Furthermore, if the user is in a hurry, the purchasing function can prioritize displaying products that can be purchased immediately. For example, the purchasing function will estimate the user's urgency using an emotion estimation function such as an emotion engine or generative AI, and then prioritize displaying products that can be purchased immediately. This improves user convenience by determining purchase priorities according to the user's emotions. Some or all of the above processing in the purchasing function may be performed using AI, for example, or without AI. For example, the purchasing department can have a generative AI perform sentiment estimation of the user and then determine purchase priorities based on the estimation results.

[0100] The purchasing function can select the optimal purchasing method at the time of purchase, taking into account the user's geographical location. For example, the purchasing function can prioritize displaying products available from nearby stores based on the user's current location. For example, the purchasing function can obtain the user's current location and prioritize displaying products available from nearby stores. The purchasing function can also prioritize displaying products popular in the user's region. For example, the purchasing function can obtain popular products in the user's region from a database and prioritize displaying them. Furthermore, the purchasing function can prioritize displaying products that can be delivered quickly, based on the user's geographical location. For example, the purchasing function can obtain the user's geographical location and prioritize displaying products that can be delivered quickly. This improves user convenience by selecting the optimal purchasing method considering the user's geographical location. Some or all of the above processing in the purchasing function may be performed using AI, for example, or without AI. For example, the purchasing function can input the user's geographical location information into a generating AI and have the generating AI select the optimal purchasing method.

[0101] The purchasing function can analyze a user's social media activity and suggest purchasing options at the time of purchase. For example, the purchasing function can display products that the user has "liked" or commented on on social media as potential purchase options. The purchasing function can also prioritize displaying products introduced by influencers that the user follows. For example, the purchasing function can analyze posts from influencers that the user follows and prioritize displaying products they have introduced. Furthermore, the purchasing function can analyze the content of the user's social media posts and suggest products of interest. For example, the purchasing function can analyze the content of the user's social media posts and suggest products of interest. In this way, by analyzing the user's social media activity, the optimal purchasing method can be suggested. Some or all of the above processing in the purchasing function may be performed using AI, for example, or without AI. For example, the purchasing function can input the user's social media activity into a generating AI and have the generating AI execute the suggestion of purchasing methods.

[0102] The judgment unit can estimate the user's emotions and adjust the authenticity determination method based on the estimated emotions. For example, if the user is relaxed, the judgment unit can present detailed authenticity determination information. For example, the judgment unit can estimate that the user is relaxed using an emotion estimation function such as an emotion engine or generative AI, and then present detailed authenticity determination information. Furthermore, if the user is in a hurry, the judgment unit can present concise authenticity determination information that gets straight to the point. For example, the judgment unit can estimate that the user is in a hurry using an emotion estimation function such as an emotion engine or generative AI, and then present concise authenticity determination information that gets straight to the point. In addition, if the user is excited, the judgment unit can present authenticity determination information with visually stimulating effects. For example, the judgment unit can estimate that the user is excited using an emotion estimation function such as an emotion engine or generative AI, and then present authenticity determination information with visually stimulating effects. This improves user convenience by adjusting the authenticity determination method according to the user's emotions. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit may have a generating AI perform user emotion estimation and adjust the method of authenticity determination based on the estimation results.

[0103] The judgment unit can improve the accuracy of its judgment based on the level of detail in the product images and descriptions when determining authenticity. For example, the judgment unit can use high-resolution images to check the details of the product. For example, the judgment unit can analyze high-resolution images to check the details of the product. The judgment unit can also analyze detailed descriptions to determine the authenticity of the product. For example, the judgment unit can analyze detailed descriptions to determine the authenticity of the product. Furthermore, the judgment unit can refer to product reviews and ratings to improve the accuracy of its authenticity determination. For example, the judgment unit can refer to product reviews and ratings to improve the accuracy of its authenticity determination. This improves user convenience by improving the accuracy of the judgment based on the level of detail in the product images and descriptions. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the level of detail in the product images and descriptions into a generating AI and have the generating AI perform the improvement of the judgment accuracy.

[0104] The judgment unit can apply different judgment algorithms depending on the product category when determining authenticity. For example, in the electronics category, the judgment unit can determine authenticity based on specifications and functions. For example, the judgment unit can determine authenticity based on the specifications and functions of electronic devices. In the fashion category, the judgment unit can also determine authenticity based on brand and design. For example, the judgment unit can determine authenticity based on the brand and design of fashion items. Furthermore, in the food category, the judgment unit can also determine authenticity based on the place of origin and expiration date. For example, the judgment unit can determine authenticity based on the place of origin and expiration date of food items. By applying different judgment algorithms depending on the product category, the accuracy of the judgment is improved. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input product category information into a generating AI and have the generating AI execute the application of the judgment algorithm.

[0105] The judgment unit can estimate the user's emotions and adjust the display method of the judgment result based on the estimated user emotions. For example, if the user is nervous, the judgment unit can provide a simple and highly visible display method. For example, the judgment unit can estimate that the user is nervous using an emotion estimation function such as an emotion engine or generative AI, and provide a simple and highly visible display method. The judgment unit can also provide a display method that includes detailed information if the user is relaxed. For example, the judgment unit can estimate that the user is relaxed using an emotion estimation function such as an emotion engine or generative AI, and provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the judgment unit can provide a display method that gets straight to the point. For example, the judgment unit can estimate that the user is in a hurry using an emotion estimation function such as an emotion engine or generative AI, and provide a display method that gets straight to the point. By adjusting the display method of the judgment result according to the user's emotions, user convenience is improved. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can have a generating AI perform user emotion estimation and adjust the display method of the judgment result based on the estimation result.

[0106] The judgment unit can perform authenticity determination by considering the geographical distribution of the goods. For example, the judgment unit can determine authenticity based on the region information of the goods' origin. For example, the judgment unit can obtain the region information of the goods' origin and determine authenticity. The judgment unit can also determine authenticity based on the region information of the goods' delivery destination. For example, the judgment unit can obtain the region information of the goods' delivery destination and determine authenticity. Furthermore, the judgment unit can analyze the goods' distribution route and determine authenticity. For example, the judgment unit can analyze the goods' distribution route and determine authenticity. By considering the geographical distribution of the goods, the accuracy of the determination is improved. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the geographical distribution of the goods into a generating AI and have the generating AI perform the determination.

[0107] The judgment unit can improve the accuracy of its judgment by referring to relevant literature on the product when determining authenticity. For example, the judgment unit can refer to the product's technical literature and determine authenticity based on its specifications and functions. The judgment unit can also determine authenticity based on product usage examples and case studies. For example, the judgment unit can refer to product usage examples and case studies to determine authenticity. Furthermore, the judgment unit can improve the accuracy of its authenticity judgment by referring to product reviews and ratings. For example, the judgment unit can refer to product reviews and ratings to improve the accuracy of its authenticity judgment. In this way, the accuracy of the judgment is improved by referring to relevant literature on the product. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input relevant literature on the product into a generating AI and have the generating AI perform the improvement of the judgment accuracy.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The input system can be equipped with a function to automatically complete detailed product information entered by the user. For example, if the user only enters the product name, the input system can automatically complete and present related product details (e.g., manufacturer name, model number, specifications, etc.) to the user. The input system can also display suggested related products based on the information entered by the user. For example, if the user enters "smartphone," the input system can display the latest smartphone models or popular models as suggestions. Furthermore, the input system can display product reviews and ratings based on the information entered by the user. For example, if the user enters the model number of a specific product, the system can automatically display reviews and ratings for that product for the user to refer to. This improves user convenience by automatically completing detailed product information based on the information entered by the user.

[0110] The authentication unit can refer to the user's past purchase history when determining the authenticity of a product. For example, it can perform authenticity checks on similar products based on the authenticity check results of products the user has purchased in the past. The authentication unit can also refer to reviews and ratings of products the user has purchased in the past to improve the accuracy of authenticity checks. For example, it can perform authenticity checks on similar products based on reviews of products the user has purchased in the past. Furthermore, the authentication unit can analyze trends in authenticity checks for products of specific brands or manufacturers from the user's past purchase history to improve the accuracy of authenticity checks. For example, if all products of a particular brand that the user has purchased in the past have been genuine, it can quickly perform authenticity checks on products of that brand. In this way, by referring to the user's past purchase history, the accuracy of authenticity checks is improved, and user convenience is enhanced.

[0111] The detection unit can suggest alternative products to users for items that may be counterfeit. For example, if the detection unit identifies an item that may be counterfeit, it can suggest a genuine product in the same category to the user. The detection unit can also display a warning to the user about items that may be counterfeit and recommend purchasing from a reliable vendor. For example, if the detection unit identifies an item that may be counterfeit, it can show the user a reliable vendor that handles that item. Furthermore, the detection unit can provide users with detailed information about items that may be counterfeit and explain the risks of counterfeit goods. For example, the detection unit can provide users with information about the characteristics and risks of items that may be counterfeit, allowing them to make informed purchasing decisions. This improves user convenience by suggesting alternative products for items that may be counterfeit.

[0112] The service provider can offer a filtering function based on the user's budget when presenting prices that include customs duties and shipping costs. For example, the service provider can display only products that are within the user's set budget. Furthermore, the service provider can suggest adjustments to customs duties and shipping costs to bring products exceeding the user's budget within that budget. For example, if a user selects a product that exceeds their budget, the service provider can suggest alternative shipping methods to reduce customs duties and shipping costs. Additionally, the service provider can offer installment payment options for products exceeding the user's budget. For example, if a user selects a product that exceeds their budget, the service provider can offer installment payment options to support the purchase. This improves user convenience by providing a filtering function based on the user's budget when presenting prices that include customs duties and shipping costs.

[0113] The purchasing department can provide users with information about the timing of their purchase when they are deciding to buy something. For example, the purchasing department can notify users that a particular product is on sale and suggest the best time to buy it. The purchasing department can also provide users with information about the likelihood of a particular product going out of stock and encourage them to buy it sooner. For example, the purchasing department can notify users that a particular product is likely to go out of stock and suggest the best time to buy it. Furthermore, the purchasing department can provide users with information about the timing of their purchase when they are deciding to buy something, thereby improving user convenience.

[0114] The reception desk can estimate the user's emotions and adjust the product input method based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick product input. In this way, the user experience is improved by adjusting the product input method according to the user's emotions.

[0115] The search engine can estimate the user's emotions and adjust how search results are displayed based on that estimation. For example, if the user is relaxed, it can display search results with detailed information. If the user is in a hurry, it can display concise search results that get straight to the point. Furthermore, if the user is excited, it can display search results with visually stimulating effects. By adjusting how search results are displayed according to the user's emotions, the user experience is improved.

[0116] The service provider can estimate the user's emotions and adjust the pricing presentation based on those emotions. For example, if the user is relaxed, detailed pricing information can be presented. If the user is in a hurry, concise pricing information can be presented. Furthermore, if the user is excited, pricing information with visually stimulating effects can be presented. By adjusting the pricing presentation method according to the user's emotions, user convenience is improved.

[0117] The purchasing function can estimate the user's emotions and adjust the purchasing decision process based on those emotions. For example, if the user is relaxed, it can present detailed purchasing information. If the user is in a hurry, it can present concise purchasing information that gets straight to the point. Furthermore, if the user is excited, it can present purchasing information with visually stimulating effects. By adjusting the purchasing decision process according to the user's emotions, this improves user convenience.

[0118] The judgment unit can estimate the user's emotions and adjust the authenticity determination method based on the estimated emotions. For example, if the user is relaxed, detailed authenticity determination information can be presented. If the user is in a hurry, concise authenticity determination information can be presented. Furthermore, if the user is excited, authenticity determination information with visually stimulating effects can be presented. In this way, the user convenience is improved by adjusting the authenticity determination method according to the user's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk receives the product the user wants. The specific type and format of the product the user enters may include, but is not limited to, electronic devices, clothing, books, etc. The reception desk can also save the product information entered by the user to a database and send it to the search unit. Step 2: The search unit searches for products from online shopping apps around the world based on the information entered by the reception unit. The search unit simultaneously sends search queries to multiple online shopping apps and retrieves search results. The search unit can also filter the search results to select products that meet the user's needs. Step 3: The service provider displays the price of the product found by the search service provider, including customs duties and shipping costs. The service provider can also calculate the price based on information such as customs duty rates, shipping distance, and weight, and provide an interface to display the calculated price to the user. Step 4: The purchasing department allows the user to decide on a purchase based on the price presented by the offering department. The purchasing department can initiate the purchase process when the user clicks the purchase button and can also notify the user of the progress of the purchase process. Step 5: The judgment unit determines the authenticity and counterfeit status of the products found by the search unit. The judgment unit analyzes the product images and descriptions to determine authenticity. It can also display a warning to the user for products that may be counterfeit.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the reception unit, search unit, provision unit, purchase unit, and determination 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 control unit 46A of the smart device 14 and stores the product information entered by the user in the database 24. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for products from online shopping apps around the world. The provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and presents the price including customs duties and shipping costs. The purchase unit is implemented by, for example, the control unit 46A of the smart device 14 and initiates the purchase procedure for the user. The determination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and performs authenticity and counterfeit product detection. 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.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, search unit, provision unit, purchase unit, and determination unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and stores the product information entered by the user in the database 24. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for products from online shopping apps around the world. The provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and presents the price including customs duties and shipping costs. The purchase unit is implemented by, for example, the control unit 46A of the smart glasses 214 and initiates the purchase procedure for the user. The determination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and performs authenticity and counterfeit product detection. 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.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, search unit, provision unit, purchase unit, and determination 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 control unit 46A of the headset terminal 314 and stores the product information entered by the user in the database 24. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for products from online shopping apps around the world. The provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and presents the price including customs duties and shipping costs. The purchase unit is implemented by, for example, the control unit 46A of the headset terminal 314 and allows the user to initiate the purchase procedure. The determination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and performs authenticity and counterfeit product detection. 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.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the reception unit, search unit, provision unit, purchase unit, and determination 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 control unit 46A of the robot 414 and stores the product information entered by the user in the database 24. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for products from online shopping apps around the world. The provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and presents the price including customs duties and shipping costs. The purchase unit is implemented by, for example, the control unit 46A of the robot 414 and initiates the purchase procedure for the user. The determination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and performs authenticity and counterfeit detection of products. 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) A reception area where users enter the products they want, Based on the information entered by the reception unit, a search unit searches for products from online shopping apps around the world, A provision unit that displays the price of the goods found by the search unit, including customs duties and transportation costs, A purchasing unit where the user decides to purchase based on the price presented by the aforementioned supply unit, The system includes a determination unit that performs authenticity and counterfeit detection on products found by the search unit. A system characterized by the following features. (Note 2) The determination unit, Authenticity is determined by analyzing product images and descriptions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The determination unit, Display a warning to the user about products that may be counterfeit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We will quote a price that includes customs duties and shipping costs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned purchasing department, Users make purchase decisions based on the price offered. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the product input method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the user's past purchase history and suggest the most suitable products to input. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter product information, the system will suggest input options based on their current interests and trends. 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 determines the priority of the entered products based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering products, the system prioritizes displaying highly relevant products 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 When entering products, the system analyzes the user's social media activity and enters relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, When searching, the search results are prioritized based on the popularity and ratings of the products. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When searching, different search algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, It estimates the user's sentiment and adjusts the display order of search results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching, display search results while considering the geographical distribution of the products. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When searching, refer to related literature for the product to improve the accuracy of search results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates user sentiment and adjusts pricing based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing a price quote, 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 20) The aforementioned supply unit is, When providing a price quote, different pricing algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the price offer based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When submitting a price quote, we prioritize prices based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When displaying prices, the order of prices will be adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned purchasing department, It estimates user emotions and adjusts the purchasing decision process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned purchasing department, At the time of purchase, the system analyzes the user's past purchase history to select the optimal purchase method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned purchasing department, At the time of purchase, the purchase method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned purchasing department, It estimates user emotions and determines purchase priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned purchasing department, When making a purchase, the system will select the most suitable purchase method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned purchasing department, When a user makes a purchase, we analyze their social media activity and suggest ways to make that purchase. The system described in Appendix 1, characterized by the features described herein. (Note 30) The determination unit, The system estimates the user's emotions and adjusts the authenticity determination method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The determination unit, When determining authenticity, the accuracy of the determination is improved based on the level of detail in the product images and descriptions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The determination unit, When determining authenticity, different authentication algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 33) The determination unit, The system estimates the user's emotions and adjusts how the judgment results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The determination unit, When determining authenticity, the geographical distribution of the goods is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 35) The determination unit, When determining authenticity, we improve the accuracy of the determination by referring to relevant literature on the product. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 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 where users enter the products they want, Based on the information entered by the reception unit, a search unit searches for products from online shopping apps around the world, A provision unit that displays the price of the goods found by the search unit, including customs duties and transportation costs, A purchasing unit where the user decides to purchase based on the price presented by the aforementioned supply unit, The system includes a determination unit that performs authenticity and counterfeit detection on products found by the search unit. A system characterized by the following features.

2. The determination unit, Authenticity is determined by analyzing product images and descriptions. The system according to feature 1.

3. The determination unit, Display a warning to the user about products that may be counterfeit. The system according to feature 1.

4. The aforementioned supply unit is, We will quote a price that includes customs duties and shipping costs. The system according to feature 1.

5. The aforementioned purchasing department, Users make purchase decisions based on the price offered. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the product input method based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze the user's past purchase history and suggest the most suitable products to input. The system according to feature 1.

8. The aforementioned reception unit is When users enter product information, the system will suggest input options based on their current interests and trends. The system according to feature 1.

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

10. The aforementioned reception unit is When entering products, the system prioritizes displaying highly relevant products based on the user's geographical location. The system according to feature 1.

Images