system

Abstract

The system according to this embodiment aims to enable users to efficiently check the inventory status of the products they want. [Solution] The system according to the embodiment comprises a reception unit, a search unit, a location information setting unit, an update unit, and a provision unit. The reception unit receives input from the user, such as a specific product name or category. The search unit searches for nearby store information based on the information entered by the reception unit and displays whether the item is in stock. The location information setting unit sets the search range based on the user's current location. The update unit updates the stock information in real time. The provision unit provides the store information displayed as a search result.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is necessary to visit a plurality of stores to check the inventory status of a desired product, which is time-consuming.

[0005] [[ID=3又9]]The system according to the embodiment aims to enable a user to efficiently check the inventory status of a desired product. <未知,原文可能有误,推测为40,实际应为39>

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a search unit, a location information setting unit, an update unit, and a provision unit. The reception unit receives input from the user, such as a specific product name or category. The search unit searches for nearby store information based on the information entered by the reception unit and displays whether the items are in stock. The location information setting unit sets the search range based on the user's current location. The update unit updates the inventory information in real time. The provision unit provides the store information displayed as a search result. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to efficiently check the inventory status of 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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI application according to an embodiment of the present invention is a system that allows users to quickly find out where a desired product is sold. This system searches for nearby store information and displays availability when a user enters a specific product name or category. The system sets a search range based on the user's current location and acquires location information in real time using GPS. This allows the user to check which stores sell the searched product within a specified distance. Furthermore, inventory information is updated in real time, and a simple management tool is provided for stores to update their inventory information. The search results also provide the store's address, business hours, contact information, and special offer information, and include a route guidance function to the store. It also displays user reviews and ratings, allowing users to refer to the opinions of other consumers. Additionally, it has a function to send notifications when a user-specified product arrives at a nearby store or when there is special offer information. For example, when a user enters a specific product name or category, the system searches for nearby store information and displays availability. The system sets a search range based on the user's current location and acquires location information in real time using GPS. This allows the user to check which stores sell the searched product within a specified distance. Furthermore, inventory information is updated in real time, and a simple management tool is provided for stores to update their inventory. The search results also include the store's address, business hours, contact information, and special offers, and a route guidance function to the store is also included. In addition, user reviews and ratings are displayed, allowing users to refer to the opinions of other consumers. Furthermore, there is a function to send notifications when a product specified by the user arrives at a nearby store or when there is a special offer. As a result, the AI ​​app allows users to quickly find the products they want, saving them the trouble of unnecessary store visits and phone calls.

[0029] The AI ​​application according to this embodiment comprises a reception unit, a search unit, a location information setting unit, an update unit, and a provision unit. The reception unit receives input from the user, such as a specific product name or category. The reception unit receives this information from the user. For example, the reception unit can receive the product name or category entered by the user in text format. The reception unit can also accept voice input. For example, if the user enters a product name or category by voice, the reception unit can recognize the voice and convert it into text. Furthermore, the reception unit can also accept image input. For example, if the user uploads an image of a product, the reception unit can analyze the image and identify the product name or category. The search unit searches for nearby store information based on the information entered by the reception unit and displays whether the product is in stock. The search unit can search for nearby store information from a database based on the product name or category entered by the user. The search unit can also obtain real-time inventory information from each store in order to display whether the product is in stock. For example, the search unit can access the inventory database of each store and check the inventory status of a specified product. Furthermore, the search unit can format search results and display them on the user interface for the user to see. The location information setting unit sets the search range based on the current location. For example, the location information setting unit can obtain the user's current location using GPS and set the search range based on that information. The location information setting unit can also allow the user to manually set the search range. For example, if the user specifies a search range within a radius of a certain number of kilometers, the location information setting unit can perform a search based on that range. Furthermore, the location information setting unit can also automatically set the search range based on the user's past search history. For example, based on the ranges the user has frequently searched in the past, the location information setting unit can suggest the optimal search range. The update unit updates inventory information in real time. For example, the update unit can provide a management tool for each store to update inventory information. The update unit can also automatically update inventory information periodically.For example, the update unit can access the inventory database of each store at regular time intervals to obtain the latest inventory information. Furthermore, the update unit has a function to notify users in real time when there are changes in inventory information. For example, the update unit can send a notification to the user when a specific product is in stock. The provision unit provides information about the stores displayed as search results. For example, the provision unit can provide the user with the address, business hours, contact information, and special sale information of the stores displayed as search results. The provision unit can also provide a route guidance function to the store. For example, the provision unit can display the shortest route from the user's current location to the store and provide navigation. Furthermore, the provision unit has a function to display user reviews and ratings. For example, the provision unit can display user reviews and ratings so that users can refer to the opinions of other consumers. As a result, the AI ​​application according to the embodiment can enable users to quickly find the products they want, saving them the trouble of unnecessary store visits and phone calls.

[0030] The reception desk receives information from users, such as specific product names or categories. For example, the reception desk can receive the user's input in text format. It can also accept voice input. For instance, if a user inputs a product name or category by voice, the reception desk can recognize the voice and convert it to text. Furthermore, the reception desk can accept image input. For example, if a user uploads an image of a product, the reception desk can analyze the image and identify the product name and category. The reception desk provides an intuitive and user-friendly input method through its user interface. For example, with text input, an autocomplete function activates as the user begins typing, displaying suggested product names and categories. This allows users to quickly find the desired product. With voice input, speech recognition technology is used to convert the user's speech into text with high accuracy. The speech recognition technology includes noise cancellation to eliminate ambient noise and achieve accurate speech recognition. With image input, image analysis technology is used to automatically extract product names and categories from uploaded images. Image analysis technology utilizes deep learning-based image recognition models to identify products with high accuracy. This allows the reception desk to receive information quickly and accurately, regardless of the input method chosen by the user. Furthermore, the reception desk has a function to save the user's input history and assist with future inputs. For example, it can automatically display previously entered product names and categories, saving the user the trouble of re-entering them. This improves user convenience and enables smooth operation.

[0031] The search unit searches for nearby store information based on the information entered by the reception unit and displays the availability of stock. For example, the search unit can search for nearby store information from the database based on the product name or category entered by the user. The search unit can also obtain real-time inventory information from each store in order to display the availability of stock. For example, the search unit can access the inventory database of each store and check the stock status of a specified product. Furthermore, the search unit can format the search results and display them on the user interface for display to the user. The search unit uses an efficient search algorithm to quickly provide the information the user is looking for. For example, by combining index search and full-text search, it can quickly extract the necessary information from large amounts of data. The search unit also has a function to learn the user's search history and preferences and provide personalized search results. For example, it can prioritize displaying highly relevant store information based on products and categories that have been searched in the past. Furthermore, the search unit can customize the display format of the search results. For example, it can display search results in list format or grid format, allowing the user to choose the format that is easiest to view. Furthermore, the search results will also display detailed information such as product prices, ratings, and reviews, making it easier for users to compare and consider options. This allows the search engine to quickly and accurately provide users with the information they need, supporting their purchasing decisions.

[0032] The location information setting unit sets the search range based on the user's current location. For example, the location information setting unit can acquire the user's current location using GPS and set the search range based on that information. The location information setting unit also allows the user to manually set the search range. For example, if the user specifies a search range within a radius of a certain number of kilometers, the location information setting unit can perform a search based on that range. Furthermore, the location information setting unit can automatically set the search range based on the user's past search history. For example, based on the ranges the user has frequently searched in the past, the location information setting unit can suggest the optimal search range. The location information setting unit accurately determines the user's current location using highly accurate location information acquisition technology. For example, by using not only GPS but also Wi-Fi and Bluetooth® beacons in combination, highly accurate location information can be acquired both indoors and outdoors. In addition, the location information setting unit provides settings regarding the acquisition and use of location information to protect user privacy. For example, it allows users to select the scope of location information sharing and the purpose of its use, enabling operation that respects privacy. Furthermore, the location information setting unit provides a function to set multiple search ranges simultaneously to allow for flexibility in setting the search range. For example, if a user wants to search for products in multiple regions, they can set each region individually and then display the search results in an integrated manner. This enables the location information setting unit to realize flexible search range settings that meet user needs and supports efficient information provision.

[0033] The update unit updates inventory information in real time. For example, it can provide management tools for each store to update their inventory information. It can also automatically update inventory information periodically. For instance, it can access each store's inventory database at regular intervals to retrieve the latest inventory information. Furthermore, the update unit has a function to notify users in real time when there are changes in inventory information. For example, it can send notifications to users when a specific product arrives in stock. The update unit maintains the accuracy and timeliness of inventory information using efficient data synchronization technology. For example, it can synchronize each store's inventory information with a central server using database replication technology and update it in real time. The update unit also manages the history of inventory information changes and can track past inventory status. This allows for analysis of inventory fluctuation trends and helps optimize demand forecasting and inventory management. Furthermore, the update unit allows for flexible setting of the frequency and timing of inventory information updates. For example, by centrally updating inventory information during specific time periods or days of the week, it can respond to peak demand. The update unit also enhances notification functions associated with inventory information updates, providing users with information quickly and reliably. For example, changes in inventory information can be notified in real time using push notifications or email notifications. This allows the update unit to maintain the accuracy and up-to-dateness of inventory information and provide users with reliable information.

[0034] The service provider provides information about the stores displayed in the search results. For example, it can provide users with the address, business hours, contact information, and special offers for the stores displayed in the search results. The service provider can also provide a route guidance function to the stores. For example, it can display the shortest route from the user's current location to the store and provide navigation. Furthermore, the service provider has a function to display user reviews and ratings. For example, it can display user reviews and ratings so that users can refer to the opinions of other consumers. The service provider provides information in a visually easy-to-understand manner through the user interface. For example, it uses a map display function to intuitively show the location of stores, making it easy for users to find stores. The service provider also displays detailed store information in a tab format, allowing users to check the address, business hours, contact information, and special offers at a glance. Furthermore, the service provider provides a store information filtering function to improve user convenience. For example, users can narrow down stores based on specific conditions. This allows users to quickly find stores that meet their needs. The service provider also has a function to evaluate the reliability and service quality of stores based on user reviews and ratings. For example, star ratings and comments can be displayed, allowing users to refer to the opinions of other users. This enables the service provider to help users choose stores with confidence. Furthermore, the service provider can enhance the route guidance function to stores so that users can arrive without getting lost. For example, it can provide navigation that reflects real-time traffic information and guide users along the optimal route. This enables the service provider to provide comprehensive information to users and improve the purchasing experience.

[0035] The notification unit has a notification function. For example, the notification unit can send notifications when a product specified by the user arrives at a nearby store or when there is special sale information. The notification unit can send notifications using methods such as push notifications, email notifications, and SMS notifications. For example, the notification unit can send push notifications if the user has installed the app. Also, the notification unit can send email notifications if the user has registered an email address. Furthermore, the notification unit can send SMS notifications if the user has registered a phone number. This allows the notification unit to provide information to the user in real time.

[0036] The review section displays user reviews and ratings. For example, the review section can display user reviews and ratings for stores and products displayed as search results. The review section can display user reviews and ratings in various formats, such as star ratings, comments, and the number of reviews. For instance, the review section can display star ratings, allowing users to quickly see the ratings of stores and products. It can also display comments posted by users, allowing users to refer to the opinions of other consumers. Furthermore, the review section can display the number of reviews, allowing users to verify the reliability of the ratings. This allows the review section to provide valuable insights into the opinions of other consumers.

[0037] The recommendation system analyzes purchase and search history to provide personalized product recommendations. For example, it can analyze a user's past purchase and search history to recommend the most suitable products. The recommendation system can use machine learning algorithms to analyze user interests and provide personalized product recommendations. For example, it can recommend related products based on products a user has previously purchased or searched for. It can also recommend products that are best suited to a specific time of day based on the user's purchase and search history. Furthermore, the recommendation system can analyze user interests and recommend the most suitable products. This allows the recommendation system to provide users with an optimal shopping experience.

[0038] The management department provides management tools for stores to update inventory information. For example, the management department can provide management tools for each store to update inventory information. The management department can provide management tools with functions such as inventory management, pricing, and sales data analysis. For example, the management department provides inventory management functions so that each store can easily update inventory information. Furthermore, the management department provides pricing functions so that each store can set product prices. In addition, the management department provides sales data analysis functions so that each store can analyze sales data. This makes inventory information management easier for the management department.

[0039] The navigation unit has a route guidance function to the store. For example, the navigation unit can display the shortest route from the user's current location to the store and provide navigation. The navigation unit can provide route guidance using methods such as map display, voice guidance, and route selection. For example, the navigation unit can display a map so that the user can visually confirm the route to the store. In addition, the navigation unit can provide voice guidance so that the user can check the route while driving or walking. Furthermore, the navigation unit can suggest multiple routes so that the user can select the optimal route. In this way, the navigation unit can help the user confirm the shortest route to the store.

[0040] The reception desk can analyze the user's past search history and suggest the most suitable input options. For example, the reception desk can automatically display product names and categories that the user has frequently searched for in the past as suggestions. Furthermore, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. In addition, the reception desk can predict and suggest product names and categories that the user might use at specific times of day based on their past search history. This allows the reception desk to suggest the most suitable input options by analyzing the user's past search history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0041] The input field can filter input suggestions based on the user's current areas of interest when the user enters a product name or category. For example, the input field can prioritize displaying relevant product names based on product categories the user has recently searched for. Furthermore, if the user is searching for products related to a specific event (e.g., birthday, Christmas), the input field can prioritize displaying product names related to that event. Additionally, if the user is interested in a particular brand, the input field can prioritize displaying product names from that brand. This allows for the presentation of highly relevant suggestions by filtering input suggestions based on the user's current areas of interest. Some or all of the above processing in the input field may be performed using AI, for example, or without AI.

[0042] The reception system can prioritize displaying highly relevant suggestions when users input product names or categories, taking into account their geographical location. For example, the reception system can prioritize displaying popular product names and categories in the user's current location. Furthermore, if the user is near a specific store, the reception system can prioritize displaying product names and categories available at that store. Additionally, if the user is participating in an event related to a specific region, the reception system can prioritize displaying product names and categories related to that event. This improves user convenience by presenting highly relevant suggestions based on the user's geographical location. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI.

[0043] The reception desk can analyze the user's social media activity when they enter a product name or category, and then suggest relevant options. For example, the reception desk can prioritize displaying product names and categories that the user frequently mentions on social media. It can also prioritize displaying product names and categories recommended by brands and influencers that the user follows on social media. Furthermore, it can prioritize displaying product names and categories related to groups and events that the user participates in on social media. This improves user convenience by analyzing the user's social media activity and suggesting relevant options. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0044] The search unit can adjust the level of detail in search results based on the importance of the products during a search. For example, the search unit can display search results with detailed information for expensive or rare products. For common products, the search unit can display search results with concise information. Furthermore, if the user is interested in a particular brand, the search unit can display search results with detailed information for products from that brand. This improves user convenience by adjusting the level of detail in search results based on the importance of the products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI.

[0045] The search unit can apply different search algorithms depending on the product category during a search. For example, in the case of electronic devices, the search unit can apply a search algorithm that emphasizes technical specifications and reviews. In the case of fashion items, the search unit can apply a search algorithm that emphasizes trends and style. Furthermore, in the case of food products, the search unit can apply a search algorithm that emphasizes expiration dates and nutritional information. By applying different search algorithms depending on the product category, user convenience is improved. Some or all of the above processing in the search unit may be performed using AI, for example, or without using AI.

[0046] The search unit can prioritize search results based on when the products were submitted. For example, it can prioritize recently arrived products. It can also prioritize products that the user has searched for in the past. Furthermore, for seasonal and limited-edition products, the search unit can adjust the priority based on when they were submitted. This improves user convenience by prioritizing search results based on when the products were submitted. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI.

[0047] The search function can adjust the order of search results based on the relevance of the products during a search. For example, the search function can prioritize displaying products that are highly relevant to the product the user searched for. It can also prioritize displaying products that are highly relevant to products the user has previously purchased. Furthermore, if the user is interested in a particular brand, the search function can prioritize displaying products from that brand. By adjusting the order of search results based on the relevance of the products, user convenience is improved. Some or all of the above processing in the search function may be performed using AI, for example, or without AI.

[0048] The location information setting unit can suggest an optimal search area by referring to the user's past travel history when setting location information. For example, the location information setting unit can suggest an optimal search area based on places the user has frequently visited in the past. It can also suggest a search area that avoids congestion based on the user's past travel history. Furthermore, the location information setting unit can analyze the user's past travel history and suggest the most efficient search area. This improves user convenience by suggesting an optimal search area by referring to the user's past travel history. Some or all of the above processing in the location information setting unit may be performed using AI, for example, or without using AI.

[0049] The location information setting unit can customize the search range based on the user's current living situation when setting the location information. For example, if the user is raising children, the location information setting unit can prioritize including stores that handle children's products in the search range. Also, if the user is elderly, the location information setting unit can prioritize including barrier-free stores in the search range. Furthermore, if the user is a tourist, the location information setting unit can prioritize including stores around tourist destinations in the search range. By customizing the search range based on the user's current living situation, user convenience is improved. Some or all of the above processing in the location information setting unit may be performed using AI, for example, or without using AI.

[0050] The location information setting unit can suggest an optimal search range considering the user's geographical location when setting location information. For example, the location information setting unit can prioritize including popular stores in the area where the user is currently located within the search range. Furthermore, if the user is near a specific store, the location information setting unit can prioritize including that store within the search range. Additionally, if the user is participating in an event related to a specific area, the location information setting unit can prioritize including stores related to that event within the search range. This improves user convenience by suggesting an optimal search range considering the user's geographical location. Some or all of the above processing in the location information setting unit may be performed using AI, for example, or without AI.

[0051] The location information setting unit can analyze the user's social media activity and adjust the search range when setting location information. For example, the location information setting unit can prioritize including stores that the user frequently mentions on social media in the search range. It can also prioritize including stores recommended by brands and influencers that the user follows on social media in the search range. Furthermore, the location information setting unit can prioritize including stores related to groups and events that the user participates in on social media in the search range. By analyzing the user's social media activity and adjusting the search range accordingly, user convenience is improved. Some or all of the above processing in the location information setting unit may be performed using AI, for example, or without using AI.

[0052] The update unit can optimize its update algorithm by referring to past inventory data during updates. For example, the update unit can analyze past inventory data and increase the update frequency of high-demand products. It can also optimize the update timing of seasonal and limited-edition products based on past inventory data. Furthermore, the update unit can adjust the update frequency of products with a high risk of stockout by referring to past inventory data. This improves the accuracy of inventory information by optimizing the update algorithm by referring to past inventory data. Some or all of the above processes in the update unit may be performed using AI, for example, or without using AI.

[0053] The update unit can apply different update methods to each product category during the update process. For example, in the case of electronic devices, the update unit can apply an update method that emphasizes technical specifications and new product information. In the case of fashion items, the update unit can apply an update method that emphasizes trends and styles. Furthermore, in the case of food products, the update unit can apply an update method that emphasizes expiration dates and nutritional information. By applying different update methods to each product category, the accuracy of inventory information is improved. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI.

[0054] The update unit can weight update data based on the product submission date during the update process. For example, the update unit can prioritize updating recently received products. It can also prioritize updating products that users have previously searched for. Furthermore, for seasonal and limited-edition products, the update unit can adjust the weighting of update data based on the submission date. This improves the accuracy of inventory information by weighting update data based on the product submission date. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI.

[0055] The update unit can improve the accuracy of updates by referring to relevant market data for products during the update process. For example, the update unit can increase the update frequency of high-demand products based on relevant market data. It can also adjust the update frequency of products with a high risk of stockouts by referring to relevant market data. Furthermore, the update unit can optimize the update timing of seasonal and limited-edition products based on relevant market data. By improving the accuracy of updates by referring to relevant market data for products, the accuracy of inventory information is improved. Some or all of the above processes in the update unit may be performed using AI, for example, or without using AI.

[0056] The service provider can select the most suitable store information by referring to the user's past search history at the time of service provision. For example, the service provider can prioritize displaying store information that the user has frequently searched for in the past. Furthermore, the service provider can predict and suggest store information that the user may use at a specific time of day based on their past search history. In addition, the service provider can analyze the user's past search history and select the most relevant store information. This improves user convenience by selecting the most suitable store information by referring to the user's past search history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0057] The service provider can customize store information based on the user's current areas of interest at the time of delivery. For example, the service provider can prioritize displaying relevant store information based on the product categories the user has recently searched for. Furthermore, if the user is searching for products related to a specific event (e.g., birthday, Christmas), the service provider can prioritize displaying store information related to that event. Additionally, if the user is interested in a particular brand, the service provider can prioritize displaying store information that carries that brand. This improves user convenience by customizing store information based on the user's current areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0058] The service provider can select the most suitable store information by considering the user's geographical location at the time of delivery. For example, the service provider can prioritize displaying information about popular stores in the area where the user is currently located. Furthermore, if the user is near a specific store, the service provider can prioritize displaying information about that store. In addition, if the user is participating in an event related to a specific area, the service provider can prioritize displaying information about stores related to that event. This improves user convenience by selecting the most suitable store information by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0059] The service provider can provide store information by analyzing the user's social media activity at the time of delivery. For example, the service provider can prioritize displaying store information that the user frequently mentions on social media. It can also prioritize displaying store information recommended by brands and influencers that the user follows on social media. Furthermore, the service provider can prioritize displaying store information related to groups and events that the user participates in on social media. This improves user convenience by providing store information based on an analysis of the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0060] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can prioritize notification methods that the user has frequently received in the past (e.g., push notifications, email). Furthermore, the notification unit can predict and suggest the optimal notification method for a specific time period based on the user's past notification history. In addition, the notification unit can analyze the user's past notification history and select the most effective notification method. This improves user convenience by selecting the optimal notification method based on the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI.

[0061] The notification unit can select the most suitable notification method when sending a notification, taking into account the user's geographical location. For example, the notification unit can prioritize notification methods popular in the user's current location. Furthermore, if the user is near a specific store, the notification unit can prioritize notification methods related to that store. Additionally, if the user is participating in an event related to a specific region, the notification unit can prioritize notification methods related to that event. This improves user convenience by selecting the most suitable notification method based on the user's geographical location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI.

[0062] The review section can select the optimal display method when displaying reviews by referring to the user's past review history. For example, the review section can prioritize displaying review formats that the user has frequently viewed in the past. Furthermore, the review section can predict and suggest the optimal display method for a specific time period based on the user's past review history. In addition, the review section can analyze the user's past review history and select the most effective display method. This improves user convenience by selecting the optimal display method by referring to the user's past review history. Some or all of the above processing in the review section may be performed using AI, for example, or without using AI.

[0063] The review unit can select the optimal display method when displaying reviews, taking into account the user's device information. For example, if the user is using a smartphone, the review unit can provide a display method that matches the screen size. If the user is using a tablet, the review unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the review unit can provide a concise and highly visible display method. By selecting the optimal display method considering the user's device information, user convenience is improved. Some or all of the above processing in the review unit may be performed using AI, for example, or without using AI.

[0064] The recommendation system can select the most suitable products by referring to the user's past purchase history when making recommendations. For example, the recommendation system can display relevant recommended products based on the product categories that the user has frequently purchased in the past. Furthermore, the recommendation system can predict and suggest the most suitable products for a specific time period based on the user's past purchase history. In addition, the recommendation system can analyze the user's past purchase history and select the most relevant recommended products. This improves user convenience by selecting the most suitable products based on the user's past purchase history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI.

[0065] The recommendation system can customize recommended products based on the user's current areas of interest. For example, it can display relevant recommended products based on product categories the user has recently searched for. It can also display recommended products related to a specific event (e.g., birthday, Christmas) if the user is searching for products related to that event. Furthermore, if the user is interested in a particular brand, the recommendation system can prioritize displaying products from that brand. This improves user convenience by customizing recommended products based on the user's current areas of interest. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not.

[0066] The recommendation system can select the most suitable products by considering the user's geographical location. For example, it can prioritize displaying products that are popular in the user's current location. It can also prioritize displaying products available at a specific store if the user is near that store. Furthermore, if the user is participating in an event related to a specific region, it can prioritize displaying products related to that event. This improves user convenience by selecting the most suitable products based on the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI.

[0067] The recommendation system can analyze a user's social media activity to provide recommended products. For example, it can prioritize displaying products that the user frequently mentions on social media. It can also prioritize displaying products recommended by brands and influencers that the user follows on social media. Furthermore, it can prioritize displaying products related to groups and events that the user participates in on social media. This improves user convenience by providing recommended products based on an analysis of the user's social media activity. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI.

[0068] The management department can optimize its management algorithm by referring to past inventory data during management. For example, the management department can analyze past inventory data and increase the management frequency of high-demand products. Furthermore, based on past inventory data, the management department can optimize the management timing of seasonal and limited-edition products. In addition, the management department can adjust the management frequency of products with a high risk of stockout by referring to past inventory data. This improves the accuracy of inventory information by optimizing the management algorithm by referring to past inventory data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI.

[0069] The management department can weight management data based on the submission date of products during the management process. For example, the management department can prioritize the management of recently received products. It can also prioritize the management of products that users have previously searched for. Furthermore, for seasonal and limited-edition products, the management department can adjust the weighting of management data based on the submission date. This improves the accuracy of inventory information by weighting management data based on the submission date of products. Some or all of the above processes in the management department may be performed using AI, for example, or without AI.

[0070] The guidance unit can suggest the optimal route by referring to the user's past travel history when providing directions. For example, the guidance unit can suggest the optimal route based on routes the user has frequently used in the past. Furthermore, the guidance unit can suggest routes that avoid congestion based on the user's past travel history. In addition, the guidance unit can analyze the user's past travel history and suggest the most efficient route. This improves user convenience by suggesting the optimal route based on the user's past travel history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI.

[0071] The navigation system can suggest the optimal route while considering the user's geographical location. For example, it can prioritize displaying popular routes in the user's current location. It can also display the optimal route to a specific store if the user is near it. Furthermore, if the user is participating in an event related to a specific area, it can prioritize displaying routes related to that event. This improves user convenience by suggesting the optimal route while considering the user's geographical location. Some or all of the above processing in the navigation system may be performed using AI, for example, or without AI.

[0072] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0073] The reception system can suggest input options by referencing the user's past purchase history when they enter a specific product name or category. For example, it can automatically display related product names and categories as suggestions based on products and categories the user has previously purchased. The reception system can also prioritize displaying product names and categories the user has previously searched for. Furthermore, the reception system can analyze the user's past purchase history and suggest the most suitable product names and categories for a specific time period. This improves user convenience by suggesting optimal input options based on the user's past purchase history.

[0074] The navigation system can suggest the optimal route by referring to the user's past travel history. For example, it can suggest the best route based on routes the user has frequently used in the past. Furthermore, the navigation system can suggest routes that avoid congestion based on the user's past travel history. In addition, the navigation system can analyze the user's past travel history and suggest the most efficient route. This improves user convenience by suggesting the optimal route based on the user's past travel history.

[0075] The search function can apply different search algorithms depending on the product category. For example, for electronic devices, a search algorithm that emphasizes technical specifications and reviews can be applied. For fashion items, a search algorithm that emphasizes trends and style can be applied. Furthermore, for food products, a search algorithm that emphasizes expiration dates and nutritional information can be applied. By applying different search algorithms depending on the product category, user convenience is improved.

[0076] The service provider can customize store information based on the user's current areas of interest. For example, it can prioritize displaying relevant store information based on the product categories the user has recently searched for. Furthermore, if the user is searching for products related to a specific event (e.g., birthday, Christmas), it can prioritize displaying store information related to that event. Additionally, if the user is interested in a particular brand, it can prioritize displaying store information that carries that brand. This improves user convenience by customizing store information based on the user's current areas of interest.

[0077] The notification unit can select the most suitable notification method by considering the user's geographical location. For example, it can prioritize notification methods popular in the user's current location. Furthermore, if the user is near a specific store, it can prioritize notification methods related to that store. Additionally, if the user is participating in an event related to a specific region, it can prioritize notification methods related to that event. This improves user convenience by selecting the most suitable notification method based on the user's geographical location.

[0078] The review section can select the optimal display method considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, selecting the optimal display method considering the user's device information improves user convenience.

[0079] The following briefly describes the processing flow for example form 1.

[0080] Step 1: The reception desk receives the user's input of a specific product name or category. Users can input the product name or category using text, voice input, or image input. The reception desk receives this input and, if necessary, converts voice to text or analyzes images to identify the product name or category. Step 2: The search unit searches for nearby store information based on the information entered by the reception unit and displays the availability of the product. The search unit searches for nearby store information from the database and accesses the inventory database of each store to check the inventory status of the specified product. The search results are formatted and displayed in the user interface. Step 3: The location information setting unit sets the search range based on the current location. The location information setting unit obtains the user's current location using GPS and sets the search range based on that information. The user can also manually set the search range, or the search range can be automatically set based on past search history. Step 4: The update unit updates inventory information in real time. The update unit provides a management tool for each store to update inventory information and automatically updates inventory information regularly. It also has a function to notify in real time when there are changes in inventory information. Step 5: The service provider provides information about the stores displayed in the search results. The service provider provides users with store addresses, business hours, contact information, special offers, etc., and also includes functions to provide directions to the stores and display user reviews and ratings.

[0081] (Example of form 2) An AI application according to an embodiment of the present invention is a system that allows users to quickly find out where a desired product is sold. This system searches for nearby store information and displays availability when a user enters a specific product name or category. The system sets a search range based on the user's current location and acquires location information in real time using GPS. This allows the user to check which stores sell the searched product within a specified distance. Furthermore, inventory information is updated in real time, and a simple management tool is provided for stores to update their inventory information. The search results also provide the store's address, business hours, contact information, and special offer information, and include a route guidance function to the store. It also displays user reviews and ratings, allowing users to refer to the opinions of other consumers. Additionally, it has a function to send notifications when a user-specified product arrives at a nearby store or when there is special offer information. For example, when a user enters a specific product name or category, the system searches for nearby store information and displays availability. The system sets a search range based on the user's current location and acquires location information in real time using GPS. This allows the user to check which stores sell the searched product within a specified distance. Furthermore, inventory information is updated in real time, and a simple management tool is provided for stores to update their inventory. The search results also include the store's address, business hours, contact information, and special offers, and a route guidance function to the store is also included. In addition, user reviews and ratings are displayed, allowing users to refer to the opinions of other consumers. Furthermore, there is a function to send notifications when a product specified by the user arrives at a nearby store or when there is a special offer. As a result, the AI ​​app allows users to quickly find the products they want, saving them the trouble of unnecessary store visits and phone calls.

[0082] The AI ​​application according to this embodiment comprises a reception unit, a search unit, a location information setting unit, an update unit, and a provision unit. The reception unit receives input from the user, such as a specific product name or category. The reception unit receives this information from the user. For example, the reception unit can receive the product name or category entered by the user in text format. The reception unit can also accept voice input. For example, if the user enters a product name or category by voice, the reception unit can recognize the voice and convert it into text. Furthermore, the reception unit can also accept image input. For example, if the user uploads an image of a product, the reception unit can analyze the image and identify the product name or category. The search unit searches for nearby store information based on the information entered by the reception unit and displays whether the product is in stock. The search unit can search for nearby store information from a database based on the product name or category entered by the user. The search unit can also obtain real-time inventory information from each store in order to display whether the product is in stock. For example, the search unit can access the inventory database of each store and check the inventory status of a specified product. Furthermore, the search unit can format search results and display them on the user interface for the user to see. The location information setting unit sets the search range based on the current location. For example, the location information setting unit can obtain the user's current location using GPS and set the search range based on that information. The location information setting unit can also allow the user to manually set the search range. For example, if the user specifies a search range within a radius of a certain number of kilometers, the location information setting unit can perform a search based on that range. Furthermore, the location information setting unit can also automatically set the search range based on the user's past search history. For example, based on the ranges the user has frequently searched in the past, the location information setting unit can suggest the optimal search range. The update unit updates inventory information in real time. For example, the update unit can provide a management tool for each store to update inventory information. The update unit can also automatically update inventory information periodically.For example, the update unit can access the inventory database of each store at regular time intervals to obtain the latest inventory information. Furthermore, the update unit has a function to notify users in real time when there are changes in inventory information. For example, the update unit can send a notification to the user when a specific product is in stock. The provision unit provides information about the stores displayed as search results. For example, the provision unit can provide the user with the address, business hours, contact information, and special sale information of the stores displayed as search results. The provision unit can also provide a route guidance function to the store. For example, the provision unit can display the shortest route from the user's current location to the store and provide navigation. Furthermore, the provision unit has a function to display user reviews and ratings. For example, the provision unit can display user reviews and ratings so that users can refer to the opinions of other consumers. As a result, the AI ​​application according to the embodiment can enable users to quickly find the products they want, saving them the trouble of unnecessary store visits and phone calls.

[0083] The reception desk receives information from users, such as specific product names or categories. For example, the reception desk can receive the user's input in text format. It can also accept voice input. For instance, if a user inputs a product name or category by voice, the reception desk can recognize the voice and convert it to text. Furthermore, the reception desk can accept image input. For example, if a user uploads an image of a product, the reception desk can analyze the image and identify the product name and category. The reception desk provides an intuitive and user-friendly input method through its user interface. For example, with text input, an autocomplete function activates as the user begins typing, displaying suggested product names and categories. This allows users to quickly find the desired product. With voice input, speech recognition technology is used to convert the user's speech into text with high accuracy. The speech recognition technology includes noise cancellation to eliminate ambient noise and achieve accurate speech recognition. With image input, image analysis technology is used to automatically extract product names and categories from uploaded images. Image analysis technology utilizes deep learning-based image recognition models to identify products with high accuracy. This allows the reception desk to receive information quickly and accurately, regardless of the input method chosen by the user. Furthermore, the reception desk has a function to save the user's input history and assist with future inputs. For example, it can automatically display previously entered product names and categories, saving the user the trouble of re-entering them. This improves user convenience and enables smooth operation.

[0084] The search unit searches for nearby store information based on the information entered by the reception unit and displays the availability of stock. For example, the search unit can search for nearby store information from the database based on the product name or category entered by the user. The search unit can also obtain real-time inventory information from each store in order to display the availability of stock. For example, the search unit can access the inventory database of each store and check the stock status of a specified product. Furthermore, the search unit can format the search results and display them on the user interface for display to the user. The search unit uses an efficient search algorithm to quickly provide the information the user is looking for. For example, by combining index search and full-text search, it can quickly extract the necessary information from large amounts of data. The search unit also has a function to learn the user's search history and preferences and provide personalized search results. For example, it can prioritize displaying highly relevant store information based on products and categories that have been searched in the past. Furthermore, the search unit can customize the display format of the search results. For example, it can display search results in list format or grid format, allowing the user to choose the format that is easiest to view. Furthermore, the search results will also display detailed information such as product prices, ratings, and reviews, making it easier for users to compare and consider options. This allows the search engine to quickly and accurately provide users with the information they need, supporting their purchasing decisions.

[0085] The location information setting unit sets the search range based on the user's current location. For example, the location information setting unit can acquire the user's current location using GPS and set the search range based on that information. The location information setting unit also allows the user to manually set the search range. For example, if the user specifies a search range within a radius of a certain number of kilometers, the location information setting unit can perform a search based on that range. Furthermore, the location information setting unit can automatically set the search range based on the user's past search history. For example, based on the ranges the user has frequently searched in the past, the location information setting unit can suggest the optimal search range. The location information setting unit accurately determines the user's current location using highly accurate location information acquisition technology. For example, by using not only GPS but also Wi-Fi and Bluetooth beacons in combination, highly accurate location information can be acquired both indoors and outdoors. In addition, the location information setting unit provides settings regarding the acquisition and use of location information to protect user privacy. For example, it allows users to select the scope and purpose of sharing location information, enabling privacy-conscious operation. Furthermore, the location information setting unit provides a function to set multiple search ranges simultaneously to provide flexibility in setting the search range. For example, if a user wants to search for products in multiple regions, they can set each region individually and then display the search results in a consolidated manner. This allows the location information setting unit to enable flexible search range settings according to the user's needs and supports efficient information provision.

[0086] The update unit updates inventory information in real time. For example, it can provide management tools for each store to update their inventory information. It can also automatically update inventory information periodically. For instance, it can access each store's inventory database at regular intervals to retrieve the latest inventory information. Furthermore, the update unit has a function to notify users in real time when there are changes in inventory information. For example, it can send notifications to users when a specific product arrives in stock. The update unit maintains the accuracy and timeliness of inventory information using efficient data synchronization technology. For example, it can synchronize each store's inventory information with a central server using database replication technology and update it in real time. The update unit also manages the history of inventory information changes and can track past inventory status. This allows for analysis of inventory fluctuation trends and helps optimize demand forecasting and inventory management. Furthermore, the update unit allows for flexible setting of the frequency and timing of inventory information updates. For example, by centrally updating inventory information during specific time periods or days of the week, it can respond to peak demand. The update unit also enhances notification functions associated with inventory information updates, providing users with information quickly and reliably. For example, changes in inventory information can be notified in real time using push notifications or email notifications. This allows the update unit to maintain the accuracy and up-to-dateness of inventory information and provide users with reliable information.

[0087] The service provider provides information about the stores displayed in the search results. For example, it can provide users with the address, business hours, contact information, and special offers for the stores displayed in the search results. The service provider can also provide a route guidance function to the stores. For example, it can display the shortest route from the user's current location to the store and provide navigation. Furthermore, the service provider has a function to display user reviews and ratings. For example, it can display user reviews and ratings so that users can refer to the opinions of other consumers. The service provider provides information in a visually easy-to-understand manner through the user interface. For example, it uses a map display function to intuitively show the location of stores, making it easy for users to find stores. The service provider also displays detailed store information in a tab format, allowing users to check the address, business hours, contact information, and special offers at a glance. Furthermore, the service provider provides a store information filtering function to improve user convenience. For example, users can narrow down stores based on specific conditions. This allows users to quickly find stores that meet their needs. The service provider also has a function to evaluate the reliability and service quality of stores based on user reviews and ratings. For example, star ratings and comments can be displayed, allowing users to refer to the opinions of other users. This enables the service provider to help users choose stores with confidence. Furthermore, the service provider can enhance the route guidance function to stores so that users can arrive without getting lost. For example, it can provide navigation that reflects real-time traffic information and guide users along the optimal route. This enables the service provider to provide comprehensive information to users and improve the purchasing experience.

[0088] The notification unit has a notification function. For example, the notification unit can send notifications when a product specified by the user arrives at a nearby store or when there is special sale information. The notification unit can send notifications using methods such as push notifications, email notifications, and SMS notifications. For example, the notification unit can send push notifications if the user has installed the app. Also, the notification unit can send email notifications if the user has registered an email address. Furthermore, the notification unit can send SMS notifications if the user has registered a phone number. This allows the notification unit to provide information to the user in real time.

[0089] The review section displays user reviews and ratings. For example, the review section can display user reviews and ratings for stores and products displayed as search results. The review section can display user reviews and ratings in various formats, such as star ratings, comments, and the number of reviews. For instance, the review section can display star ratings, allowing users to quickly see the ratings of stores and products. It can also display comments posted by users, allowing users to refer to the opinions of other consumers. Furthermore, the review section can display the number of reviews, allowing users to verify the reliability of the ratings. This allows the review section to provide valuable insights into the opinions of other consumers.

[0090] The recommendation system analyzes purchase and search history to provide personalized product recommendations. For example, it can analyze a user's past purchase and search history to recommend the most suitable products. The recommendation system can use machine learning algorithms to analyze user interests and provide personalized product recommendations. For example, it can recommend related products based on products a user has previously purchased or searched for. It can also recommend products that are best suited to a specific time of day based on the user's purchase and search history. Furthermore, the recommendation system can analyze user interests and recommend the most suitable products. This allows the recommendation system to provide users with an optimal shopping experience.

[0091] The management department provides management tools for stores to update inventory information. For example, the management department can provide management tools for each store to update inventory information. The management department can provide management tools with functions such as inventory management, pricing, and sales data analysis. For example, the management department provides inventory management functions so that each store can easily update inventory information. Furthermore, the management department provides pricing functions so that each store can set product prices. In addition, the management department provides sales data analysis functions so that each store can analyze sales data. This makes inventory information management easier for the management department.

[0092] The navigation unit has a route guidance function to the store. For example, the navigation unit can display the shortest route from the user's current location to the store and provide navigation. The navigation unit can provide route guidance using methods such as map display, voice guidance, and route selection. For example, the navigation unit can display a map so that the user can visually confirm the route to the store. In addition, the navigation unit can provide voice guidance so that the user can check the route while driving or walking. Furthermore, the navigation unit can suggest multiple routes so that the user can select the optimal route. In this way, the navigation unit can help the user confirm the shortest route to the store.

[0093] The reception desk can estimate the user's emotions and adjust the input method for product names and categories based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of product names and categories. This improves user convenience by adjusting the input method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The reception desk can analyze the user's past search history and suggest the most suitable input options. For example, the reception desk can automatically display product names and categories that the user has frequently searched for in the past as suggestions. Furthermore, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. In addition, the reception desk can predict and suggest product names and categories that the user might use at specific times of day based on their past search history. This allows the reception desk to suggest the most suitable input options by analyzing the user's past search history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0095] The input field can filter input suggestions based on the user's current areas of interest when the user enters a product name or category. For example, the input field can prioritize displaying relevant product names based on product categories the user has recently searched for. Furthermore, if the user is searching for products related to a specific event (e.g., birthday, Christmas), the input field can prioritize displaying product names related to that event. Additionally, if the user is interested in a particular brand, the input field can prioritize displaying product names from that brand. This allows for the presentation of highly relevant suggestions by filtering input suggestions based on the user's current areas of interest. Some or all of the above processing in the input field may be performed using AI, for example, or without AI.

[0096] The reception desk can estimate the user's emotions and prioritize input suggestions based on those emotions. For example, if the user is stressed, the reception desk can prioritize displaying the most relevant input suggestions. If the user is relaxed, the reception desk can present multiple input suggestions to broaden their options. Furthermore, if the user is in a hurry, the reception desk can quickly present input suggestions based on their past search history. This improves user convenience by prioritizing input suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The reception system can prioritize displaying highly relevant suggestions when users input product names or categories, taking into account their geographical location. For example, the reception system can prioritize displaying popular product names and categories in the user's current location. Furthermore, if the user is near a specific store, the reception system can prioritize displaying product names and categories available at that store. Additionally, if the user is participating in an event related to a specific region, the reception system can prioritize displaying product names and categories related to that event. This improves user convenience by presenting highly relevant suggestions based on the user's geographical location. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI.

[0098] The reception desk can analyze the user's social media activity when they enter a product name or category, and then suggest relevant options. For example, the reception desk can prioritize displaying product names and categories that the user frequently mentions on social media. It can also prioritize displaying product names and categories recommended by brands and influencers that the user follows on social media. Furthermore, it can prioritize displaying product names and categories related to groups and events that the user participates in on social media. This improves user convenience by analyzing the user's social media activity and suggesting relevant options. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0099] The search engine can estimate the user's emotions and adjust how search results are displayed based on those emotions. For example, if the user is stressed, the search engine can display simple, easy-to-read search results. If the user is relaxed, it can display search results with more detailed information. Furthermore, if the user is in a hurry, the search engine can prioritize displaying the most relevant search results. This improves user convenience by adjusting how search results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The search unit can adjust the level of detail in search results based on the importance of the products during a search. For example, the search unit can display search results with detailed information for expensive or rare products. For common products, the search unit can display search results with concise information. Furthermore, if the user is interested in a particular brand, the search unit can display search results with detailed information for products from that brand. This improves user convenience by adjusting the level of detail in search results based on the importance of the products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI.

[0101] The search unit can apply different search algorithms depending on the product category during a search. For example, in the case of electronic devices, the search unit can apply a search algorithm that emphasizes technical specifications and reviews. In the case of fashion items, the search unit can apply a search algorithm that emphasizes trends and style. Furthermore, in the case of food products, the search unit can apply a search algorithm that emphasizes expiration dates and nutritional information. By applying different search algorithms depending on the product category, user convenience is improved. Some or all of the above processing in the search unit may be performed using AI, for example, or without using AI.

[0102] The search engine can estimate the user's emotions and adjust the display order of search results based on those emotions. For example, if the user is stressed, the search engine can prioritize displaying the most relevant search results. If the user is relaxed, the search engine can present multiple search results to broaden their options. Furthermore, if the user is in a hurry, the search engine can quickly display search results based on their past search history. By adjusting the display order of search results based on the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The search unit can prioritize search results based on when the products were submitted. For example, it can prioritize recently arrived products. It can also prioritize products that the user has searched for in the past. Furthermore, for seasonal and limited-edition products, the search unit can adjust the priority based on when they were submitted. This improves user convenience by prioritizing search results based on when the products were submitted. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI.

[0104] The search function can adjust the order of search results based on the relevance of the products during a search. For example, the search function can prioritize displaying products that are highly relevant to the product the user searched for. It can also prioritize displaying products that are highly relevant to products the user has previously purchased. Furthermore, if the user is interested in a particular brand, the search function can prioritize displaying products from that brand. By adjusting the order of search results based on the relevance of the products, user convenience is improved. Some or all of the above processing in the search function may be performed using AI, for example, or without AI.

[0105] The location information setting unit can estimate the user's emotions and adjust the search range setting method based on the estimated emotions. For example, if the user is feeling stressed, the location information setting unit can prioritize including the nearest store in the search range. Conversely, if the user is relaxed, the location information setting unit can include a wider range of stores in the search range. Furthermore, if the user is in a hurry, the location information setting unit can quickly set the search range based on the distance from the current location. This improves user convenience by adjusting the search range setting method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The location information setting unit can suggest an optimal search area by referring to the user's past travel history when setting location information. For example, the location information setting unit can suggest an optimal search area based on places the user has frequently visited in the past. It can also suggest a search area that avoids congestion based on the user's past travel history. Furthermore, the location information setting unit can analyze the user's past travel history and suggest the most efficient search area. This improves user convenience by suggesting an optimal search area by referring to the user's past travel history. Some or all of the above processing in the location information setting unit may be performed using AI, for example, or without using AI.

[0107] The location information setting unit can customize the search range based on the user's current living situation when setting the location information. For example, if the user is raising children, the location information setting unit can prioritize including stores that handle children's products in the search range. Also, if the user is elderly, the location information setting unit can prioritize including barrier-free stores in the search range. Furthermore, if the user is a tourist, the location information setting unit can prioritize including stores around tourist destinations in the search range. By customizing the search range based on the user's current living situation, user convenience is improved. Some or all of the above processing in the location information setting unit may be performed using AI, for example, or without using AI.

[0108] The location information setting unit can estimate the user's emotions and determine the priority of the search range based on the estimated emotions. For example, if the user is feeling stressed, the location information setting unit can prioritize including the nearest store in the search range. Conversely, if the user is relaxed, the location information setting unit can include a wider range of stores in the search range. Furthermore, if the user is in a hurry, the location information setting unit can quickly set the search range based on the distance from the current location. This improves user convenience by determining the priority of the search range based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The location information setting unit can suggest an optimal search range considering the user's geographical location when setting location information. For example, the location information setting unit can prioritize including popular stores in the area where the user is currently located within the search range. Furthermore, if the user is near a specific store, the location information setting unit can prioritize including that store within the search range. Additionally, if the user is participating in an event related to a specific area, the location information setting unit can prioritize including stores related to that event within the search range. This improves user convenience by suggesting an optimal search range considering the user's geographical location. Some or all of the above processing in the location information setting unit may be performed using AI, for example, or without AI.

[0110] The location information setting unit can analyze the user's social media activity and adjust the search range when setting location information. For example, the location information setting unit can prioritize including stores that the user frequently mentions on social media in the search range. It can also prioritize including stores recommended by brands and influencers that the user follows on social media in the search range. Furthermore, the location information setting unit can prioritize including stores related to groups and events that the user participates in on social media in the search range. By analyzing the user's social media activity and adjusting the search range accordingly, user convenience is improved. Some or all of the above processing in the location information setting unit may be performed using AI, for example, or without using AI.

[0111] The update unit can estimate the user's emotions and adjust the frequency of inventory information updates based on the estimated emotions. For example, if the user is stressed, the update unit can frequently update inventory information to provide the latest information. If the user is relaxed, the update unit can provide inventory information at a normal update frequency. Furthermore, if the user is in a hurry, the update unit can update inventory information in real time to provide information quickly. This improves user convenience by adjusting the frequency of inventory information updates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The update unit can optimize its update algorithm by referring to past inventory data during updates. For example, the update unit can analyze past inventory data and increase the update frequency of high-demand products. It can also optimize the update timing of seasonal and limited-edition products based on past inventory data. Furthermore, the update unit can adjust the update frequency of products with a high risk of stockout by referring to past inventory data. This improves the accuracy of inventory information by optimizing the update algorithm by referring to past inventory data. Some or all of the above processes in the update unit may be performed using AI, for example, or without using AI.

[0113] The update unit can apply different update methods to each product category during the update process. For example, in the case of electronic devices, the update unit can apply an update method that emphasizes technical specifications and new product information. In the case of fashion items, the update unit can apply an update method that emphasizes trends and styles. Furthermore, in the case of food products, the update unit can apply an update method that emphasizes expiration dates and nutritional information. By applying different update methods to each product category, the accuracy of inventory information is improved. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI.

[0114] The update unit can estimate the user's emotions and adjust the timing of inventory information updates based on the estimated emotions. For example, if the user is stressed, the update unit can frequently update inventory information to provide the latest information. If the user is relaxed, the update unit can provide inventory information at the normal update timing. Furthermore, if the user is in a hurry, the update unit can update inventory information in real time to provide information quickly. This improves user convenience by adjusting the timing of inventory information updates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The update unit can weight update data based on the product submission date during the update process. For example, the update unit can prioritize updating recently received products. It can also prioritize updating products that users have previously searched for. Furthermore, for seasonal and limited-edition products, the update unit can adjust the weighting of update data based on the submission date. This improves the accuracy of inventory information by weighting update data based on the product submission date. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI.

[0116] The update unit can improve the accuracy of updates by referring to relevant market data for products during the update process. For example, the update unit can increase the update frequency of high-demand products based on relevant market data. It can also adjust the update frequency of products with a high risk of stockouts by referring to relevant market data. Furthermore, the update unit can optimize the update timing of seasonal and limited-edition products based on relevant market data. By improving the accuracy of updates by referring to relevant market data for products, the accuracy of inventory information is improved. Some or all of the above processes in the update unit may be performed using AI, for example, or without using AI.

[0117] The service provider can estimate the user's emotions and adjust how store information is displayed based on those emotions. For example, if the user is stressed, the service provider can display simple and easy-to-read store information. If the user is relaxed, the service provider can display store information that includes more detailed information. Furthermore, if the user is in a hurry, the service provider can prioritize displaying the most relevant store information. This improves user convenience by adjusting how store information is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The service provider can select the most suitable store information by referring to the user's past search history at the time of service provision. For example, the service provider can prioritize displaying store information that the user has frequently searched for in the past. Furthermore, the service provider can predict and suggest store information that the user may use at a specific time of day based on their past search history. In addition, the service provider can analyze the user's past search history and select the most relevant store information. This improves user convenience by selecting the most suitable store information by referring to the user's past search history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0119] The service provider can customize store information based on the user's current areas of interest at the time of delivery. For example, the service provider can prioritize displaying relevant store information based on the product categories the user has recently searched for. Furthermore, if the user is searching for products related to a specific event (e.g., birthday, Christmas), the service provider can prioritize displaying store information related to that event. Additionally, if the user is interested in a particular brand, the service provider can prioritize displaying store information that carries that brand. This improves user convenience by customizing store information based on the user's current areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0120] The service provider can estimate the user's emotions and adjust the display order of store information based on the estimated emotions. For example, if the user is feeling stressed, the service provider can prioritize displaying the most relevant store information. If the user is relaxed, the service provider can present multiple store options to broaden their choices. Furthermore, if the user is in a hurry, the service provider can quickly present store information based on their past search history. This improves user convenience by adjusting the display order of store information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The service provider can select the most suitable store information by considering the user's geographical location at the time of delivery. For example, the service provider can prioritize displaying information about popular stores in the area where the user is currently located. Furthermore, if the user is near a specific store, the service provider can prioritize displaying information about that store. In addition, if the user is participating in an event related to a specific area, the service provider can prioritize displaying information about stores related to that event. This improves user convenience by selecting the most suitable store information by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0122] The service provider can provide store information by analyzing the user's social media activity at the time of delivery. For example, the service provider can prioritize displaying store information that the user frequently mentions on social media. It can also prioritize displaying store information recommended by brands and influencers that the user follows on social media. Furthermore, the service provider can prioritize displaying store information related to groups and events that the user participates in on social media. This improves user convenience by providing store information based on an analysis of the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0123] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is feeling stressed, the notification unit can prioritize sending important notifications. If the user is relaxed, the notification unit can provide information at the normal notification timing. Furthermore, if the user is in a hurry, the notification unit can send notifications in real time to provide information quickly. This improves user convenience by adjusting notification timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0124] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can prioritize notification methods that the user has frequently received in the past (e.g., push notifications, email). Furthermore, the notification unit can predict and suggest the optimal notification method for a specific time period based on the user's past notification history. In addition, the notification unit can analyze the user's past notification history and select the most effective notification method. This improves user convenience by selecting the optimal notification method based on the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI.

[0125] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is stressed, the notification unit can prioritize sending important notifications. Conversely, if the user is relaxed, the notification unit can provide information with normal notification priorities. Furthermore, if the user is in a hurry, the notification unit can send notifications in real time to provide information quickly. This improves user convenience by prioritizing notifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0126] The notification unit can select the most suitable notification method when sending a notification, taking into account the user's geographical location. For example, the notification unit can prioritize notification methods popular in the user's current location. Furthermore, if the user is near a specific store, the notification unit can prioritize notification methods related to that store. Additionally, if the user is participating in an event related to a specific region, the notification unit can prioritize notification methods related to that event. This improves user convenience by selecting the most suitable notification method based on the user's geographical location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI.

[0127] The review section can estimate the user's emotions and adjust how reviews are displayed based on those emotions. For example, if the user is stressed, the review section can display simple, easy-to-read reviews. If the user is relaxed, the review section can display reviews with more detailed information. Furthermore, if the user is in a hurry, the review section can prioritize displaying the most relevant reviews. This improves user convenience by adjusting how reviews are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0128] The review section can select the optimal display method when displaying reviews by referring to the user's past review history. For example, the review section can prioritize displaying review formats that the user has frequently viewed in the past. Furthermore, the review section can predict and suggest the optimal display method for a specific time period based on the user's past review history. In addition, the review section can analyze the user's past review history and select the most effective display method. This improves user convenience by selecting the optimal display method by referring to the user's past review history. Some or all of the above processing in the review section may be performed using AI, for example, or without using AI.

[0129] The review section can estimate the user's emotions and adjust the display order of reviews based on the estimated emotions. For example, if the user is stressed, the review section can prioritize displaying the most relevant reviews. If the user is relaxed, the review section can present multiple reviews to broaden the options. Furthermore, if the user is in a hurry, the review section can quickly present reviews based on past review history. This improves user convenience by adjusting the display order of reviews based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0130] The review unit can select the optimal display method when displaying reviews, taking into account the user's device information. For example, if the user is using a smartphone, the review unit can provide a display method that matches the screen size. If the user is using a tablet, the review unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the review unit can provide a concise and highly visible display method. By selecting the optimal display method considering the user's device information, user convenience is improved. Some or all of the above processing in the review unit may be performed using AI, for example, or without using AI.

[0131] The recommendation system can estimate the user's emotions and adjust how recommended products are displayed based on those emotions. For example, if the user is stressed, the recommendation system can display simple, highly visible recommended products. If the user is relaxed, the recommendation system can display recommended products with more detailed information. Furthermore, if the user is in a hurry, the recommendation system can prioritize displaying the most relevant recommended products. This improves user convenience by adjusting how recommended products are displayed based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0132] The recommendation system can select the most suitable products by referring to the user's past purchase history when making recommendations. For example, the recommendation system can display relevant recommended products based on the product categories that the user has frequently purchased in the past. Furthermore, the recommendation system can predict and suggest the most suitable products for a specific time period based on the user's past purchase history. In addition, the recommendation system can analyze the user's past purchase history and select the most relevant recommended products. This improves user convenience by selecting the most suitable products based on the user's past purchase history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI.

[0133] The recommendation system can customize recommended products based on the user's current areas of interest. For example, it can display relevant recommended products based on product categories the user has recently searched for. It can also display recommended products related to a specific event (e.g., birthday, Christmas) if the user is searching for products related to that event. Furthermore, if the user is interested in a particular brand, the recommendation system can prioritize displaying products from that brand. This improves user convenience by customizing recommended products based on the user's current areas of interest. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not.

[0134] The recommendation system can estimate the user's emotions and adjust the display order of recommended products based on those emotions. For example, if the user is stressed, the recommendation system can prioritize displaying the most relevant recommended products. If the user is relaxed, the recommendation system can present multiple recommended products to broaden their choices. Furthermore, if the user is in a hurry, the recommendation system can quickly present recommended products based on their past purchase history. This improves user convenience by adjusting the display order of recommended products based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0135] The recommendation system can select the most suitable products by considering the user's geographical location. For example, it can prioritize displaying products that are popular in the user's current location. It can also prioritize displaying products available at a specific store if the user is near that store. Furthermore, if the user is participating in an event related to a specific region, it can prioritize displaying products related to that event. This improves user convenience by selecting the most suitable products based on the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI.

[0136] The recommendation system can analyze a user's social media activity to provide recommended products. For example, it can prioritize displaying products that the user frequently mentions on social media. It can also prioritize displaying products recommended by brands and influencers that the user follows on social media. Furthermore, it can prioritize displaying products related to groups and events that the user participates in on social media. This improves user convenience by providing recommended products based on an analysis of the user's social media activity. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI.

[0137] The management department can estimate the user's emotions and adjust the inventory information management method based on the estimated emotions. For example, if the user is stressed, the management department can frequently update inventory information to provide the latest information. If the user is relaxed, the management department can provide inventory information using the normal management method. Furthermore, if the user is in a hurry, the management department can update inventory information in real time to provide information quickly. This improves user convenience by adjusting the inventory information management method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0138] The management department can optimize its management algorithm by referring to past inventory data during management. For example, the management department can analyze past inventory data and increase the management frequency of high-demand products. Furthermore, based on past inventory data, the management department can optimize the management timing of seasonal and limited-edition products. In addition, the management department can adjust the management frequency of products with a high risk of stockout by referring to past inventory data. This improves the accuracy of inventory information by optimizing the management algorithm by referring to past inventory data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI.

[0139] The management department can estimate the user's emotions and adjust the frequency of inventory information management based on the estimated emotions. For example, if the user is stressed, the management department can frequently update inventory information to provide the latest information. If the user is relaxed, the management department can provide inventory information at the normal frequency. Furthermore, if the user is in a hurry, the management department can update inventory information in real time to provide information quickly. This improves user convenience by adjusting the frequency of inventory information management based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0140] The management department can weight management data based on the submission date of products during the management process. For example, the management department can prioritize the management of recently received products. It can also prioritize the management of products that users have previously searched for. Furthermore, for seasonal and limited-edition products, the management department can adjust the weighting of management data based on the submission date. This improves the accuracy of inventory information by weighting management data based on the submission date of products. Some or all of the above processes in the management department may be performed using AI, for example, or without AI.

[0141] The navigation system can estimate the user's emotions and adjust the way route guidance is displayed based on those emotions. For example, if the user is stressed, the navigation system can display simple and easy-to-read route guidance. If the user is relaxed, the navigation system can display route guidance with more detailed information. Furthermore, if the user is in a hurry, the navigation system can prioritize displaying the most relevant route guidance. This improves user convenience by adjusting the way route guidance is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0142] The guidance unit can suggest the optimal route by referring to the user's past travel history when providing directions. For example, the guidance unit can suggest the optimal route based on routes the user has frequently used in the past. Furthermore, the guidance unit can suggest routes that avoid congestion based on the user's past travel history. In addition, the guidance unit can analyze the user's past travel history and suggest the most efficient route. This improves user convenience by suggesting the optimal route based on the user's past travel history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI.

[0143] The navigation system can estimate the user's emotions and prioritize route guidance based on those emotions. For example, if the user is stressed, the navigation system can prioritize displaying the most relevant route guidance. If the user is relaxed, the navigation system can present multiple route guidance options to broaden their choices. Furthermore, if the user is in a hurry, the navigation system can quickly present route guidance based on past travel history. This improves user convenience by prioritizing route guidance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0144] The navigation system can suggest the optimal route while considering the user's geographical location. For example, it can prioritize displaying popular routes in the user's current location. It can also display the optimal route to a specific store if the user is near it. Furthermore, if the user is participating in an event related to a specific area, it can prioritize displaying routes related to that event. This improves user convenience by suggesting the optimal route while considering the user's geographical location. Some or all of the above processing in the navigation system may be performed using AI, for example, or without AI.

[0145] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0146] The reception system can suggest input options by referencing the user's past purchase history when they enter a specific product name or category. For example, it can automatically display related product names and categories as suggestions based on products and categories the user has previously purchased. The reception system can also prioritize displaying product names and categories the user has previously searched for. Furthermore, the reception system can analyze the user's past purchase history and suggest the most suitable product names and categories for a specific time period. This improves user convenience by suggesting optimal input options based on the user's past purchase history.

[0147] The notification unit can estimate the user's emotions and adjust the content of notifications based on those emotions. For example, if the user is stressed, the notification can be concise and provide only essential information. If the user is relaxed, a notification containing detailed information can be sent. Furthermore, if the user is in a hurry, the notification can be summarized for quick understanding. By adjusting notification content based on the user's emotions, user convenience is improved.

[0148] The review section can estimate the user's emotions and adjust how reviews are displayed based on that estimation. For example, if a user is stressed, simple, easy-to-read reviews can be displayed. If the user is relaxed, reviews with more detailed information can be displayed. Furthermore, if the user is in a hurry, the most relevant reviews can be prioritized. This improves user convenience by adjusting how reviews are displayed based on the user's emotions.

[0149] The recommendation system can estimate the user's emotions and adjust how recommended products are displayed based on those emotions. For example, if a user is stressed, it can display simple, highly visible recommended products. If a user is relaxed, it can display recommended products with more detailed information. Furthermore, if a user is in a hurry, it can prioritize displaying the most relevant recommended products. By adjusting how recommended products are displayed based on the user's emotions, the system improves user convenience.

[0150] The management department can estimate user emotions and adjust inventory management methods based on those estimates. For example, if a user is stressed, inventory information can be updated frequently to provide the latest information. If a user is relaxed, inventory information can be provided using the normal management method. Furthermore, if a user is in a hurry, inventory information can be updated in real time to provide information quickly. In this way, adjusting inventory management methods based on user emotions improves user convenience.

[0151] The navigation system can suggest the optimal route by referring to the user's past travel history. For example, it can suggest the best route based on routes the user has frequently used in the past. Furthermore, the navigation system can suggest routes that avoid congestion based on the user's past travel history. In addition, the navigation system can analyze the user's past travel history and suggest the most efficient route. This improves user convenience by suggesting the optimal route based on the user's past travel history.

[0152] The search function can apply different search algorithms depending on the product category. For example, for electronic devices, a search algorithm that emphasizes technical specifications and reviews can be applied. For fashion items, a search algorithm that emphasizes trends and style can be applied. Furthermore, for food products, a search algorithm that emphasizes expiration dates and nutritional information can be applied. By applying different search algorithms depending on the product category, user convenience is improved.

[0153] The service provider can customize store information based on the user's current areas of interest. For example, it can prioritize displaying relevant store information based on the product categories the user has recently searched for. Furthermore, if the user is searching for products related to a specific event (e.g., birthday, Christmas), it can prioritize displaying store information related to that event. Additionally, if the user is interested in a particular brand, it can prioritize displaying store information that carries that brand. This improves user convenience by customizing store information based on the user's current areas of interest.

[0154] The notification unit can select the most suitable notification method by considering the user's geographical location. For example, it can prioritize notification methods popular in the user's current location. Furthermore, if the user is near a specific store, it can prioritize notification methods related to that store. Additionally, if the user is participating in an event related to a specific region, it can prioritize notification methods related to that event. This improves user convenience by selecting the most suitable notification method based on the user's geographical location.

[0155] The review section can select the optimal display method considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, selecting the optimal display method considering the user's device information improves user convenience.

[0156] The following briefly describes the processing flow for example form 2.

[0157] Step 1: The reception desk receives the user's input of a specific product name or category. Users can input the product name or category using text, voice input, or image input. The reception desk receives this input and, if necessary, converts voice to text or analyzes images to identify the product name or category. Step 2: The search unit searches for nearby store information based on the information entered by the reception unit and displays the availability of the product. The search unit searches for nearby store information from the database and accesses the inventory database of each store to check the inventory status of the specified product. The search results are formatted and displayed in the user interface. Step 3: The location information setting unit sets the search range based on the current location. The location information setting unit obtains the user's current location using GPS and sets the search range based on that information. The user can also manually set the search range, or the search range can be automatically set based on past search history. Step 4: The update unit updates inventory information in real time. The update unit provides a management tool for each store to update inventory information and automatically updates inventory information regularly. It also has a function to notify in real time when there are changes in inventory information. Step 5: The service provider provides information about the stores displayed in the search results. The service provider provides users with store addresses, business hours, contact information, special offers, etc., and also includes functions to provide directions to the stores and display user reviews and ratings.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, search unit, location information setting unit, update unit, provision unit, notification unit, review unit, recommendation unit, management unit, and guidance unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the product name and category entered by the user. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for nearby store information and displays whether it is in stock. The location information setting unit is implemented by, for example, the control unit 46A of the smart device 14 and obtains the current location using GPS and sets the search range. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and updates the inventory information in real time. The provision unit is implemented by, for example, the control unit 46A of the smart device 14 and provides store information displayed as a search result. The notification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sends a notification to the user. The review section is implemented, for example, by the control unit 46A of the smart device 14, and displays user reviews and ratings. The recommendation section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides personalized product recommendations. The management section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides management tools for stores to update inventory information. The guidance section is implemented, for example, by the control unit 46A of the smart device 14, and provides route guidance to the store. The correspondence between each section and the devices and control units is not limited to the examples described above, and various changes are possible.

[0162] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reception unit, search unit, location information setting unit, update unit, provision unit, notification unit, review unit, recommendation unit, management unit, and guidance unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the product name and category entered by the user. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and searches for nearby store information and displays whether it is in stock. The location information setting unit is implemented, for example, by the control unit 46A of the smart glasses 214 and obtains the current location using GPS and sets the search range. The update unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and updates the inventory information in real time. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides store information displayed as a search result. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and sends a notification to the user. The review section is implemented, for example, by the control unit 46A of the smart glasses 214, and displays user reviews and ratings. The recommendation section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides personalized product recommendations. The management section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides management tools for stores to update inventory information. The guidance section is implemented, for example, by the control unit 46A of the smart glasses 214, and provides route guidance to the store. The correspondence between each section and the devices and control units is not limited to the examples described above, and various changes are possible.

[0178] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] Each of the multiple elements described above, including the reception unit, search unit, location information setting unit, update unit, provision unit, notification unit, review unit, recommendation unit, management unit, and guidance unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the product name and category entered by the user. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for nearby store information and displays whether it is in stock. The location information setting unit is implemented by, for example, the control unit 46A of the headset terminal 314 and obtains the current location using GPS and sets the search range. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and updates the inventory information in real time. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides store information displayed as a search result. The notification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sends a notification to the user. The review section is implemented, for example, by the control unit 46A of the headset terminal 314, and displays user reviews and ratings. The recommendation section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides personalized product recommendations. The management section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides management tools for stores to update inventory information. The guidance section is implemented, for example, by the control unit 46A of the headset terminal 314, and provides route guidance to the store. The correspondence between each section and the devices and control units is not limited to the examples described above, and various changes are possible.

[0194] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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).

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] Each of the multiple elements described above, including the reception unit, search unit, location information setting unit, update unit, provision unit, notification unit, review unit, recommendation unit, management unit, and guidance unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the product name and category entered by the user. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for nearby store information and displays whether or not it is in stock. The location information setting unit is implemented by, for example, the control unit 46A of the robot 414 and obtains the current location using GPS and sets the search range. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and updates the inventory information in real time. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides store information displayed as a search result. The notification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sends a notification to the user. The review section is implemented, for example, by the control unit 46A of the robot 414, and displays user reviews and ratings. The recommendation section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides personalized product recommendations. The management section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides management tools for stores to update inventory information. The guidance section is implemented, for example, by the control unit 46A of the robot 414, and provides route guidance to the store. The correspondence between each section and the devices and control units is not limited to the examples described above, and various changes are possible.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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."

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] (Note 1) A reception area where the user enters a specific product name or category, A search unit that searches for nearby store information based on the information entered by the reception unit and displays whether the items are in stock, A location information setting unit that sets the search range based on the current location, An update unit that updates inventory information in real time, The information provider department provides the store information displayed as a search result, Equipped with A system characterized by the following features. (Note 2) Equipped with a notification unit that has a notification function. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a review section that displays user reviews and ratings. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a recommendation department that analyzes purchase and search history to provide personalized product recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The store has a management department that provides management tools for updating inventory information. The system described in Appendix 1, characterized by the features described herein. (Note 6) It is equipped with a guidance unit that provides route guidance to the store. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for product names and categories based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past search history and presents the most suitable input suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering product names or categories, the system filters input suggestions based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter product names or categories, the system prioritizes displaying highly relevant suggestions by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter product names or categories, the system analyzes their social media activity and suggests relevant options. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When searching, adjust the level of detail in search results based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When searching, different search algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, It estimates the user's sentiment and adjusts the display order of search results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When searching, search results are prioritized based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, the order of search results is adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned location information setting unit is It estimates the user's emotions and adjusts the search scope based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned location information setting unit is When setting location information, the system suggests the optimal search area by referencing the user's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned location information setting unit is When setting location information, the search range is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned location information setting unit is It estimates the user's emotions and determines the search scope priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned location information setting unit is When setting location information, the system suggests the optimal search range considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned location information setting unit is When setting location information, the search range is adjusted by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update unit is, The system estimates user sentiment and adjusts the frequency of inventory information updates based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is, During updates, the update algorithm is optimized by referring to past inventory data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned update unit is, When updating, different update methods are applied to each product category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned update unit is, The system estimates user sentiment and adjusts the timing of inventory information updates based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned update unit is, During updates, update data is weighted based on the product submission date. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned update unit is, During updates, we refer to relevant market data for the product to improve the accuracy of the updates. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how store information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, the system selects the most suitable store by referring to the user's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the service, store information is customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 34) The providing unit estimates the user's emotion and adjusts the display order of store information based on the estimated user emotion The system according to Addendum 1, characterized in that. (Addendum 35) The providing unit selects optimal store information in consideration of the user's geographical location information at the time of provision The system according to Addendum 1, characterized in that. (Addendum 36) The providing unit analyzes the user's social media activities at the time of provision and provides store information The system according to Addendum 1, characterized in that. (Addendum 37) The notification unit estimates the user's emotion and adjusts the timing of notification based on the estimated user emotion <00​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​When displaying reviews, the system selects the optimal display method by referring to the user's past review history. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned review section, It estimates user sentiment and adjusts the display order of reviews based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned review section, When displaying reviews, the system selects the optimal display method considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned recommendation department, The system estimates the user's emotions and adjusts how recommended products are displayed based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned recommendation department, When making recommendations, the system selects the most suitable products by referring to the user's past purchase history. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned recommendation department, When making recommendations, customize the recommended products based on the user's current areas of interest. The system described in Appendix 4, characterized by the features described herein. (Note 48) The aforementioned recommendation department, It estimates the user's emotions and adjusts the display order of recommended products based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 49) The aforementioned recommendation department, When making recommendations, the system selects the most suitable products by taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 50) The aforementioned recommendation department, When making recommendations, analyze the user's social media activities to provide recommended products The system according to Appendix 4, characterized by this (Appendix 51) The management department Estimate the user's emotion and adjust the inventory information management method based on the estimated user emotion The system according to Appendix 5, characterized by this (Appendix 52) The management department When managing, optimize the management algorithm by referring to past inventory data The system according to Appendix 5, characterized by this (Appendix 53) The management department Estimate the user's emotion and adjust the inventory information management frequency based on the estimated user emotion The system according to Appendix 5, characterized by this (Appendix 54) The management department When managing, perform weighting of management data based on the product submission time The system according to Appendix 5, characterized by this (Appendix 55) The guidance department Estimate the user's emotion and adjust the route guidance display method based on the estimated user emotion The system according to Appendix 6, characterized by this (Appendix 56) The guidance department When guiding, propose an optimal route by referring to the user's past movement history The system according to Appendix 6, characterized by this (Appendix 57) The guidance department Estimate the user's emotion and determine the priority of route guidance based on the estimated user emotion The system according to Appendix 6, characterized by this (Appendix 58) The guidance department When providing directions, the system will suggest the optimal route, taking into account the user's geographical location. The system described in Appendix 6, characterized by the features described herein. [Explanation of Symbols]

[0230] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where the user enters a specific product name or category, A search unit that searches for nearby store information based on the information entered by the reception unit and displays whether the items are in stock, A location information setting unit that sets the search range based on the current location, An update unit that updates inventory information in real time, The information provider department provides the store information displayed as a search result, Equipped with A system characterized by the following features.

2. Equipped with a notification unit that has a notification function. The system according to feature 1.

3. It includes a review section that displays user reviews and ratings. The system according to feature 1.

4. It features a recommendation department that analyzes purchase and search history to provide personalized product recommendations. The system according to feature 1.

5. The store has a management department that provides management tools for updating inventory information. The system according to feature 1.

6. It is equipped with a guidance unit that provides route guidance to the store. The system according to feature 1.

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

8. The aforementioned reception unit is It analyzes the user's past search history and presents the most suitable input suggestions. The system according to feature 1.

9. The aforementioned reception unit is When entering product names or categories, the system filters input suggestions based on the user's current areas of interest. The system according to feature 1.

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

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