system

Abstract

The system according to this embodiment aims to enable users to efficiently find and purchase the products they want. [Solution] The system according to the embodiment comprises a registration unit, a search unit, a notification unit, and a purchase unit. The registration unit registers the products the user wants in a list. The search unit continues to search for products based on the information registered by the registration unit. The notification unit notifies the user of the products found by the search unit. The purchase unit purchases the products notified by the notification unit.

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, it takes time and effort for a user to find a desired product, and there is a risk of missing it.

[0005] The system according to the embodiment aims to efficiently find and purchase a product desired by the user.

Means for Solving the Problems

[0006] The system according to the embodiment includes a registration unit, a search unit, a notification unit, and a purchase unit. The registration unit registers a product desired by the user in a list. The search unit continuously searches for products based on the information registered by the registration unit. The notification unit notifies the user of the products found by the search unit. The purchase unit purchases the products notified by the notification unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to efficiently find and purchase the products they want. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The product search system according to an embodiment of the present invention is a system that uses a generating AI to efficiently find products that users want and provides them with purchase opportunities. The product search system allows users to register desired products in a list, and the generating AI continues to search for products based on the registered information. When a product is found that matches the criteria, the user is notified by push notification. Furthermore, if the product is from a group company, the purchase process can be completed with a single button click. This mechanism allows users to save time searching and purchase products at a discount. In addition, the company can acquire loyal customers and expect stable revenue. First, the user registers the products they want in a list. At this time, detailed product information (size, color, year, etc.) is also registered. For example, if a user is looking for a watch from a specific year, they register information such as the year, brand, and color. This information is input into the generating AI. Next, the generating AI analyzes the input information and continues to search for products that match the registered criteria. The generating AI searches multiple sites and databases on the internet to find products that meet the criteria. For example, if the watch the user is looking for is sold at a specific online shop, the generating AI retrieves that information. When a product is found that matches the criteria, the generating AI notifies the user by push notification. For example, if the user finds the watch they are looking for, the generating AI will send a notification such as, "We have found the watch you are looking for." This allows the user to quickly complete the purchase process without missing out on the product. Furthermore, if it is a group company, the purchase process can be completed with a single click. For example, if the product the user is looking for is sold at a group company's online shop, the generating AI will provide that information, and the user can complete the purchase process with a single click. This allows the user to purchase the product without any hassle. This system saves users the trouble of searching and allows them to purchase products at a discount. For example, even when searching for rare items such as limited editions or old products, the generating AI constantly keeps its antenna up and continues searching, so the user can find the product without any effort. In addition, the company can acquire a loyal customer base and expect stable revenue.For example, by offering opportunities to purchase everyday necessities at a discount, users can be expected to continue using the company's services. This allows the product search system to efficiently find the products users want and provide them with purchase opportunities.

[0029] The product search system according to this embodiment comprises a registration unit, a search unit, a notification unit, and a purchase unit. The registration unit registers products that the user wants in a list. Products that the user wants include, but are not limited to, electronic devices, clothing, and food. The registration unit can register products in the list by methods such as manual input, barcode scanning, and voice input. The search unit continues to search for products based on the information registered by the registration unit. The search unit searches multiple sites and databases on the internet, for example, to find products that meet the criteria. For example, the search unit can search a specific online shop or marketplace and find products that match the criteria. The notification unit notifies the user by push notification if a product found by the search unit matches the criteria. The notification unit can notify the user by methods such as push notification, email notification, and SMS notification. The purchase unit purchases the product notified by the notification unit. The purchase unit can, for example, purchase products sold at the online shop of a group company with a single button click. As a result, the product search system according to this embodiment can efficiently find products that the user wants and provide them with a purchase opportunity. Some or all of the above-described processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can use AI to automatically complete the detailed information of products when a user registers desired products to a list. Some or all of the above-described processes in the search unit may be performed using generative AI, for example, or without generative AI. For example, the search unit can use generative AI to efficiently find products when searching multiple sites or databases on the internet. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to send a notification to the user at the optimal time when a found product matches the criteria. Some or all of the above-described processes in the purchase unit may be performed using AI, for example, or without AI.For example, the purchasing department can use AI to suggest the optimal purchase procedure when purchasing products sold on the group company's online shop. This allows the product search system according to this embodiment to efficiently find the products the user wants and provide them with purchase opportunities.

[0030] The registration unit registers items that the user wants to buy. These items may include, but are not limited to, electronic devices, clothing, and food. The registration unit can register items using methods such as manual input, barcode scanning, and voice input. Specifically, for manual input, the user can enter detailed information such as product name, model number, and desired price. For barcode scanning, the smartphone camera is used to scan the product's barcode, automatically retrieving product information. For voice input, the user voice-instructs the desired product, which is then converted to text using speech recognition technology and registered in the list. Furthermore, the registration unit can use AI to supplement the information entered by the user. For example, if the user enters "latest smartphone," the AI ​​will generate a list of the latest smartphones on the market and suggest them to the user. It can also analyze the user's past purchase and search history and automatically add items that the user might be interested in to the list. This allows the registration unit to efficiently and accurately register items that the user wants to buy.

[0031] The search unit continuously searches for products based on information registered by the registration unit. For example, the search unit searches multiple websites and databases on the internet to find products that meet the criteria. Specifically, the search unit periodically scans specific online shops and marketplaces to find products that match the registered product criteria. For example, it can use price comparison sites and review sites to find the best deals and highly-rated products. Furthermore, the search unit can efficiently find products using generative AI. Generative AI uses natural language processing technology to analyze vast amounts of data on the internet and identify products that best suit the user's criteria. For example, generative AI comprehensively evaluates product features, specifications, price, reviews, etc., and suggests the most suitable product to the user. In addition, the search unit can always provide the latest information based on data that is updated in real time. As a result, the search unit can quickly and accurately find the products that users want.

[0032] The notification unit notifies the user via push notification when a product found by the search unit matches the specified criteria. The notification unit can notify the user through methods such as push notifications, email notifications, and SMS notifications. Specifically, in the case of push notifications, notifications are sent in real time through the smartphone app. In the case of email notifications, detailed product information is sent to the user's registered email address. In the case of SMS notifications, product information is sent in a short message, and the user can view detailed information by clicking a link. Furthermore, the notification unit can use AI to send notifications to the user at the optimal time. For example, it analyzes the user's past behavior patterns and usage times to send notifications at the most effective time. The notification content is also optimized by AI and customized to be more interesting to the user. As a result, the notification unit can provide users with product information effectively and in a timely manner.

[0033] The purchasing department purchases products notified by the notification department. For example, the purchasing department can purchase products sold on the group company's online shop with a single click. Specifically, after the user receives a notification, they simply click the purchase button, and the purchase process proceeds automatically. The purchasing department ensures a quick and smooth purchase process by pre-registering the user's payment information and shipping address information. Furthermore, the purchasing department can use AI to suggest the optimal purchase procedure. For example, the AI ​​analyzes the user's past purchase history and payment methods to suggest the most suitable payment method and shipping option. It also checks inventory status and delivery schedules in real time and provides the best option for receiving the product as quickly as possible. This allows the purchasing department to purchase the products the user wants quickly and easily. In addition, the purchasing department also provides follow-up after purchase, notifying the user of the product's delivery status and estimated arrival date. This allows the user to purchase and receive products with peace of mind.

[0034] The registration unit can register detailed product information. For example, it can register details such as the size, color, year, and brand of a product. For instance, if a user is looking for a watch from a specific year, they can register information such as the year, brand, and color. The registration unit can also use AI to supplement the information entered by the user when registering detailed product information. For example, if a user has only entered some of the product information, the AI ​​can automatically complete the remaining information. This allows for more accurate product searches as users register more detailed product information. Detailed information includes, but is not limited to, the size, color, year, and brand of a product. Some or all of the above-described processes in the registration unit may be performed using AI, for example, or not. For example, when a user registers detailed product information, the registration unit can use AI to analyze the input and supplement the information to the most appropriate level.

[0035] The search unit can search multiple websites and databases on the internet to find products that meet the specified criteria. For example, the search unit can search a specific online shop or marketplace to find products that match the criteria. For example, the search unit can search products sold in a specific online shop to find products that match the criteria. The search unit can also search multiple databases to find products that match the criteria. For example, the search unit can search the databases of multiple online shops and marketplaces to find products that match the criteria. This allows for efficient discovery of products that meet the criteria by searching multiple websites and databases on the internet. Multiple websites and databases include, but are not limited to, online shops, marketplaces, and auction sites. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or not. For example, when the search unit searches multiple websites and databases on the internet, it can use generative AI to efficiently find products.

[0036] The notification unit can notify the user via push notification if a found product matches the criteria. The notification unit notifies the user by methods such as push notification, email notification, or SMS notification. For example, the notification unit can notify the user via push notification if a found product matches the criteria. The notification unit can also notify the user via email notification or SMS notification. For example, the notification unit can notify the user via email if a found product matches the criteria. This allows the user to be quickly notified when a product matching the criteria is found. Push notifications include, but are not limited to, smartphone notification functions and web browser notification functions. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can use AI to send a notification to the user at the optimal time when a found product matches the criteria.

[0037] The purchasing department allows users to purchase products sold on the group company's online shop with a single click. The purchasing department can perform purchase procedures using methods such as one-click purchase, adding to cart, and selecting a payment method. For example, the purchasing department can purchase products sold on the group company's online shop with a single click. It can also add products to a cart, select a payment method, and proceed with the purchase. For example, the purchasing department can add products sold on the group company's online shop to a cart, select a payment method such as credit card or electronic money, and proceed with the purchase. This makes it easy to purchase products sold on the group company's online shop. One-click purchase procedures include, but are not limited to, one-click purchase, adding to cart, and selecting a payment method. Some or all of the above-described processes in the purchasing department may be performed using, for example, AI, or not. For example, when purchasing products sold on the group company's online shop, the purchasing department can use AI to suggest the optimal purchase procedure.

[0038] The registration unit can provide product recommendations by referring to the user's past purchase history. For example, the registration unit can automatically display products similar to those the user has previously purchased as candidates. The registration unit can also prioritize suggesting products from specific brands or categories based on the user's past purchase history. Furthermore, the registration unit can analyze the user's past purchase history and suggest products that are in line with the season or trends. This allows the system to provide product recommendations tailored to the user by referring to past purchase history. Past purchase history includes, but is not limited to, purchase date and time, purchased products, and purchase frequency. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's past purchase history into AI and have the AI ​​provide recommendations.

[0039] The registration unit can suggest relevant products based on the user's current interests. For example, the registration unit can suggest relevant products based on keywords the user has recently searched for or their browsing history. It can also suggest relevant products based on information about brands and influencers the user follows on social media. Furthermore, the registration unit can suggest relevant products based on information about online communities and forums the user participates in. This improves user satisfaction by suggesting relevant products based on the user's interests. Current interests include, but are not limited to, search history, browsing history, and survey results. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's current interests into AI and have the AI ​​suggest relevant products.

[0040] The registration unit can provide region-specific product information, taking into account the user's geographical location. For example, the registration unit can suggest products sold at nearby stores based on the user's current location. The registration unit can also provide region-specific sales information and benefits based on the user's geographical location. Furthermore, the registration unit can prioritize displaying products within a delivery range based on the user's geographical location. This allows for the provision of region-specific product information by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input the user's geographical location information into AI and have the AI ​​provide region-specific product information.

[0041] The registration unit can analyze a user's social media activity and suggest relevant products. For example, the registration unit can suggest relevant products based on products that the user has "liked" or shared on social media. It can also analyze posts from brands and influencers that the user follows and suggest relevant products. Furthermore, the registration unit can suggest relevant products based on information about social media groups and events the user participates in. This allows the system to suggest products relevant to the user by analyzing their social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the registration unit may be performed using AI, for example, or not. For example, the registration unit can input the user's social media activity into an AI and have the AI ​​suggest relevant products.

[0042] The search unit can monitor product inventory status and price fluctuations in real time and notify users at the optimal time. For example, the search unit can immediately notify the user if product inventory becomes low. It can also notify the user if product prices fall. Furthermore, it can notify the user if product inventory is replenished. In this way, by monitoring inventory status and price fluctuations in real time, it is possible to notify users at the optimal time. Inventory status includes, but is not limited to, the frequency of inventory updates and the method of notifying out-of-stock items. Price fluctuations include, but are not limited to, the frequency of price updates and the method of notifying price fluctuations. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the search unit can input product inventory status and price fluctuations into a generative AI and have the generative AI perform real-time monitoring and notification.

[0043] The search unit can improve the accuracy of its search by referring to the user's past search history. For example, the search unit can prioritize displaying relevant products based on keywords the user has previously searched for. It can also prioritize displaying products of specific brands or categories based on the user's past search history. Furthermore, the search unit can analyze the user's past search history and suggest the most relevant products. In this way, the accuracy of the search can be improved by referring to past search history. Past search history includes, but is not limited to, search keywords, search date and time, and search result click history. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the search unit can input the user's past search history into a generative AI and have the generative AI perform the search accuracy improvement.

[0044] The search unit can prioritize searching for region-specific products by taking into account the user's geographical location information. For example, the search unit can prioritize displaying products sold at nearby stores based on the user's current location. The search unit can also provide region-specific sales information and benefits based on the user's geographical location information. Furthermore, the search unit can prioritize displaying products within a delivery range based on the user's geographical location information. In this way, by taking into account the user's geographical location information, region-specific products can be prioritized in the search. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's geographical location information into a generative AI and have the generative AI perform a search for region-specific products.

[0045] The search unit can improve the reliability of its search results by referring to reviews and ratings of related products. For example, the search unit may prioritize displaying highly-rated products. It can also suggest highly reliable products based on user reviews and ratings. Furthermore, the search unit can analyze product reviews and ratings to suggest the most relevant products. This improves the reliability of search results by referring to product reviews and ratings. Reviews and ratings include, but are not limited to, review reliability ratings and methods for calculating rating scores. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the search unit can input product reviews and ratings into a generative AI and have the generative AI generate highly reliable product suggestions.

[0046] The notification unit can select the optimal notification method by referring to the user's past response history. For example, the notification unit may prioritize notification methods that the user has previously preferred (email, push notifications, etc.). The notification unit can also select the most effective notification method based on the user's past response history. Furthermore, the notification unit can analyze the user's past response history and select the optimal notification timing. In this way, the notification unit can select the most suitable notification method for the user by referring to past response history. Past response history includes, but is not limited to, response time, response content, and response frequency to notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past response history into AI and have the AI ​​select the optimal notification method.

[0047] The notification unit can adjust the priority of notifications based on the importance of the products. For example, it may prioritize notifications for limited-edition items or items with low stock. It can also prioritize notifications for items that the user is particularly interested in. Furthermore, the notification unit can adjust the frequency and timing of notifications based on the importance of the products. This allows important products to be prioritized by adjusting the notification priority based on product importance. Product importance includes, but is not limited to, the user's level of interest, product rarity, and price. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input product importance into AI and have the AI ​​adjust the notification priority.

[0048] The notification unit can select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the notification unit will prioritize push notifications. Furthermore, if the user is using a tablet, the notification unit can select a notification method optimized for a larger screen. Additionally, if the user is using a smartwatch, the notification unit can select a concise and highly visible notification method. This allows the system to provide the optimal notification method by considering the user's device information. Device information includes, but is not limited to, the device type, OS version, and applications used. Some or all of the processing described above in the notification unit may be performed using AI, or not. For example, the notification unit can input the user's device information into an AI and have the AI ​​select the optimal notification method.

[0049] The notification unit can analyze a user's social media activity and notify them of relevant information. For example, the notification unit can notify users of relevant information based on products that the user has "liked" or shared on social media. The notification unit can also analyze posts from brands and influencers that the user follows and notify them of relevant information. Furthermore, the notification unit can notify users of relevant information based on information about social media groups and events that the user participates in. In this way, by analyzing social media activity, the notification unit can notify users of information relevant to them. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the user's social media activity into AI and have the AI ​​notify the user of relevant information.

[0050] The purchasing department can suggest optimal purchase options by referring to the user's past purchase history at the time of purchase. For example, the purchasing department can suggest products similar to those the user has purchased in the past. The purchasing department can also prioritize suggesting products from specific brands or categories based on the user's past purchase history. Furthermore, the purchasing department can analyze the user's past purchase history and suggest products that are in line with the season or trends. In this way, by referring to past purchase history, the purchasing department can suggest the most suitable purchase options to the user. Past purchase history includes, but is not limited to, purchase date and time, purchased products, and purchase frequency. 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 past purchase history into AI and have the AI ​​suggest optimal purchase options.

[0051] The purchasing department can suggest the optimal payment method at the time of purchase, taking into account the user's current payment methods and points information. For example, the purchasing department may prioritize suggesting payment methods the user has used in the past. It can also suggest the optimal payment method based on the user's current points information. Furthermore, it can suggest the most convenient payment option based on the user's current payment methods. In this way, by considering the current payment methods and points information, the purchasing department can suggest the optimal payment method for the user. Payment methods include, but are not limited to, credit cards, debit cards, electronic money, and points. 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 payment methods and points information into AI and have the AI ​​suggest the optimal payment method.

[0052] The purchasing department can offer region-specific offers at the time of purchase, taking into account the user's geographical location. For example, the purchasing department can suggest offers available at nearby stores based on the user's current location. It can also provide region-specific sales information and benefits based on the user's geographical location. Furthermore, the purchasing department can prioritize displaying offers within a delivery range based on the user's geographical location. In this way, by considering geographical location, it is possible to provide users with region-specific offers. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the above processing in the purchasing department may be performed using, for example, AI, or not using AI. For example, the purchasing department can input the user's geographical location information into AI and have the AI ​​perform the task of providing region-specific offers.

[0053] The purchasing department can analyze a user's social media activity at the time of purchase and suggest relevant products. For example, the purchasing department can suggest relevant products based on products the user has "liked" or shared on social media. It can also analyze posts from brands and influencers the user follows and suggest relevant products. Furthermore, the purchasing department can suggest relevant products based on information about social media groups and events the user participates in. In this way, by analyzing social media activity, it is possible to suggest products relevant to the user. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the purchasing department may be performed using AI, for example, or not. For example, the purchasing department can input the user's social media activity into AI and have the AI ​​suggest relevant products.

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

[0055] Product search systems can analyze a user's purchase history and predict future purchasing behavior. For example, if a user has frequently purchased clothing from a particular brand in the past, the system can send a notification when new products from that brand are released. Furthermore, if a user tends to purchase specific items during certain seasons, the system can suggest related products as that season approaches. Additionally, if a user tends to purchase items related to specific events (e.g., birthdays or anniversaries), the system can suggest related products as those events approach. This allows for more personalized product recommendations by leveraging the user's purchase history.

[0056] The product search system can analyze users' social media activity and provide trend-based product recommendations. For example, it can suggest products featured by influencers the user follows. It can also suggest products that are trending in social media groups the user belongs to. Furthermore, it can suggest products related to posts the user has liked or shared. This allows the system to leverage social media activity to suggest products that are interesting to the user.

[0057] Product search systems can leverage users' geographical location information to provide region-specific event and sale information. For example, if a user is in a specific region, they can be notified about events and sales being held in that area. Similarly, if a user is near a specific store, they can be offered special discounts and promotions at that store. Furthermore, if a user is traveling, they can be offered region-specific products and tourist information for their destination. In this way, geographical location information can be used to provide users with useful information.

[0058] The product search system can suggest related product bundles based on the user's purchase history. For example, if a user has previously purchased skincare products from a specific brand, the system can suggest a new skincare set from that brand. Similarly, if a user frequently purchases products from a particular category, the system can suggest bundles that offer discounts for purchasing multiple items from that category. Furthermore, if a user tends to purchase seasonally relevant products, the system can suggest bundles tailored to that season. This allows for more advantageous product suggestions by leveraging the user's purchase history.

[0059] A product search system can suggest relevant products based on a user's current interests and preferences. For example, it can suggest relevant products based on keywords the user has recently searched for and their browsing history. It can also suggest relevant products based on brands and influencers the user follows on social media. Furthermore, it can suggest relevant products based on information from online communities and forums the user participates in. By suggesting relevant products based on the user's interests and preferences, this system can improve user satisfaction.

[0060] The product search system can provide region-specific product information by considering the user's geographical location. For example, it can suggest products sold at nearby stores based on the user's current location. It can also provide region-specific sales information and special offers based on the user's geographical location. Furthermore, it can prioritize displaying products within a delivery range based on the user's geographical location. In this way, by considering the user's geographical location, it can provide region-specific product information.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The registration unit registers the items the user wants to a list. These items may include, but are not limited to, electronic devices, clothing, and food. The registration unit can register items to the list using methods such as manual input, barcode scanning, or voice input. Some or all of the processing in the registration unit may be performed using, for example, AI, or not. For example, when the registration unit registers items the user wants to a list, it can use AI to automatically complete the details of the items. Step 2: The search unit continues to search for products based on the information registered by the registration unit. The search unit searches multiple sites and databases on the internet, for example, to find products that meet the criteria. For example, the search unit can search a specific online shop or marketplace and find products that match the criteria. Some or all of the processing in the search unit may be performed using, for example, generative AI, or not. For example, when searching multiple sites and databases on the internet, the search unit can use generative AI to efficiently find products. Step 3: The notification unit notifies the user via push notification if the product found by the search unit matches the criteria. The notification unit can notify the user by methods such as push notification, email notification, or SMS notification. Some or all of the processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can use AI to send a notification to the user at the optimal time when the found product matches the criteria. Step 4: The purchasing unit purchases the products notified by the notification unit. The purchasing unit can, for example, purchase products sold on the group company's online shop with a single click. Some or all of the processing in the purchasing unit may be performed using AI, or not. For example, when purchasing products sold on the group company's online shop, the purchasing unit can use AI to suggest the optimal purchase procedure.

[0063] (Example of form 2) The product search system according to an embodiment of the present invention is a system that uses a generating AI to efficiently find products that users want and provides them with purchase opportunities. The product search system allows users to register desired products in a list, and the generating AI continues to search for products based on the registered information. When a product is found that matches the criteria, the user is notified by push notification. Furthermore, if the product is from a group company, the purchase process can be completed with a single button click. This mechanism allows users to save time searching and purchase products at a discount. In addition, the company can acquire loyal customers and expect stable revenue. First, the user registers the products they want in a list. At this time, detailed product information (size, color, year, etc.) is also registered. For example, if a user is looking for a watch from a specific year, they register information such as the year, brand, and color. This information is input into the generating AI. Next, the generating AI analyzes the input information and continues to search for products that match the registered criteria. The generating AI searches multiple sites and databases on the internet to find products that meet the criteria. For example, if the watch the user is looking for is sold at a specific online shop, the generating AI retrieves that information. When a product is found that matches the criteria, the generating AI notifies the user by push notification. For example, if the user finds the watch they are looking for, the generating AI will send a notification such as, "We have found the watch you are looking for." This allows the user to quickly complete the purchase process without missing out on the product. Furthermore, if it is a group company, the purchase process can be completed with a single click. For example, if the product the user is looking for is sold at a group company's online shop, the generating AI will provide that information, and the user can complete the purchase process with a single click. This allows the user to purchase the product without any hassle. This system saves users the trouble of searching and allows them to purchase products at a discount. For example, even when searching for rare items such as limited editions or old products, the generating AI constantly keeps its antenna up and continues searching, so the user can find the product without any effort. In addition, the company can acquire a loyal customer base and expect stable revenue.For example, by offering opportunities to purchase everyday necessities at a discount, users can be expected to continue using the company's services. This allows the product search system to efficiently find the products users want and provide them with purchase opportunities.

[0064] The product search system according to this embodiment comprises a registration unit, a search unit, a notification unit, and a purchase unit. The registration unit registers products that the user wants in a list. Products that the user wants include, but are not limited to, electronic devices, clothing, and food. The registration unit can register products in the list by methods such as manual input, barcode scanning, and voice input. The search unit continues to search for products based on the information registered by the registration unit. The search unit searches multiple sites and databases on the internet, for example, to find products that meet the criteria. For example, the search unit can search a specific online shop or marketplace and find products that match the criteria. The notification unit notifies the user by push notification if a product found by the search unit matches the criteria. The notification unit can notify the user by methods such as push notification, email notification, and SMS notification. The purchase unit purchases the product notified by the notification unit. The purchase unit can, for example, purchase products sold at the online shop of a group company with a single button click. As a result, the product search system according to this embodiment can efficiently find products that the user wants and provide them with a purchase opportunity. Some or all of the above-described processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can use AI to automatically complete the detailed information of products when a user registers desired products to a list. Some or all of the above-described processes in the search unit may be performed using generative AI, for example, or without generative AI. For example, the search unit can use generative AI to efficiently find products when searching multiple sites or databases on the internet. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use AI to send a notification to the user at the optimal time when a found product matches the criteria. Some or all of the above-described processes in the purchase unit may be performed using AI, for example, or without AI.For example, the purchasing department can use AI to suggest the optimal purchase procedure when purchasing products sold on the group company's online shop. This allows the product search system according to this embodiment to efficiently find the products the user wants and provide them with purchase opportunities.

[0065] The registration unit registers items that the user wants to buy. These items may include, but are not limited to, electronic devices, clothing, and food. The registration unit can register items using methods such as manual input, barcode scanning, and voice input. Specifically, for manual input, the user can enter detailed information such as product name, model number, and desired price. For barcode scanning, the smartphone camera is used to scan the product's barcode, automatically retrieving product information. For voice input, the user voice-instructs the desired product, which is then converted to text using speech recognition technology and registered in the list. Furthermore, the registration unit can use AI to supplement the information entered by the user. For example, if the user enters "latest smartphone," the AI ​​will generate a list of the latest smartphones on the market and suggest them to the user. It can also analyze the user's past purchase and search history and automatically add items that the user might be interested in to the list. This allows the registration unit to efficiently and accurately register items that the user wants to buy.

[0066] The search unit continuously searches for products based on information registered by the registration unit. For example, the search unit searches multiple websites and databases on the internet to find products that meet the criteria. Specifically, the search unit periodically scans specific online shops and marketplaces to find products that match the registered product criteria. For example, it can use price comparison sites and review sites to find the best deals and highly-rated products. Furthermore, the search unit can efficiently find products using generative AI. Generative AI uses natural language processing technology to analyze vast amounts of data on the internet and identify products that best suit the user's criteria. For example, generative AI comprehensively evaluates product features, specifications, price, reviews, etc., and suggests the most suitable product to the user. In addition, the search unit can always provide the latest information based on data that is updated in real time. As a result, the search unit can quickly and accurately find the products that users want.

[0067] The notification unit notifies the user via push notification when a product found by the search unit matches the specified criteria. The notification unit can notify the user through methods such as push notifications, email notifications, and SMS notifications. Specifically, in the case of push notifications, notifications are sent in real time through the smartphone app. In the case of email notifications, detailed product information is sent to the user's registered email address. In the case of SMS notifications, product information is sent in a short message, and the user can view detailed information by clicking a link. Furthermore, the notification unit can use AI to send notifications to the user at the optimal time. For example, it analyzes the user's past behavior patterns and usage times to send notifications at the most effective time. The notification content is also optimized by AI and customized to be more interesting to the user. As a result, the notification unit can provide users with product information effectively and in a timely manner.

[0068] The purchasing department purchases products notified by the notification department. For example, the purchasing department can purchase products sold on the group company's online shop with a single click. Specifically, after the user receives a notification, they simply click the purchase button, and the purchase process proceeds automatically. The purchasing department ensures a quick and smooth purchase process by pre-registering the user's payment information and shipping address information. Furthermore, the purchasing department can use AI to suggest the optimal purchase procedure. For example, the AI ​​analyzes the user's past purchase history and payment methods to suggest the most suitable payment method and shipping option. It also checks inventory status and delivery schedules in real time and provides the best option for receiving the product as quickly as possible. This allows the purchasing department to purchase the products the user wants quickly and easily. In addition, the purchasing department also provides follow-up after purchase, notifying the user of the product's delivery status and estimated arrival date. This allows the user to purchase and receive products with peace of mind.

[0069] The registration unit can register detailed product information. For example, it can register details such as the size, color, year, and brand of a product. For instance, if a user is looking for a watch from a specific year, they can register information such as the year, brand, and color. The registration unit can also use AI to supplement the information entered by the user when registering detailed product information. For example, if a user has only entered some of the product information, the AI ​​can automatically complete the remaining information. This allows for more accurate product searches as users register more detailed product information. Detailed information includes, but is not limited to, the size, color, year, and brand of a product. Some or all of the above-described processes in the registration unit may be performed using AI, for example, or not. For example, when a user registers detailed product information, the registration unit can use AI to analyze the input and supplement the information to the most appropriate level.

[0070] The search unit can search multiple websites and databases on the internet to find products that meet the specified criteria. For example, the search unit can search a specific online shop or marketplace to find products that match the criteria. For example, the search unit can search products sold in a specific online shop to find products that match the criteria. The search unit can also search multiple databases to find products that match the criteria. For example, the search unit can search the databases of multiple online shops and marketplaces to find products that match the criteria. This allows for efficient discovery of products that meet the criteria by searching multiple websites and databases on the internet. Multiple websites and databases include, but are not limited to, online shops, marketplaces, and auction sites. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or not. For example, when the search unit searches multiple websites and databases on the internet, it can use generative AI to efficiently find products.

[0071] The notification unit can notify the user via push notification if a found product matches the criteria. The notification unit notifies the user by methods such as push notification, email notification, or SMS notification. For example, the notification unit can notify the user via push notification if a found product matches the criteria. The notification unit can also notify the user via email notification or SMS notification. For example, the notification unit can notify the user via email if a found product matches the criteria. This allows the user to be quickly notified when a product matching the criteria is found. Push notifications include, but are not limited to, smartphone notification functions and web browser notification functions. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can use AI to send a notification to the user at the optimal time when a found product matches the criteria.

[0072] The purchasing department allows users to purchase products sold on the group company's online shop with a single click. The purchasing department can perform purchase procedures using methods such as one-click purchase, adding to cart, and selecting a payment method. For example, the purchasing department can purchase products sold on the group company's online shop with a single click. It can also add products to a cart, select a payment method, and proceed with the purchase. For example, the purchasing department can add products sold on the group company's online shop to a cart, select a payment method such as credit card or electronic money, and proceed with the purchase. This makes it easy to purchase products sold on the group company's online shop. One-click purchase procedures include, but are not limited to, one-click purchase, adding to cart, and selecting a payment method. Some or all of the above-described processes in the purchasing department may be performed using, for example, AI, or not. For example, when purchasing products sold on the group company's online shop, the purchasing department can use AI to suggest the optimal purchase procedure.

[0073] The registration unit can estimate the user's emotions and adjust the input method for product details based on the estimated emotions. For example, if the user is stressed, the registration unit can provide a simple interface and minimize the input steps. If the user is relaxed, the registration unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the registration unit can prioritize voice input to allow for quick input of product details. This reduces the user's burden by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input user emotion data into a generative AI and have the generative AI adjust the input method based on the emotion.

[0074] The registration unit can provide product recommendations by referring to the user's past purchase history. For example, the registration unit can automatically display products similar to those the user has previously purchased as candidates. The registration unit can also prioritize suggesting products from specific brands or categories based on the user's past purchase history. Furthermore, the registration unit can analyze the user's past purchase history and suggest products that are in line with the season or trends. This allows the system to provide product recommendations tailored to the user by referring to past purchase history. Past purchase history includes, but is not limited to, purchase date and time, purchased products, and purchase frequency. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's past purchase history into AI and have the AI ​​provide recommendations.

[0075] The registration unit can suggest relevant products based on the user's current interests. For example, the registration unit can suggest relevant products based on keywords the user has recently searched for or their browsing history. It can also suggest relevant products based on information about brands and influencers the user follows on social media. Furthermore, the registration unit can suggest relevant products based on information about online communities and forums the user participates in. This improves user satisfaction by suggesting relevant products based on the user's interests. Current interests include, but are not limited to, search history, browsing history, and survey results. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's current interests into AI and have the AI ​​suggest relevant products.

[0076] The registration unit can estimate the user's emotions and determine the priority of products to register based on the estimated emotions. For example, if the user is excited, the registration unit can prioritize displaying products that are immediately available for purchase. If the user is relaxed, the registration unit can also prioritize displaying products that provide detailed information and allow for comparison. Furthermore, if the user is stressed, the registration unit can provide a simple and intuitive interface and determine priorities accordingly. This allows for product suggestions tailored to the user's needs by prioritizing products according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input user emotion data into a generative AI and have the generative AI perform the emotion-based product prioritization.

[0077] The registration unit can provide region-specific product information, taking into account the user's geographical location. For example, the registration unit can suggest products sold at nearby stores based on the user's current location. The registration unit can also provide region-specific sales information and benefits based on the user's geographical location. Furthermore, the registration unit can prioritize displaying products within a delivery range based on the user's geographical location. This allows for the provision of region-specific product information by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the above processing in the registration unit may be performed using, for example, AI, or not using AI. For example, the registration unit can input the user's geographical location information into AI and have the AI ​​provide region-specific product information.

[0078] The registration unit can analyze a user's social media activity and suggest relevant products. For example, the registration unit can suggest relevant products based on products that the user has "liked" or shared on social media. It can also analyze posts from brands and influencers that the user follows and suggest relevant products. Furthermore, the registration unit can suggest relevant products based on information about social media groups and events the user participates in. This allows the system to suggest products relevant to the user by analyzing their social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the registration unit may be performed using AI, for example, or not. For example, the registration unit can input the user's social media activity into an AI and have the AI ​​suggest relevant products.

[0079] The search unit can estimate the user's emotions and adjust the search algorithm based on the estimated emotions. For example, if the user is relaxed, the search unit can provide search results that include detailed information. If the user is in a hurry, the search unit can also narrow the search range to display results quickly. Furthermore, if the user is excited, the search unit can prioritize displaying visually appealing products. In this way, by adjusting the search algorithm according to the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using a generative AI, or not using a generative AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the search algorithm.

[0080] The search unit can monitor product inventory status and price fluctuations in real time and notify users at the optimal time. For example, the search unit can immediately notify the user if product inventory becomes low. It can also notify the user if product prices fall. Furthermore, it can notify the user if product inventory is replenished. In this way, by monitoring inventory status and price fluctuations in real time, it is possible to notify users at the optimal time. Inventory status includes, but is not limited to, the frequency of inventory updates and the method of notifying out-of-stock items. Price fluctuations include, but are not limited to, the frequency of price updates and the method of notifying price fluctuations. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the search unit can input product inventory status and price fluctuations into a generative AI and have the generative AI perform real-time monitoring and notification.

[0081] The search unit can improve the accuracy of its search by referring to the user's past search history. For example, the search unit can prioritize displaying relevant products based on keywords the user has previously searched for. It can also prioritize displaying products of specific brands or categories based on the user's past search history. Furthermore, the search unit can analyze the user's past search history and suggest the most relevant products. In this way, the accuracy of the search can be improved by referring to past search history. Past search history includes, but is not limited to, search keywords, search date and time, and search result click history. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the search unit can input the user's past search history into a generative AI and have the generative AI perform the search accuracy improvement.

[0082] The search unit can estimate the user's emotions and adjust the display method of the search results based on the estimated user emotions. For example, if the user is nervous, the search unit can provide a simple and highly visible display method. If the user is relaxed, the search unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the search unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the search results according to the user's emotions, a display method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the search unit may be performed using a generative AI, or not using a generative AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method based on the emotion.

[0083] The search unit can prioritize searching for region-specific products by taking into account the user's geographical location information. For example, the search unit can prioritize displaying products sold at nearby stores based on the user's current location. The search unit can also provide region-specific sales information and benefits based on the user's geographical location information. Furthermore, the search unit can prioritize displaying products within a delivery range based on the user's geographical location information. In this way, by taking into account the user's geographical location information, region-specific products can be prioritized in the search. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's geographical location information into a generative AI and have the generative AI perform a search for region-specific products.

[0084] The search unit can improve the reliability of its search results by referring to reviews and ratings of related products. For example, the search unit may prioritize displaying highly-rated products. It can also suggest highly reliable products based on user reviews and ratings. Furthermore, the search unit can analyze product reviews and ratings to suggest the most relevant products. This improves the reliability of search results by referring to product reviews and ratings. Reviews and ratings include, but are not limited to, review reliability ratings and methods for calculating rating scores. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the search unit can input product reviews and ratings into a generative AI and have the generative AI generate highly reliable product suggestions.

[0085] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is relaxed, the notification unit can send a notification immediately. It can also postpone notifications if the user is busy. Furthermore, if the user is excited, the notification unit can send a notification immediately. This allows notifications to be delivered at the optimal time for the user by adjusting the timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the notification timing based on the emotion.

[0086] The notification unit can select the optimal notification method by referring to the user's past response history. For example, the notification unit may prioritize notification methods that the user has previously preferred (email, push notifications, etc.). The notification unit can also select the most effective notification method based on the user's past response history. Furthermore, the notification unit can analyze the user's past response history and select the optimal notification timing. In this way, the notification unit can select the most suitable notification method for the user by referring to past response history. Past response history includes, but is not limited to, response time, response content, and response frequency to notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past response history into AI and have the AI ​​select the optimal notification method.

[0087] The notification unit can adjust the priority of notifications based on the importance of the products. For example, it may prioritize notifications for limited-edition items or items with low stock. It can also prioritize notifications for items that the user is particularly interested in. Furthermore, the notification unit can adjust the frequency and timing of notifications based on the importance of the products. This allows important products to be prioritized by adjusting the notification priority based on product importance. Product importance includes, but is not limited to, the user's level of interest, product rarity, and price. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input product importance into AI and have the AI ​​adjust the notification priority.

[0088] The notification unit can estimate the user's emotions and customize the notification content based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and highly visible notification. If the user is relaxed, the notification unit can also provide a notification with more detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a concise notification. By customizing the notification content according to the user's emotions, the system can provide the most suitable notification for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI customize the notification content based on the emotion.

[0089] The notification unit can select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the notification unit will prioritize push notifications. Furthermore, if the user is using a tablet, the notification unit can select a notification method optimized for a larger screen. Additionally, if the user is using a smartwatch, the notification unit can select a concise and highly visible notification method. This allows the system to provide the optimal notification method by considering the user's device information. Device information includes, but is not limited to, the device type, OS version, and applications used. Some or all of the processing described above in the notification unit may be performed using AI, or not. For example, the notification unit can input the user's device information into an AI and have the AI ​​select the optimal notification method.

[0090] The notification unit can analyze a user's social media activity and notify them of relevant information. For example, the notification unit can notify users of relevant information based on products that the user has "liked" or shared on social media. The notification unit can also analyze posts from brands and influencers that the user follows and notify them of relevant information. Furthermore, the notification unit can notify users of relevant information based on information about social media groups and events that the user participates in. In this way, by analyzing social media activity, the notification unit can notify users of information relevant to them. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the user's social media activity into AI and have the AI ​​notify the user of relevant information.

[0091] The purchasing unit can estimate the user's emotions and adjust the purchase process based on those emotions. For example, if the user is relaxed, the purchasing unit can provide a purchase process that includes detailed explanations. If the user is in a hurry, the purchasing unit can also enable them to complete the purchase process quickly. Furthermore, if the user is excited, the purchasing unit can provide a visually appealing interface. By adjusting the purchase process according to the user's emotions, the system can provide the user with the best possible purchase experience. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the purchasing unit may be performed using AI or not. For example, the purchasing unit can input user emotion data into a generative AI and have the generative AI adjust the purchase process based on those emotions.

[0092] The purchasing department can suggest optimal purchase options by referring to the user's past purchase history at the time of purchase. For example, the purchasing department can suggest products similar to those the user has purchased in the past. The purchasing department can also prioritize suggesting products from specific brands or categories based on the user's past purchase history. Furthermore, the purchasing department can analyze the user's past purchase history and suggest products that are in line with the season or trends. In this way, by referring to past purchase history, the purchasing department can suggest the most suitable purchase options to the user. Past purchase history includes, but is not limited to, purchase date and time, purchased products, and purchase frequency. 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 past purchase history into AI and have the AI ​​suggest optimal purchase options.

[0093] The purchasing department can suggest the optimal payment method at the time of purchase, taking into account the user's current payment methods and points information. For example, the purchasing department may prioritize suggesting payment methods the user has used in the past. It can also suggest the optimal payment method based on the user's current points information. Furthermore, it can suggest the most convenient payment option based on the user's current payment methods. In this way, by considering the current payment methods and points information, the purchasing department can suggest the optimal payment method for the user. Payment methods include, but are not limited to, credit cards, debit cards, electronic money, and points. 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 payment methods and points information into AI and have the AI ​​suggest the optimal payment method.

[0094] The purchasing unit can estimate the user's emotions and determine purchase priorities based on those emotions. For example, if the user is excited, the purchasing unit can prioritize displaying products that are immediately available for purchase. If the user is relaxed, the purchasing unit can also prioritize displaying products that offer detailed information and allow for comparison. Furthermore, if the user is stressed, the purchasing unit can provide a simple and intuitive interface and determine priorities accordingly. This allows for product suggestions tailored to the user's needs by determining purchase priorities according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the purchasing unit may be performed using AI or not. For example, the purchasing unit can input user emotion data into a generative AI and have the generative AI perform emotion-based purchase priority determination.

[0095] The purchasing department can offer region-specific offers at the time of purchase, taking into account the user's geographical location. For example, the purchasing department can suggest offers available at nearby stores based on the user's current location. It can also provide region-specific sales information and benefits based on the user's geographical location. Furthermore, the purchasing department can prioritize displaying offers within a delivery range based on the user's geographical location. In this way, by considering geographical location, it is possible to provide users with region-specific offers. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the above processing in the purchasing department may be performed using, for example, AI, or not using AI. For example, the purchasing department can input the user's geographical location information into AI and have the AI ​​perform the task of providing region-specific offers.

[0096] The purchasing department can analyze a user's social media activity at the time of purchase and suggest relevant products. For example, the purchasing department can suggest relevant products based on products the user has "liked" or shared on social media. It can also analyze posts from brands and influencers the user follows and suggest relevant products. Furthermore, the purchasing department can suggest relevant products based on information about social media groups and events the user participates in. In this way, by analyzing social media activity, it is possible to suggest products relevant to the user. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the purchasing department may be performed using AI, for example, or not. For example, the purchasing department can input the user's social media activity into AI and have the AI ​​suggest relevant products.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] Product search systems can analyze a user's purchase history and predict future purchasing behavior. For example, if a user has frequently purchased clothing from a particular brand in the past, the system can send a notification when new products from that brand are released. Furthermore, if a user tends to purchase specific items during certain seasons, the system can suggest related products as that season approaches. Additionally, if a user tends to purchase items related to specific events (e.g., birthdays or anniversaries), the system can suggest related products as those events approach. This allows for more personalized product recommendations by leveraging the user's purchase history.

[0099] The product search system can estimate the user's emotions and adjust its product recommendations based on those emotions. For example, if the user is stressed, it can suggest products with relaxing effects (such as aromatherapy candles or massage devices). If the user is excited, it can suggest entertainment-related products (such as movie tickets or games). Furthermore, if the user is sad, it can suggest products accompanied by positive messages to lift their spirits (such as encouraging message cards or uplifting music). This enables product recommendations tailored to the user's emotions, thereby improving user satisfaction.

[0100] The product search system can analyze users' social media activity and provide trend-based product recommendations. For example, it can suggest products featured by influencers the user follows. It can also suggest products that are trending in social media groups the user belongs to. Furthermore, it can suggest products related to posts the user has liked or shared. This allows the system to leverage social media activity to suggest products that are interesting to the user.

[0101] Product search systems can leverage users' geographical location information to provide region-specific event and sale information. For example, if a user is in a specific region, they can be notified about events and sales being held in that area. Similarly, if a user is near a specific store, they can be offered special discounts and promotions at that store. Furthermore, if a user is traveling, they can be offered region-specific products and tourist information for their destination. In this way, geographical location information can be used to provide users with useful information.

[0102] The product search system can estimate the user's emotions and customize notification content based on those emotions. For example, if the user is relaxed, it can send a notification containing detailed product information. If the user is in a hurry, it can send a concise and to-the-point notification. Furthermore, if the user is excited, it can send a visually appealing notification. By providing notifications tailored to the user's emotions, the system can deliver the most relevant information to the user.

[0103] The product search system can suggest related product bundles based on the user's purchase history. For example, if a user has previously purchased skincare products from a specific brand, the system can suggest a new skincare set from that brand. Similarly, if a user frequently purchases products from a particular category, the system can suggest bundles that offer discounts for purchasing multiple items from that category. Furthermore, if a user tends to purchase seasonally relevant products, the system can suggest bundles tailored to that season. This allows for more advantageous product suggestions by leveraging the user's purchase history.

[0104] The product search system can estimate the user's emotions and display product reviews and ratings based on those emotions. For example, if the user is relaxed, detailed reviews and ratings can be displayed. If the user is in a hurry, concise reviews and ratings can be displayed. Furthermore, if the user is excited, positive reviews and highly-rated products can be prioritized. This enables the display of reviews and ratings tailored to the user's emotions, supporting their purchasing decisions.

[0105] A product search system can suggest relevant products based on a user's current interests and preferences. For example, it can suggest relevant products based on keywords the user has recently searched for and their browsing history. It can also suggest relevant products based on brands and influencers the user follows on social media. Furthermore, it can suggest relevant products based on information from online communities and forums the user participates in. By suggesting relevant products based on the user's interests and preferences, this system can improve user satisfaction.

[0106] The product search system can estimate the user's emotions and prioritize products based on those emotions. For example, if the user is excited, it can prioritize displaying products that are immediately available for purchase. If the user is relaxed, it can prioritize displaying products that offer detailed information and allow for comparison. Furthermore, if the user is stressed, it can provide a simple and intuitive interface and prioritize accordingly. By prioritizing products according to the user's emotions, it becomes possible to suggest products that meet the user's needs.

[0107] The product search system can provide region-specific product information by considering the user's geographical location. For example, it can suggest products sold at nearby stores based on the user's current location. It can also provide region-specific sales information and special offers based on the user's geographical location. Furthermore, it can prioritize displaying products within a delivery range based on the user's geographical location. In this way, by considering the user's geographical location, it can provide region-specific product information.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The registration unit registers the items the user wants to a list. These items may include, but are not limited to, electronic devices, clothing, and food. The registration unit can register items to the list using methods such as manual input, barcode scanning, or voice input. Some or all of the processing in the registration unit may be performed using, for example, AI, or not. For example, when the registration unit registers items the user wants to a list, it can use AI to automatically complete the details of the items. Step 2: The search unit continues to search for products based on the information registered by the registration unit. The search unit searches multiple sites and databases on the internet, for example, to find products that meet the criteria. For example, the search unit can search a specific online shop or marketplace and find products that match the criteria. Some or all of the processing in the search unit may be performed using, for example, generative AI, or not. For example, when searching multiple sites and databases on the internet, the search unit can use generative AI to efficiently find products. Step 3: The notification unit notifies the user via push notification if the product found by the search unit matches the criteria. The notification unit can notify the user by methods such as push notification, email notification, or SMS notification. Some or all of the processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can use AI to send a notification to the user at the optimal time when the found product matches the criteria. Step 4: The purchasing unit purchases the products notified by the notification unit. The purchasing unit can, for example, purchase products sold on the group company's online shop with a single click. Some or all of the processing in the purchasing unit may be performed using AI, or not. For example, when purchasing products sold on the group company's online shop, the purchasing unit can use AI to suggest the optimal purchase procedure.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the registration unit, search unit, notification unit, and purchase unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14, which registers the products the user wants in a list. The search unit is implemented by the identification processing unit 290 of the data processing unit 12, which continues to search for products based on the registered information. The notification unit is implemented by the control unit 46A of the smart device 14, which notifies the user by push notification when a found product matches the criteria. The purchase unit is implemented by the identification processing unit 290 of the data processing unit 12, which allows the user to purchase products sold on the group company's online shop with a single button click. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the registration unit, search unit, notification unit, and purchase unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214, which registers the products the user wants in a list. The search unit is implemented by the identification processing unit 290 of the data processing unit 12, which continues to search for products based on the registered information. The notification unit is implemented by the control unit 46A of the smart glasses 214, which notifies the user by push notification when a found product matches the criteria. The purchase unit is implemented by the identification processing unit 290 of the data processing unit 12, which allows the user to purchase products sold on the group company's online shop with a single button click. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the registration unit, search unit, notification unit, and purchase unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314, which registers the products the user wants in a list. The search unit is implemented by the identification processing unit 290 of the data processing unit 12, which continues to search for products based on the registered information. The notification unit is implemented by the control unit 46A of the headset terminal 314, which notifies the user by push notification when a found product matches the criteria. The purchase unit is implemented by the identification processing unit 290 of the data processing unit 12, which allows the user to purchase products sold on the group company's online shop with a single button click. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the registration unit, search unit, notification unit, and purchase unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414 and registers the products the user wants in a list. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and continues to search for products based on the registered information. The notification unit is implemented by, for example, the control unit 46A of the robot 414 and notifies the user by push notification when a found product matches the criteria. The purchase unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and allows the user to purchase products sold on the group company's online shop with a single button click. 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A registration section where users register the products they want in a list, A search unit that continues to search for products based on the information registered by the registration unit, A notification unit that notifies the user of the products found by the search unit, A purchase unit that purchases the goods notified by the notification unit. A system characterized by the following features. (Note 2) The aforementioned registration unit is Register product details The system described in Appendix 1, characterized by the features described herein. (Note 3) The search unit, Search multiple websites and databases on the internet to find products that meet the criteria. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, If a product is found that matches the criteria, the user will be notified via push notification. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned purchasing department, Purchase products sold on our group company's online shop with just one click. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned registration unit is The system estimates the user's emotions and adjusts how product details are entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is We provide recommended product information based on the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is Based on the user's current interests, we suggest relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is The system estimates the user's emotions and determines the priority of products to register based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is Provide region-specific product information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is We analyze users' social media activity and suggest relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 12) The search unit, It estimates the user's emotions and adjusts the search algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The search unit, We monitor product inventory and price fluctuations in real time and notify you at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 14) The search unit, We improve search accuracy by referring to the user's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The search unit, It estimates the user's emotions and adjusts how the search results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The search unit, The system prioritizes searching for region-specific products, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The search unit, Refer to reviews and ratings of related products to improve the reliability of your search results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, The system selects the optimal notification method by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, Prioritize notifications based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, It estimates the user's emotions and customizes the content of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, The optimal notification method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, Analyze users' social media activity and notify them of relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned purchasing department, It estimates the user's emotions and adjusts the purchase process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned purchasing department, When a user makes a purchase, the system refers to their past purchase history to suggest the most suitable purchase options. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned purchasing department, When making a purchase, we consider the user's current payment methods and points information to suggest the most suitable payment method. 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, we take the user's geographical location into consideration and offer region-specific offers. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned purchasing department, At the time of purchase, the system analyzes the user's social media activity and suggests relevant products. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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 registration section where users register the products they want in a list, A search unit that continues to search for products based on the information registered by the registration unit, A notification unit that notifies the user of the products found by the search unit, A purchase unit that purchases the goods notified by the notification unit. A system characterized by the following features.

2. The aforementioned registration unit is Register product details The system according to feature 1.

3. The search unit, Search multiple websites and databases on the internet to find products that meet the criteria. The system according to feature 1.

4. The aforementioned notification unit, If a product is found that matches the criteria, the user will be notified via push notification. The system according to feature 1.

5. The aforementioned purchasing department, Purchase products sold on our group company's online shop with just one click. The system according to feature 1.

6. The aforementioned registration unit is The system estimates the user's emotions and adjusts how product details are entered based on those estimated emotions. The system according to feature 1.

7. The aforementioned registration unit is We provide recommended product information based on the user's past purchase history. The system according to feature 1.

8. The aforementioned registration unit is Based on the user's current interests, we suggest relevant products. The system according to feature 1.

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

10. The aforementioned registration unit is Provide region-specific product information, taking into account the user's geographical location. The system according to feature 1.

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