system

Abstract

The system according to this embodiment aims to enable users to efficiently check the inventory status of desired products and provide route guidance to the store. [Solution] The system according to the embodiment comprises a reception unit, a search unit, a provision unit, a guidance unit, and a notification unit. The reception unit receives input from the user for 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 provision unit provides store information based on the search results obtained by the search unit. The guidance unit provides route guidance to the store based on the information provided by the provision unit. The notification unit sends a notification when the product specified by the user arrives in stock or when there is special sale information.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is necessary to visit a plurality of stores to check the inventory status of desired products, which takes time and effort.

[0005] The system according to the embodiment aims to efficiently check the inventory status of products desired by a user and provide route guidance to stores.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a search unit, a provision unit, a guidance unit, and a notification 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 provision unit provides store information based on the search results obtained by the search unit. The guidance unit provides route guidance to the store based on the information provided by the provision unit. The notification unit sends a notification when the product specified by the user is in stock or when there is special sale information. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently check the inventory status of the product the user wants and provide route guidance to the store. [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 based on the user's input of a specific product name or category. For example, the user inputs a specific product name or category such as "cold medicine" or "smartphone." This information is input into the AI. Next, the AI ​​analyzes the input information, performs a search within a specified distance from the user's current location, and displays availability. For example, if the user searches for "cold medicine," the system displays inventory information for nearby pharmacies. In this process, GPS is used to acquire real-time location information and display the stores that carry the product. The search results provide the store's address, business hours, contact information, and special offer information. For example, if the user searches for "cold medicine," the system displays the address, business hours, contact information, and special offer information of nearby pharmacies. The system also includes a route guidance function to help the user reach the store via the shortest route. Furthermore, it has a function to send notifications when a product specified by the user arrives in stock at a nearby store or when there is a special offer. For example, if the user searches for "cold medicine" and it is out of stock, they can receive a notification when it becomes available again. Furthermore, users can receive notifications when there are special offers. This system allows users to quickly find out where the products they want are sold, saving them the trouble of checking stock availability. In emergencies, it can also display the nearest store and guide users on the shortest route, significantly reducing search time. In addition, users can improve their shopping experience by checking special offers and user reviews. By utilizing AI, it can analyze purchase and search history to provide personalized product recommendations. As a result, the AI ​​app can efficiently find the products users want and provide stock checks and store directions.

[0029] The AI ​​application according to this embodiment comprises a reception unit, a search unit, a provision unit, a guidance unit, and a notification unit. The reception unit receives input from the user for a specific product name or category. When the user enters a specific product name or category, they can enter specific product names or categories such as food, clothing, or home appliances. The reception unit can receive input for product names and categories using methods such as text input, voice input, or image recognition. 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 search unit can, for example, perform a search within a specified distance from the current location and display whether the items are in stock. The search unit can acquire location information in real time using GPS and display stores that carry the product. The search unit can, for example, provide real-time inventory information and perform periodic inventory updates. The provision unit provides store information based on the search results obtained by the search unit. The provision unit can, for example, provide the store's address, business hours, contact information, special sale information, etc., as displayed in the search results. The provision unit can, for example, provide information such as the store's address, business hours, contact information, and special sale information. The guidance unit provides route directions to the store based on the information provided by the service unit. The guidance unit can provide route directions such as the shortest route or a route that takes traffic conditions into consideration. The guidance unit can provide route directions using methods such as voice guidance, visual guidance, and real-time updates. The notification unit sends notifications when a product specified by the user is in stock or when there is special sale information. The notification unit can send notifications using methods such as email notifications, push notifications, and notification timing. This allows the AI ​​app to efficiently find the products the user wants and to check inventory and provide store directions.

[0030] The input system allows users to enter specific product names or categories. Users can enter specific product names or categories such as food, clothing, or home appliances. The input system can accept product names and categories using methods such as text input, voice input, and image recognition. Specifically, with text input, users use a keyboard to enter product names or categories. With voice input, users speak the product name or category into a microphone, and voice recognition technology converts it into text. With image recognition, users photograph the product or its packaging with a camera, and image recognition technology identifies the product name or category. This allows users to input product information in the most convenient way for them. Furthermore, the input system can save the user's input history and provide suggestion features to simplify future input. For example, it can automatically display previously searched product names or categories, saving the user the trouble of re-entering them. The input system can also analyze the user's input and suggest related products and categories. This allows users to find their desired products more efficiently.

[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 within a specified distance from the user's current location and display the availability of stock. The search unit can use GPS to obtain location information in real time and display stores that carry the product. Specifically, it searches the store information in the database based on the product name and category entered by the user and lists the relevant stores. The search results display the stock status of each store, allowing the user to see at a glance which stores have the product in stock. The search unit can provide real-time stock information and perform periodic stock updates. For example, by linking with the stock database of each store and updating stock information regularly, it can always provide the latest information. In addition, the search unit can prioritize displaying the nearest store based on the user's current location. This allows the user to obtain the desired product in the shortest possible time. Furthermore, the search unit can suggest related products and stores based on the user's past search history and purchase history. This increases the user's opportunities to discover new products and stores, improving the purchasing experience.

[0032] The information provider unit provides store information based on the search results obtained by the search unit. For example, the information provider unit provides the address, business hours, contact information, and special offers information of the stores displayed in the search results. Specifically, it displays detailed information of the store selected by the user, allowing the user to see the information they need at a glance. For example, the store address is displayed on a map, allowing the user to confirm the store's exact location. Since business hours may vary depending on the day of the week, detailed business hours information is provided. Contact information such as a phone number and email address is displayed, allowing the user to contact the store directly. Special offers information displays details of current sales and campaigns, allowing users to purchase products at a better price. Furthermore, the information provider unit can also provide customized information according to the user's preferences. For example, if the user is interested in a particular brand or category, store information related to that brand or category will be displayed preferentially. In addition, the information provider unit can suggest the nearest or easily accessible stores based on the user's location information. This allows users to efficiently obtain store information and proceed smoothly with their purchasing actions.

[0033] The guidance unit provides route guidance to the store based on information provided by the service unit. The guidance unit can provide route guidance such as the shortest route or a route that takes traffic conditions into account. Specifically, it calculates the optimal route from the user's current location to the target store and displays it on a map. The guidance unit acquires real-time traffic information and optimizes the route considering information such as congestion and construction. This allows the user to reach their destination in the shortest possible time. The guidance unit can provide route guidance using methods such as voice guidance, visual guidance, and real-time updates. Voice guidance allows users to receive route guidance without taking their eyes off the road, even while driving or walking. Visual guidance displays the route on a map, allowing users to visually confirm the route. Real-time updates automatically recalculate the route in response to changes in traffic conditions, always providing the optimal route. Furthermore, the guidance unit can also provide route guidance tailored to the user's mode of transportation. For example, it calculates the optimal route for each mode of transportation, such as car, bicycle, or walking, and provides guidance methods appropriate for each. This allows users to receive route guidance that is most suitable for their situation, ensuring a smooth journey to their destination.

[0034] The notification unit sends notifications when a user-specified product is in stock or when there is special sale information. The notification unit can send notifications using methods such as email notifications, push notifications, and notification timing. Specifically, when a user-specified product is in stock, it immediately sends a notification to inform the user. Email notifications send detailed information to the user's email address, while push notifications immediately display the notification on the smartphone screen. Notification timing can be customized according to the user's settings. For example, users can choose to receive notifications at specific times or only when specific conditions are met, allowing for flexible settings to meet user needs. Furthermore, the notification unit can also suggest related products and special sale information based on the user's purchase and search history. This allows users to receive information that matches their interests and needs in a timely manner. In addition, the notification unit can combine multiple notification methods to ensure that important information reaches the user. For example, by using push notifications and email notifications together, users can be sure to receive important information without missing any notifications. This allows the notification unit to help users efficiently find the products they want and proceed smoothly with their purchasing actions.

[0035] The recommendation system uses AI to analyze users' purchase and search history to provide personalized product recommendations. For example, the recommendation system recommends products based on the user's past purchase history, search history, and interests. It can use recommendation algorithms such as collaborative filtering and content-based filtering to recommend products. For example, it can recommend products similar to those the user has previously purchased. It can also recommend highly relevant products based on the user's past purchase history. Furthermore, it can analyze the user's past purchase history to recommend products related to specific seasons or events. This enables personalized product recommendations based on the user's purchase and search history.

[0036] The Management Department provides management tools for stores to update inventory information. The Management Department provides management tools with functions such as adding and deleting inventory, changing inventory quantities, and real-time updates. For example, the Management Department can add and delete inventory. The Management Department can also change inventory quantities. Furthermore, the Management Department can update inventory information in real time. This allows stores to efficiently manage inventory information and provide accurate information to users.

[0037] The review section displays user reviews and ratings. For example, the review section displays information such as star ratings, text reviews, and review reliability. The review section can display star ratings, text reviews, and review reliability. This allows users to choose products based on reviews and ratings from other users.

[0038] The search unit can perform a search within a specified distance from the user's current location and display whether the item is in stock. For example, the search unit can perform a search within a specified distance from the user's current location and display whether the item is in stock. The search unit can perform a search within a range such as a radius of several kilometers or a walking distance of several minutes. This allows the user to check the availability of an item within a specified distance from their current location.

[0039] The service provider can provide information such as the store's address, business hours, contact information, and special offers displayed in the search results. For example, the service provider can provide information such as the store's address, business hours, contact information, and special offers displayed in the search results. For example, the service provider can provide information such as the store's address, business hours, contact information, and special offers. For example, the service provider can provide information such as the store's address, business hours, contact information, and special offers displayed in the search results. This allows users to view detailed information about the store.

[0040] The guidance unit can guide users to the shortest route to the store. For example, the guidance unit can guide users to the shortest route to the store. The guidance unit can provide route guidance such as the shortest route or a route that takes traffic conditions into consideration. This allows users to reach the store via the shortest route.

[0041] The notification unit can send notifications when a product specified by the user arrives in a nearby store or when there is special sale information. For example, the notification unit can send notifications when a product specified by the user arrives in a nearby store or when there is special sale information. The notification unit can send notifications using methods such as email notifications, push notifications, and notification timing. For example, the notification unit can send notifications when a product specified by the user arrives in a nearby store or when there is special sale information. The notification unit can send notifications using methods such as email notifications, push notifications, and notification timing. This allows users to receive immediate notifications about the arrival of specified products and special sales.

[0042] 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. For example, the reception desk can prioritize suggesting input methods that the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest product names and categories that the user will use at a specific time of day based on their past search history. This allows the system to present the most suitable input options based on the user's past search history.

[0043] The reception desk can improve the convenience of input by using voice input and image recognition when entering product names and categories. For example, the reception desk can allow users to input product names and categories by voice, saving them time. For example, the reception desk can use AI to automatically recognize product names and categories when a user uploads an image of a product. For example, the reception desk can automatically input product names and categories when a user scans a barcode. In this way, the effort required for input can be reduced by using voice input and image recognition.

[0044] The input system can prioritize displaying highly relevant suggestions when users enter product names or categories, taking into account their geographical location. For example, the input system can prioritize displaying products available at nearby stores based on the user's current location. For example, the input system can prioritize displaying products from stores the user frequently visits, based on the user's past location information. For example, the input system can prioritize displaying popular products in a specific region based on the user's geographical location information. This allows the system to prioritize displaying highly relevant products based on the user's geographical location.

[0045] The reception desk can analyze the user's social media activity when they enter product names and categories, and suggest relevant products. For example, the reception desk can suggest related products based on products the user has "liked" on social media. For example, the reception desk can prioritize displaying products from brands the user follows on social media. For example, the reception desk can analyze the content of the user's social media posts and suggest products they might be interested in. In this way, it can suggest relevant products based on the user's social media activity.

[0046] The search engine can rank search results based on factors such as product popularity and ratings. For example, it can rank results based on the number of reviews and ratings. It can also prioritize displaying highly popular products based on sales data, or prioritize displaying highly relevant products based on a user's past rating history. This allows search results to be ranked based on product popularity and ratings.

[0047] The search unit can apply different search algorithms to each product category during a search. For example, in the food category, the search unit applies a search algorithm that takes into account expiration dates and nutritional information. In the electronics category, for example, the search unit applies a search algorithm that emphasizes specifications and functions. In the fashion category, for example, the search unit applies a search algorithm that takes into account trends and styles. By applying a search algorithm appropriate to the product category, the search accuracy is improved.

[0048] The search function can prioritize displaying highly relevant stores by considering the user's geographical location during a search. For example, the search function can prioritize displaying nearby stores based on the user's current location. For example, the search function can prioritize displaying frequently visited stores by referring to the user's past location information. For example, the search function can prioritize displaying popular stores in a specific area based on the user's geographical location information. This allows the system to prioritize displaying highly relevant stores based on the user's geographical location.

[0049] The search function can analyze a user's social media activity during a search and suggest relevant stores. For example, it can suggest stores based on the stores a user has "liked" on social media. For example, it can prioritize displaying stores that a user follows on social media. For example, it can analyze the content of a user's social media posts and suggest stores that might be of interest. In this way, it can suggest relevant stores based on the user's social media activity.

[0050] The information provider can adjust the level of detail of the information provided, taking into account the store's rating and reviews. For example, if a store has a high rating, the provider will provide detailed information. For example, if a store has many reviews, the provider will provide detailed information. For example, if a store has few ratings or reviews, the provider will provide only basic information. This allows the level of detail of the information to be adjusted based on the store's rating and reviews.

[0051] The information provisioning department can apply different information provision algorithms depending on the store category when providing information. For example, in the food category, the department applies an information provision algorithm that emphasizes expiration dates and nutritional information. For example, in the electronics category, the department applies an information provision algorithm that emphasizes specifications and functions. For example, in the fashion category, the department applies an information provision algorithm that emphasizes trends and styles. By applying an information provision algorithm appropriate to the store category, the accuracy of information provision is improved.

[0052] The service provider can prioritize displaying highly relevant store information by considering the user's geographical location at the time of delivery. For example, the service provider can prioritize displaying nearby store information based on the user's current location. For example, the service provider can prioritize displaying frequently visited store information by referring to the user's past location information. For example, the service provider can prioritize displaying popular store information in a specific area based on the user's geographical location information. This allows the service provider to prioritize displaying highly relevant store information based on the user's geographical location.

[0053] The service provider can analyze the user's social media activity and suggest relevant store information at the time of delivery. For example, the service provider can suggest relevant store information based on stores that the user has "liked" on social media. For example, the service provider can prioritize displaying store information that the user follows on social media. For example, the service provider can analyze the content of the user's social media posts and suggest store information that they might be interested in. In this way, relevant store information can be suggested based on the user's social media activity.

[0054] The guidance system can suggest the optimal route by considering traffic conditions and weather information. For example, it can suggest the optimal route based on real-time traffic congestion information. For example, it can suggest a route with a roof in rainy weather by considering real-time weather information. For example, it can suggest the optimal route by considering the real-time operating status of public transportation. In this way, it can suggest the optimal route based on traffic conditions and weather information.

[0055] The guidance unit can apply different route guidance algorithms depending on the user's mode of transportation. For example, when guiding pedestrians, the guidance unit prioritizes sidewalks and crosswalks. For example, when guiding cyclists, the guidance unit prioritizes bicycle paths. For example, when guiding car users, the guidance unit prioritizes routes that avoid traffic congestion. By applying a route guidance algorithm tailored to the user's mode of transportation, the accuracy of the guidance is improved.

[0056] The navigation system can prioritize displaying the optimal route by considering the user's geographical location. For example, it can prioritize displaying the optimal route based on the user's current location. For example, it can prioritize displaying frequently used routes by referring to the user's past location information. For example, it can prioritize displaying popular routes in a particular area based on the user's geographical location information. This allows the system to prioritize displaying the optimal route based on the user's geographical location information.

[0057] The navigation system can analyze a user's social media activity and suggest relevant routes during navigation. For example, it can suggest relevant routes based on places the user has "liked" on social media. For example, it can prioritize displaying places the user follows on social media. For example, it can analyze the content of a user's social media posts and suggest routes that might interest them. In this way, it can suggest relevant routes based on the user's social media activity.

[0058] The notification system can adjust notification content based on product popularity and ratings. For example, it can immediately send a notification when a popular product is in stock. For example, it can send a notification when a highly-rated product goes on sale. For example, it can send a notification when a product that a user has previously given a high rating to is in stock. This allows the notification content to be adjusted based on product popularity and ratings.

[0059] The notification unit can apply different notification algorithms to each product category when sending notifications. For example, in the food category, the notification unit prioritizes notifying users of products nearing their expiration date. In the electronics category, for example, the notification unit prioritizes notifying users of new product arrival information. In the fashion category, for example, the notification unit prioritizes notifying users of sales information. By applying a notification algorithm appropriate to the product category, the accuracy of notifications is improved.

[0060] The notification unit can prioritize sending highly relevant notifications by considering the user's geographical location. For example, the notification unit can prioritize sending notifications about new stock at nearby stores based on the user's current location. For example, the notification unit can prioritize sending notifications about frequently visited stores by referring to the user's past location information. For example, the notification unit can prioritize sending notifications about popular products in a specific region based on the user's geographical location information. This allows the notification unit to prioritize sending highly relevant notifications based on the user's geographical location information.

[0061] The notification unit can analyze the user's social media activity and suggest relevant notifications when sending a notification. For example, the notification unit can send a notification when a product that the user "liked" on social media becomes available. For example, the notification unit can send a notification when a product from a brand that the user follows on social media becomes available. For example, the notification unit can analyze the user's social media posts and send notifications about products that might interest them. This allows the system to suggest relevant notifications based on the user's social media activity.

[0062] The recommendation system can analyze a user's past purchase history to recommend the most suitable products. For example, it can recommend products similar to those the user has previously purchased. For example, it can recommend highly relevant products based on the user's past purchase history. For example, it can analyze the user's past purchase history to recommend products related to a specific season or event. This allows the system to recommend the most suitable products based on the user's past purchase history.

[0063] The recommendation system can apply recommendation algorithms based on the user's current areas of interest during the recommendation process. For example, the recommendation system can recommend relevant products based on the product categories the user has recently searched for. For example, the recommendation system can recommend relevant products based on the products the user has recently viewed. For example, the recommendation system can recommend relevant products based on the products the user has recently purchased. This allows the system to recommend the most suitable products based on the user's current areas of interest.

[0064] The recommendation system can prioritize recommending highly relevant products by considering the user's geographical location. For example, it can prioritize recommending products available at nearby stores based on the user's current location. For example, it can prioritize recommending products at stores the user frequently visits by referring to the user's past location data. For example, it can prioritize recommending popular products in a specific region based on the user's geographical location. This allows the system to prioritize recommending highly relevant products based on the user's geographical location.

[0065] The recommendation team can analyze a user's social media activity and suggest relevant products when making recommendations. For example, the recommendation team can recommend products based on what the user has "liked" on social media. For example, the recommendation team can prioritize recommending products from brands that the user follows on social media. For example, the recommendation team can analyze the content of a user's social media posts and recommend products that they might be interested in. In this way, relevant products can be suggested based on the user's social media activity.

[0066] The management department can provide automated tools for updating store inventory information in real time. For example, the management department provides automated tools to eliminate the need for stores to manually update inventory information. For example, the management department updates store inventory information in real time, providing users with accurate information. For example, the management department automatically collects store inventory information and reflects it in the system. This eliminates the need for stores to manually update inventory information and allows for the provision of accurate information in real time.

[0067] The management department can optimize inventory management by analyzing store sales data during management. For example, the management department can optimize the timing of inventory replenishment based on store sales data. For example, the management department can analyze store sales data and prioritize securing inventory of high-demand products. For example, the management department can predict inventory surpluses and shortages based on store sales data and implement optimal inventory management. In this way, inventory management can be optimized based on store sales data.

[0068] The management department can prioritize inventory when updating store inventory information, taking into account user purchase history. For example, the management department can prioritize securing inventory of high-demand items based on user purchase history. For example, the management department can analyze user purchase history and prioritize securing inventory of items related to specific seasons or events. For example, the management department can optimize inventory replenishment timing based on user purchase history. This allows for prioritizing the securing of inventory of high-demand items based on user purchase history.

[0069] The management department can optimize inventory allocation by considering the geographical location of stores during management. For example, the management department can prioritize the allocation of inventory for high-demand products based on the geographical location of stores. For example, the management department can prioritize the allocation of inventory for products popular in a particular region by considering the geographical location of stores. For example, the management department can optimize the timing of inventory replenishment based on the geographical location of stores. This allows for the priority allocation of inventory for high-demand products based on the geographical location of stores.

[0070] The review section can adjust the level of detail in reviews based on the product's rating and popularity. For example, if a product has a high rating, the review section will display detailed reviews. For example, if a product has many reviews, the review section will display detailed reviews. For example, if a product has few ratings or reviews, the review section will display only basic information. This allows the level of detail in reviews to be adjusted based on the product's rating and popularity.

[0071] The review department can apply different review display algorithms depending on the product category during the review process. For example, in the food category, the review department applies a review display algorithm that emphasizes expiration date and nutritional information. For example, in the electronics category, the review department applies a review display algorithm that emphasizes specifications and functions. For example, in the fashion category, the review department applies a review display algorithm that emphasizes trends and style. By applying a review display algorithm appropriate to the product category, the accuracy of reviews is improved.

[0072] The review section can prioritize displaying highly relevant reviews by considering the user's geographical location during the review process. For example, the review section can prioritize displaying reviews of nearby stores based on the user's current location. For example, the review section can prioritize displaying reviews of frequently visited stores by referring to the user's past location information. For example, the review section can prioritize displaying reviews of popular products in a specific region based on the user's geographical location information. This allows the system to prioritize displaying highly relevant reviews based on the user's geographical location.

[0073] The review section can analyze a user's social media activity during the review process and suggest relevant reviews. For example, the review section can display reviews related to products that the user has "liked" on social media. For example, the review section can prioritize displaying product reviews of brands that the user follows on social media. For example, the review section can analyze a user's social media posts and suggest reviews of products they might be interested in. This allows the review section to suggest relevant reviews based on the user's social media activity.

[0074] The review section can prioritize displaying highly relevant reviews by considering the user's purchase history during the review process. For example, the review section can prioritize displaying reviews of products purchased by the user based on their purchase history. For example, the review section can analyze the user's purchase history and prioritize displaying reviews of highly relevant products. For example, the review section can prioritize displaying reviews of products related to a specific season or event based on the user's purchase history. This allows the system to prioritize displaying highly relevant reviews based on the user's purchase history.

[0075] The review section can prioritize displaying highly relevant reviews by considering the user's search history during the review process. For example, the review section can prioritize displaying reviews of products the user has searched for based on their search history. For example, the review section can analyze the user's search history and prioritize displaying reviews of highly relevant products. For example, the review section can prioritize displaying reviews of products related to a specific season or event based on the user's search history. This allows the system to prioritize displaying highly relevant reviews based on the user's search history.

[0076] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0077] The search engine can rank search results based on product popularity and ratings. For example, it can rank search results based on the number of reviews and ratings. It can also prioritize displaying popular products based on sales data. Furthermore, it can prioritize displaying highly relevant products by referring to the user's past rating history. This allows for ranking search results based on product popularity and ratings.

[0078] The information provider can adjust the level of detail based on the store's rating and reviews when providing information. For example, if a store has a high rating, detailed information can be provided. Similarly, if a store has many reviews, detailed information can also be provided. Furthermore, if a store has few ratings or reviews, only basic information can be provided. This allows the level of detail of information to be adjusted based on the store's rating and reviews.

[0079] The guidance system can suggest the optimal route by considering traffic conditions and weather information. For example, it can suggest the best route based on real-time traffic congestion information. It can also suggest a route with a roof in case of rain by considering real-time weather information. Furthermore, it can suggest the best route by considering the real-time operating status of public transportation. In this way, it can suggest the optimal route based on traffic conditions and weather information.

[0080] The notification system can adjust notification content based on product popularity and ratings. For example, it can send an immediate notification when a popular product is back in stock. It can also send a notification when a highly-rated product goes on sale. Furthermore, it can send a notification when a product that a user has previously given a high rating to becomes available again. This allows the system to adjust notification content based on product popularity and ratings.

[0081] The recommendation system can apply recommendation algorithms based on the user's current areas of interest. For example, it can recommend relevant products based on the product categories the user has recently searched for. It can also recommend relevant products based on the products the user has recently viewed. Furthermore, it can recommend relevant products based on the products the user has recently purchased. This allows for the recommendation of the most suitable products based on the user's current areas of interest.

[0082] The following briefly describes the processing flow for example form 1.

[0083] Step 1: The reception desk receives input from the user regarding specific product names or categories. Users can input specific product names or categories such as food, clothing, or home appliances. The reception desk can receive product names or categories using methods such as text input, voice input, or image recognition. Step 2: 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 search unit performs a search within a specified distance from the current location and displays whether the item is in stock. The search unit can use GPS to obtain location information in real time and display stores that carry the item. The search unit can provide real-time inventory information and perform periodic inventory updates. Step 3: The service provider provides store information based on the search results obtained by the search unit. The service provider provides the store's address, business hours, contact information, special offers, etc., as displayed in the search results. Step 4: The guidance unit provides route guidance to the store based on the information provided by the service unit. The guidance unit can provide route guidance such as the shortest route or a route that takes traffic conditions into consideration. The guidance unit can provide route guidance using methods such as voice guidance, visual guidance, and real-time updates. Step 5: The notification unit sends notifications when a product specified by the user is in stock or when there is special sale information. The notification unit can send notifications using methods such as email notifications, push notifications, and notification timing.

[0084] (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 based on the user's input of a specific product name or category. For example, the user inputs a specific product name or category such as "cold medicine" or "smartphone." This information is input into the AI. Next, the AI ​​analyzes the input information, performs a search within a specified distance from the user's current location, and displays availability. For example, if the user searches for "cold medicine," the system displays inventory information for nearby pharmacies. In this process, GPS is used to acquire real-time location information and display the stores that carry the product. The search results provide the store's address, business hours, contact information, and special offer information. For example, if the user searches for "cold medicine," the system displays the address, business hours, contact information, and special offer information of nearby pharmacies. The system also includes a route guidance function to help the user reach the store via the shortest route. Furthermore, it has a function to send notifications when a product specified by the user arrives in stock at a nearby store or when there is a special offer. For example, if the user searches for "cold medicine" and it is out of stock, they can receive a notification when it becomes available again. Furthermore, users can receive notifications when there are special offers. This system allows users to quickly find out where the products they want are sold, saving them the trouble of checking stock availability. In emergencies, it can also display the nearest store and guide users on the shortest route, significantly reducing search time. In addition, users can improve their shopping experience by checking special offers and user reviews. By utilizing AI, it can analyze purchase and search history to provide personalized product recommendations. As a result, the AI ​​app can efficiently find the products users want and provide stock checks and store directions.

[0085] The AI ​​application according to this embodiment comprises a reception unit, a search unit, a provision unit, a guidance unit, and a notification unit. The reception unit receives input from the user for a specific product name or category. When the user enters a specific product name or category, they can enter specific product names or categories such as food, clothing, or home appliances. The reception unit can receive input for product names and categories using methods such as text input, voice input, or image recognition. 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 search unit can, for example, perform a search within a specified distance from the current location and display whether the items are in stock. The search unit can acquire location information in real time using GPS and display stores that carry the product. The search unit can, for example, provide real-time inventory information and perform periodic inventory updates. The provision unit provides store information based on the search results obtained by the search unit. The provision unit can, for example, provide the store's address, business hours, contact information, special sale information, etc., as displayed in the search results. The provision unit can, for example, provide information such as the store's address, business hours, contact information, and special sale information. The guidance unit provides route directions to the store based on the information provided by the service unit. The guidance unit can provide route directions such as the shortest route or a route that takes traffic conditions into consideration. The guidance unit can provide route directions using methods such as voice guidance, visual guidance, and real-time updates. The notification unit sends notifications when a product specified by the user is in stock or when there is special sale information. The notification unit can send notifications using methods such as email notifications, push notifications, and notification timing. This allows the AI ​​app to efficiently find the products the user wants and to check inventory and provide store directions.

[0086] The input system allows users to enter specific product names or categories. Users can enter specific product names or categories such as food, clothing, or home appliances. The input system can accept product names and categories using methods such as text input, voice input, and image recognition. Specifically, with text input, users use a keyboard to enter product names or categories. With voice input, users speak the product name or category into a microphone, and voice recognition technology converts it into text. With image recognition, users photograph the product or its packaging with a camera, and image recognition technology identifies the product name or category. This allows users to input product information in the most convenient way for them. Furthermore, the input system can save the user's input history and provide suggestion features to simplify future input. For example, it can automatically display previously searched product names or categories, saving the user the trouble of re-entering them. The input system can also analyze the user's input and suggest related products and categories. This allows users to find their desired products more efficiently.

[0087] 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 within a specified distance from the user's current location and display the availability of stock. The search unit can use GPS to obtain location information in real time and display stores that carry the product. Specifically, it searches the store information in the database based on the product name and category entered by the user and lists the relevant stores. The search results display the stock status of each store, allowing the user to see at a glance which stores have the product in stock. The search unit can provide real-time stock information and perform periodic stock updates. For example, by linking with the stock database of each store and updating stock information regularly, it can always provide the latest information. In addition, the search unit can prioritize displaying the nearest store based on the user's current location. This allows the user to obtain the desired product in the shortest possible time. Furthermore, the search unit can suggest related products and stores based on the user's past search history and purchase history. This increases the user's opportunities to discover new products and stores, improving the purchasing experience.

[0088] The information provider unit provides store information based on the search results obtained by the search unit. For example, the information provider unit provides the address, business hours, contact information, and special offers information of the stores displayed in the search results. Specifically, it displays detailed information of the store selected by the user, allowing the user to see the information they need at a glance. For example, the store address is displayed on a map, allowing the user to confirm the store's exact location. Since business hours may vary depending on the day of the week, detailed business hours information is provided. Contact information such as a phone number and email address is displayed, allowing the user to contact the store directly. Special offers information displays details of current sales and campaigns, allowing users to purchase products at a better price. Furthermore, the information provider unit can also provide customized information according to the user's preferences. For example, if the user is interested in a particular brand or category, store information related to that brand or category will be displayed preferentially. In addition, the information provider unit can suggest the nearest or easily accessible stores based on the user's location information. This allows users to efficiently obtain store information and proceed smoothly with their purchasing actions.

[0089] The guidance unit provides route guidance to the store based on information provided by the service unit. The guidance unit can provide route guidance such as the shortest route or a route that takes traffic conditions into account. Specifically, it calculates the optimal route from the user's current location to the target store and displays it on a map. The guidance unit acquires real-time traffic information and optimizes the route considering information such as congestion and construction. This allows the user to reach their destination in the shortest possible time. The guidance unit can provide route guidance using methods such as voice guidance, visual guidance, and real-time updates. Voice guidance allows users to receive route guidance without taking their eyes off the road, even while driving or walking. Visual guidance displays the route on a map, allowing users to visually confirm the route. Real-time updates automatically recalculate the route in response to changes in traffic conditions, always providing the optimal route. Furthermore, the guidance unit can also provide route guidance tailored to the user's mode of transportation. For example, it calculates the optimal route for each mode of transportation, such as car, bicycle, or walking, and provides guidance methods appropriate for each. This allows users to receive route guidance that is most suitable for their situation, ensuring a smooth journey to their destination.

[0090] The notification unit sends notifications when a user-specified product is in stock or when there is special sale information. The notification unit can send notifications using methods such as email notifications, push notifications, and notification timing. Specifically, when a user-specified product is in stock, it immediately sends a notification to inform the user. Email notifications send detailed information to the user's email address, while push notifications immediately display the notification on the smartphone screen. Notification timing can be customized according to the user's settings. For example, users can choose to receive notifications at specific times or only when specific conditions are met, allowing for flexible settings to meet user needs. Furthermore, the notification unit can also suggest related products and special sale information based on the user's purchase and search history. This allows users to receive information that matches their interests and needs in a timely manner. In addition, the notification unit can combine multiple notification methods to ensure that important information reaches the user. For example, by using push notifications and email notifications together, users can be sure to receive important information without missing any notifications. This allows the notification unit to help users efficiently find the products they want and proceed smoothly with their purchasing actions.

[0091] The recommendation system uses AI to analyze users' purchase and search history to provide personalized product recommendations. For example, the recommendation system recommends products based on the user's past purchase history, search history, and interests. It can use recommendation algorithms such as collaborative filtering and content-based filtering to recommend products. For example, it can recommend products similar to those the user has previously purchased. It can also recommend highly relevant products based on the user's past purchase history. Furthermore, it can analyze the user's past purchase history to recommend products related to specific seasons or events. This enables personalized product recommendations based on the user's purchase and search history.

[0092] The Management Department provides management tools for stores to update inventory information. The Management Department provides management tools with functions such as adding and deleting inventory, changing inventory quantities, and real-time updates. For example, the Management Department can add and delete inventory. The Management Department can also change inventory quantities. Furthermore, the Management Department can update inventory information in real time. This allows stores to efficiently manage inventory information and provide accurate information to users.

[0093] The review section displays user reviews and ratings. For example, the review section displays information such as star ratings, text reviews, and review reliability. The review section can display star ratings, text reviews, and review reliability. This allows users to choose products based on reviews and ratings from other users.

[0094] The search unit can perform a search within a specified distance from the user's current location and display whether the item is in stock. For example, the search unit can perform a search within a specified distance from the user's current location and display whether the item is in stock. The search unit can perform a search within a range such as a radius of several kilometers or a walking distance of several minutes. This allows the user to check the availability of an item within a specified distance from their current location.

[0095] The service provider can provide information such as the store's address, business hours, contact information, and special offers displayed in the search results. For example, the service provider can provide information such as the store's address, business hours, contact information, and special offers displayed in the search results. For example, the service provider can provide information such as the store's address, business hours, contact information, and special offers. For example, the service provider can provide information such as the store's address, business hours, contact information, and special offers displayed in the search results. This allows users to view detailed information about the store.

[0096] The guidance unit can guide users to the shortest route to the store. For example, the guidance unit can guide users to the shortest route to the store. The guidance unit can provide route guidance such as the shortest route or a route that takes traffic conditions into consideration. This allows users to reach the store via the shortest route.

[0097] The notification unit can send notifications when a product specified by the user arrives in a nearby store or when there is special sale information. For example, the notification unit can send notifications when a product specified by the user arrives in a nearby store or when there is special sale information. The notification unit can send notifications using methods such as email notifications, push notifications, and notification timing. For example, the notification unit can send notifications when a product specified by the user arrives in a nearby store or when there is special sale information. The notification unit can send notifications using methods such as email notifications, push notifications, and notification timing. This allows users to receive immediate notifications about the arrival of specified products and special sales.

[0098] The input system 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 input system can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the input system can provide detailed input options and suggest customizable input methods. For example, if the user is in a hurry, the input system can prioritize voice input to allow for quick input of product names and categories. This improves the convenience of input by providing input methods that respond to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] 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. For example, the reception desk can prioritize suggesting input methods that the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest product names and categories that the user will use at a specific time of day based on their past search history. This allows the system to present the most suitable input options based on the user's past search history.

[0100] The reception desk can improve the convenience of input by using voice input and image recognition when entering product names and categories. For example, the reception desk can allow users to input product names and categories by voice, saving them time. For example, the reception desk can use AI to automatically recognize product names and categories when a user uploads an image of a product. For example, the reception desk can automatically input product names and categories when a user scans a barcode. In this way, the effort required for input can be reduced by using voice input and image recognition.

[0101] The reception system can estimate the user's emotions and adjust the input interface design based on those emotions. For example, if the user is tense, the reception system can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, for example, the reception system can provide an interface with bright colors to make the input process more enjoyable. If the user is tired, for example, the reception system can provide a simple and highly visible interface to facilitate the input process. This makes the input process more comfortable by providing an interface design that responds to 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.

[0102] The input system can prioritize displaying highly relevant suggestions when users enter product names or categories, taking into account their geographical location. For example, the input system can prioritize displaying products available at nearby stores based on the user's current location. For example, the input system can prioritize displaying products from stores the user frequently visits, based on the user's past location information. For example, the input system can prioritize displaying popular products in a specific region based on the user's geographical location information. This allows the system to prioritize displaying highly relevant products based on the user's geographical location.

[0103] The reception desk can analyze the user's social media activity when they enter product names and categories, and suggest relevant products. For example, the reception desk can suggest related products based on products the user has "liked" on social media. For example, the reception desk can prioritize displaying products from brands the user follows on social media. For example, the reception desk can analyze the content of the user's social media posts and suggest products they might be interested in. In this way, it can suggest relevant products based on the user's social media activity.

[0104] The search unit can estimate the user's emotions and adjust how search results are displayed based on those emotions. For example, if the user is relaxed, the search unit will display search results containing detailed information. If the user is in a hurry, the search unit will display concise search results that get straight to the point. If the user is excited, the search unit will display search results with visually stimulating effects. This improves the usability of the search by providing a search result display method that suits 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.

[0105] The search engine can rank search results based on factors such as product popularity and ratings. For example, it can rank results based on the number of reviews and ratings. It can also prioritize displaying highly popular products based on sales data, or prioritize displaying highly relevant products based on a user's past rating history. This allows search results to be ranked based on product popularity and ratings.

[0106] The search unit can apply different search algorithms to each product category during a search. For example, in the food category, the search unit applies a search algorithm that takes into account expiration dates and nutritional information. In the electronics category, for example, the search unit applies a search algorithm that emphasizes specifications and functions. In the fashion category, for example, the search unit applies a search algorithm that takes into account trends and styles. By applying a search algorithm appropriate to the product category, the search accuracy is improved.

[0107] The search unit can estimate the user's emotions and prioritize search results based on those emotions. For example, if the user is stressed, the search unit will prioritize simple and highly visible search results. If the user is relaxed, the search unit will prioritize search results containing detailed information. If the user is in a hurry, the search unit will prioritize search results that are to the point. This improves the usability of the search by providing search results that are prioritized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The search function can prioritize displaying highly relevant stores by considering the user's geographical location during a search. For example, the search function can prioritize displaying nearby stores based on the user's current location. For example, the search function can prioritize displaying frequently visited stores by referring to the user's past location information. For example, the search function can prioritize displaying popular stores in a specific area based on the user's geographical location information. This allows the system to prioritize displaying highly relevant stores based on the user's geographical location.

[0109] The search function can analyze a user's social media activity during a search and suggest relevant stores. For example, it can suggest stores based on the stores a user has "liked" on social media. For example, it can prioritize displaying stores that a user follows on social media. For example, it can analyze the content of a user's social media posts and suggest stores that might be of interest. In this way, it can suggest relevant stores based on the user's social media activity.

[0110] The service provider can estimate the user's emotions and adjust the way store information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider will provide a simple and highly visible display method. For example, if the user is relaxed, the service provider will provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider will provide a display method that gets straight to the point. This improves the usability of the information by providing a way to display store information that suits 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.

[0111] The information provider can adjust the level of detail of the information provided, taking into account the store's rating and reviews. For example, if a store has a high rating, the provider will provide detailed information. For example, if a store has many reviews, the provider will provide detailed information. For example, if a store has few ratings or reviews, the provider will provide only basic information. This allows the level of detail of the information to be adjusted based on the store's rating and reviews.

[0112] The information provisioning department can apply different information provision algorithms depending on the store category when providing information. For example, in the food category, the department applies an information provision algorithm that emphasizes expiration dates and nutritional information. For example, in the electronics category, the department applies an information provision algorithm that emphasizes specifications and functions. For example, in the fashion category, the department applies an information provision algorithm that emphasizes trends and styles. By applying an information provision algorithm appropriate to the store category, the accuracy of information provision is improved.

[0113] The service provider can estimate the user's emotions and prioritize store information based on those emotions. For example, if the user is feeling stressed, the service provider will prioritize displaying simple and easily visible store information. For example, if the user is relaxed, the service provider will prioritize displaying store information that includes detailed information. For example, if the user is in a hurry, the service provider will prioritize displaying store information that gets straight to the point. This improves the usability of the information by providing priority of store information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The service provider can prioritize displaying highly relevant store information by considering the user's geographical location at the time of delivery. For example, the service provider can prioritize displaying nearby store information based on the user's current location. For example, the service provider can prioritize displaying frequently visited store information by referring to the user's past location information. For example, the service provider can prioritize displaying popular store information in a specific area based on the user's geographical location information. This allows the service provider to prioritize displaying highly relevant store information based on the user's geographical location.

[0115] The service provider can analyze the user's social media activity and suggest relevant store information at the time of delivery. For example, the service provider can suggest relevant store information based on stores that the user has "liked" on social media. For example, the service provider can prioritize displaying store information that the user follows on social media. For example, the service provider can analyze the content of the user's social media posts and suggest store information that they might be interested in. In this way, relevant store information can be suggested based on the user's social media activity.

[0116] The guidance system can estimate the user's emotions and adjust the route guidance method based on the estimated emotions. For example, if the user is nervous, the guidance system provides simple and easy-to-understand route guidance. For example, if the user is relaxed, the guidance system provides route guidance with detailed information. For example, if the user is in a hurry, the guidance system provides route guidance that gets straight to the point. This improves the convenience of guidance by providing route guidance methods that respond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The guidance system can suggest the optimal route by considering traffic conditions and weather information. For example, it can suggest the optimal route based on real-time traffic congestion information. For example, it can suggest a route with a roof in rainy weather by considering real-time weather information. For example, it can suggest the optimal route by considering the real-time operating status of public transportation. In this way, it can suggest the optimal route based on traffic conditions and weather information.

[0118] The guidance unit can apply different route guidance algorithms depending on the user's mode of transportation. For example, when guiding pedestrians, the guidance unit prioritizes sidewalks and crosswalks. For example, when guiding cyclists, the guidance unit prioritizes bicycle paths. For example, when guiding car users, the guidance unit prioritizes routes that avoid traffic congestion. By applying a route guidance algorithm tailored to the user's mode of transportation, the accuracy of the guidance is improved.

[0119] The navigation system can estimate the user's emotions and determine the priority of route guidance based on those emotions. For example, if the user is stressed, the navigation system will prioritize displaying simple and highly visible route guidance. If the user is relaxed, the navigation system will prioritize displaying route guidance that includes detailed information. If the user is in a hurry, the navigation system will prioritize displaying route guidance that gets straight to the point. This improves the usability of the guidance by providing route guidance priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The navigation system can prioritize displaying the optimal route by considering the user's geographical location. For example, it can prioritize displaying the optimal route based on the user's current location. For example, it can prioritize displaying frequently used routes by referring to the user's past location information. For example, it can prioritize displaying popular routes in a particular area based on the user's geographical location information. This allows the system to prioritize displaying the optimal route based on the user's geographical location information.

[0121] The navigation system can analyze a user's social media activity and suggest relevant routes during navigation. For example, it can suggest relevant routes based on places the user has "liked" on social media. For example, it can prioritize displaying places the user follows on social media. For example, it can analyze the content of a user's social media posts and suggest routes that might interest them. In this way, it can suggest relevant routes based on the user's social media activity.

[0122] 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 relaxed, the notification unit will send a notification immediately. If the user is busy, the notification unit will postpone the notification. If the user is in a hurry, the notification unit will prioritize sending important notifications. This improves the convenience of notifications by providing notification timing that is appropriate to 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.

[0123] The notification system can adjust notification content based on product popularity and ratings. For example, it can immediately send a notification when a popular product is in stock. For example, it can send a notification when a highly-rated product goes on sale. For example, it can send a notification when a product that a user has previously given a high rating to is in stock. This allows the notification content to be adjusted based on product popularity and ratings.

[0124] The notification unit can apply different notification algorithms to each product category when sending notifications. For example, in the food category, the notification unit prioritizes notifying users of products nearing their expiration date. In the electronics category, for example, the notification unit prioritizes notifying users of new product arrival information. In the fashion category, for example, the notification unit prioritizes notifying users of sales information. By applying a notification algorithm appropriate to the product category, the accuracy of notifications is improved.

[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 will prioritize sending important notifications. For example, if the user is relaxed, the notification unit will prioritize sending detailed notifications. For example, if the user is in a hurry, the notification unit will prioritize sending concise notifications. This improves the convenience of notifications by providing notification priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0126] The notification unit can prioritize sending highly relevant notifications by considering the user's geographical location. For example, the notification unit can prioritize sending notifications about new stock at nearby stores based on the user's current location. For example, the notification unit can prioritize sending notifications about frequently visited stores by referring to the user's past location information. For example, the notification unit can prioritize sending notifications about popular products in a specific region based on the user's geographical location information. This allows the notification unit to prioritize sending highly relevant notifications based on the user's geographical location information.

[0127] The notification unit can analyze the user's social media activity and suggest relevant notifications when sending a notification. For example, the notification unit can send a notification when a product that the user "liked" on social media becomes available. For example, the notification unit can send a notification when a product from a brand that the user follows on social media becomes available. For example, the notification unit can analyze the user's social media posts and send notifications about products that might interest them. This allows the system to suggest relevant notifications based on the user's social media activity.

[0128] 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 relaxed, the recommendation system will display recommended products with detailed information. If the user is in a hurry, the recommendation system will display concise recommendations that get straight to the point. If the user is excited, the recommendation system will display recommended products with visually stimulating effects. This improves the convenience of recommendations by providing a way to display recommended products that suits 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.

[0129] The recommendation system can analyze a user's past purchase history to recommend the most suitable products. For example, it can recommend products similar to those the user has previously purchased. For example, it can recommend highly relevant products based on the user's past purchase history. For example, it can analyze the user's past purchase history to recommend products related to a specific season or event. This allows the system to recommend the most suitable products based on the user's past purchase history.

[0130] The recommendation system can apply recommendation algorithms based on the user's current areas of interest during the recommendation process. For example, the recommendation system can recommend relevant products based on the product categories the user has recently searched for. For example, the recommendation system can recommend relevant products based on the products the user has recently viewed. For example, the recommendation system can recommend relevant products based on the products the user has recently purchased. This allows the system to recommend the most suitable products based on the user's current areas of interest.

[0131] The recommendation system can estimate the user's emotions and prioritize recommended products based on those emotions. For example, if the user is stressed, the recommendation system will prioritize simple, highly visible recommended products. If the user is relaxed, the recommendation system will prioritize recommended products containing detailed information. If the user is in a hurry, the recommendation system will prioritize recommended products that get straight to the point. This improves the convenience of recommendations by providing priority for recommended products according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0132] The recommendation system can prioritize recommending highly relevant products by considering the user's geographical location. For example, it can prioritize recommending products available at nearby stores based on the user's current location. For example, it can prioritize recommending products at stores the user frequently visits by referring to the user's past location data. For example, it can prioritize recommending popular products in a specific region based on the user's geographical location. This allows the system to prioritize recommending highly relevant products based on the user's geographical location.

[0133] The recommendation team can analyze a user's social media activity and suggest relevant products when making recommendations. For example, the recommendation team can recommend products based on what the user has "liked" on social media. For example, the recommendation team can prioritize recommending products from brands that the user follows on social media. For example, the recommendation team can analyze the content of a user's social media posts and recommend products that they might be interested in. In this way, relevant products can be suggested based on the user's social media activity.

[0134] The management department can provide automated tools for updating store inventory information in real time. For example, the management department provides automated tools to eliminate the need for stores to manually update inventory information. For example, the management department updates store inventory information in real time, providing users with accurate information. For example, the management department automatically collects store inventory information and reflects it in the system. This eliminates the need for stores to manually update inventory information and allows for the provision of accurate information in real time.

[0135] The management department can optimize inventory management by analyzing store sales data during management. For example, the management department can optimize the timing of inventory replenishment based on store sales data. For example, the management department can analyze store sales data and prioritize securing inventory of high-demand products. For example, the management department can predict inventory surpluses and shortages based on store sales data and implement optimal inventory management. In this way, inventory management can be optimized based on store sales data.

[0136] The management department can prioritize inventory when updating store inventory information, taking into account user purchase history. For example, the management department can prioritize securing inventory of high-demand items based on user purchase history. For example, the management department can analyze user purchase history and prioritize securing inventory of items related to specific seasons or events. For example, the management department can optimize inventory replenishment timing based on user purchase history. This allows for prioritizing the securing of inventory of high-demand items based on user purchase history.

[0137] The management department can optimize inventory allocation by considering the geographical location of stores during management. For example, the management department can prioritize the allocation of inventory for high-demand products based on the geographical location of stores. For example, the management department can prioritize the allocation of inventory for products popular in a particular region by considering the geographical location of stores. For example, the management department can optimize the timing of inventory replenishment based on the geographical location of stores. This allows for the priority allocation of inventory for high-demand products based on the geographical location of stores.

[0138] 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 provides a simple and easy-to-read review display. If the user is relaxed, the review section provides a review display with detailed information. If the user is in a hurry, the review section provides a concise review display. This improves the usability of reviews by providing a review display method that suits 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.

[0139] The review section can adjust the level of detail in reviews based on the product's rating and popularity. For example, if a product has a high rating, the review section will display detailed reviews. For example, if a product has many reviews, the review section will display detailed reviews. For example, if a product has few ratings or reviews, the review section will display only basic information. This allows the level of detail in reviews to be adjusted based on the product's rating and popularity.

[0140] The review department can apply different review display algorithms depending on the product category during the review process. For example, in the food category, the review department applies a review display algorithm that emphasizes expiration date and nutritional information. For example, in the electronics category, the review department applies a review display algorithm that emphasizes specifications and functions. For example, in the fashion category, the review department applies a review display algorithm that emphasizes trends and style. By applying a review display algorithm appropriate to the product category, the accuracy of reviews is improved.

[0141] The review section can estimate the user's emotions and prioritize reviews based on those emotions. For example, if the user is stressed, the review section will prioritize simple, easy-to-read reviews. If the user is relaxed, the review section will prioritize reviews containing detailed information. If the user is in a hurry, the review section will prioritize reviews that get straight to the point. This improves the usability of reviews by providing priority 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.

[0142] The review section can prioritize displaying highly relevant reviews by considering the user's geographical location during the review process. For example, the review section can prioritize displaying reviews of nearby stores based on the user's current location. For example, the review section can prioritize displaying reviews of frequently visited stores by referring to the user's past location information. For example, the review section can prioritize displaying reviews of popular products in a specific region based on the user's geographical location information. This allows the system to prioritize displaying highly relevant reviews based on the user's geographical location.

[0143] The review section can analyze a user's social media activity during the review process and suggest relevant reviews. For example, the review section can display reviews related to products that the user has "liked" on social media. For example, the review section can prioritize displaying product reviews of brands that the user follows on social media. For example, the review section can analyze a user's social media posts and suggest reviews of products they might be interested in. This allows the review section to suggest relevant reviews based on the user's social media activity.

[0144] The review section can prioritize displaying highly relevant reviews by considering the user's purchase history during the review process. For example, the review section can prioritize displaying reviews of products purchased by the user based on their purchase history. For example, the review section can analyze the user's purchase history and prioritize displaying reviews of highly relevant products. For example, the review section can prioritize displaying reviews of products related to a specific season or event based on the user's purchase history. This allows the system to prioritize displaying highly relevant reviews based on the user's purchase history.

[0145] The review section can prioritize displaying highly relevant reviews by considering the user's search history during the review process. For example, the review section can prioritize displaying reviews of products the user has searched for based on their search history. For example, the review section can analyze the user's search history and prioritize displaying reviews of highly relevant products. For example, the review section can prioritize displaying reviews of products related to a specific season or event based on the user's search history. This allows the system to prioritize displaying highly relevant reviews based on the user's search history.

[0146] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0147] The reception system can estimate the user's emotions and adjust the input method for product names and categories based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of product names and categories. This improves the convenience of input by providing input methods that respond to the user's emotions.

[0148] The search function can estimate the user's emotions and adjust how search results are displayed based on that estimation. For example, if the user is relaxed, it can display search results containing detailed information. If the user is in a hurry, it can display concise search results that get straight to the point. Furthermore, if the user is excited, it can display search results with visually stimulating effects. By providing search results that respond to the user's emotions, the usability of the search is improved.

[0149] The system can estimate the user's emotions and adjust how store information is displayed based on those emotions. For example, if the user is feeling anxious, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. By providing store information in a way that suits the user's emotions, the usability of the information is improved.

[0150] The navigation system can estimate the user's emotions and adjust the route guidance method based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand route guidance. If the user is relaxed, it can provide route guidance with more detailed information. Furthermore, if the user is in a hurry, it can provide route guidance that gets straight to the point. By providing route guidance methods that match the user's emotions, the convenience of navigation is improved.

[0151] 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 relaxed, notifications can be sent immediately. If the user is busy, notifications can be postponed. Furthermore, if the user is in a hurry, important notifications can be sent as a priority. This improves the convenience of notifications by providing notification timing that is tailored to the user's emotions.

[0152] The search engine can rank search results based on product popularity and ratings. For example, it can rank search results based on the number of reviews and ratings. It can also prioritize displaying popular products based on sales data. Furthermore, it can prioritize displaying highly relevant products by referring to the user's past rating history. This allows for ranking search results based on product popularity and ratings.

[0153] The information provider can adjust the level of detail based on the store's rating and reviews when providing information. For example, if a store has a high rating, detailed information can be provided. Similarly, if a store has many reviews, detailed information can also be provided. Furthermore, if a store has few ratings or reviews, only basic information can be provided. This allows the level of detail of information to be adjusted based on the store's rating and reviews.

[0154] The guidance system can suggest the optimal route by considering traffic conditions and weather information. For example, it can suggest the best route based on real-time traffic congestion information. It can also suggest a route with a roof in case of rain by considering real-time weather information. Furthermore, it can suggest the best route by considering the real-time operating status of public transportation. In this way, it can suggest the optimal route based on traffic conditions and weather information.

[0155] The notification system can adjust notification content based on product popularity and ratings. For example, it can send an immediate notification when a popular product is back in stock. It can also send a notification when a highly-rated product goes on sale. Furthermore, it can send a notification when a product that a user has previously given a high rating to becomes available again. This allows the system to adjust notification content based on product popularity and ratings.

[0156] The recommendation system can apply recommendation algorithms based on the user's current areas of interest. For example, it can recommend relevant products based on the product categories the user has recently searched for. It can also recommend relevant products based on the products the user has recently viewed. Furthermore, it can recommend relevant products based on the products the user has recently purchased. This allows for the recommendation of the most suitable products based on the user's current areas of interest.

[0157] The following briefly describes the processing flow for example form 2.

[0158] Step 1: The reception desk receives input from the user regarding specific product names or categories. Users can input specific product names or categories such as food, clothing, or home appliances. The reception desk can receive product names or categories using methods such as text input, voice input, or image recognition. Step 2: 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 search unit performs a search within a specified distance from the current location and displays whether the item is in stock. The search unit can use GPS to obtain location information in real time and display stores that carry the item. The search unit can provide real-time inventory information and perform periodic inventory updates. Step 3: The service provider provides store information based on the search results obtained by the search unit. The service provider provides the store's address, business hours, contact information, special offers, etc., as displayed in the search results. Step 4: The guidance unit provides route guidance to the store based on the information provided by the service unit. The guidance unit can provide route guidance such as the shortest route or a route that takes traffic conditions into consideration. The guidance unit can provide route guidance using methods such as voice guidance, visual guidance, and real-time updates. Step 5: The notification unit sends notifications when a product specified by the user is in stock or when there is special sale information. The notification unit can send notifications using methods such as email notifications, push notifications, and notification timing.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, search unit, provision unit, guidance unit, notification unit, recommendation unit, management unit, and review unit, is implemented by 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, allowing the user to input a specific product name or category. The search unit is implemented by the identification processing unit 290 of the data processing unit 12, which searches for nearby store information based on the input information and displays whether the item is in stock. The provision unit is implemented by the control unit 46A of the smart device 14, which provides store information based on the search results. The guidance unit is implemented by the control unit 46A of the smart device 14, which provides route guidance to the store. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12, which sends a notification when a product specified by the user arrives in stock or when there is special sale information. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's purchase history and search history and provides personalized product recommendations. The management unit is implemented by the specific processing unit 290 of the data processing device 12 and provides a management tool for stores to update inventory information. The review unit is implemented by the control unit 46A of the smart device 14 and displays user reviews and ratings. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0163] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the reception unit, search unit, provision unit, guidance unit, notification unit, recommendation unit, management unit, and review unit, is implemented by 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, allowing the user to input a specific product name or category. The search unit is implemented by the identification processing unit 290 of the data processing unit 12, which searches for nearby store information based on the input information and displays whether the item is in stock. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides store information based on the search results. The guidance unit is implemented by the control unit 46A of the smart glasses 214, which provides route guidance to the store. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12, which sends a notification when a product specified by the user arrives in stock or when there is special sale information. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's purchase history and search history and provides personalized product recommendations. The management unit is implemented by the specific processing unit 290 of the data processing device 12 and provides a management tool for stores to update inventory information. The review unit is implemented by the control unit 46A of the smart glasses 214 and displays user reviews and ratings. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0179] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Each of the multiple elements described above, including the reception unit, search unit, provision unit, guidance unit, notification unit, recommendation unit, management unit, and review unit, is implemented by 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, allowing the user to input a specific product name or category. The search unit is implemented by the identification processing unit 290 of the data processing unit 12, which searches for nearby store information based on the input information and displays whether the item is in stock. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides store information based on the search results. The guidance unit is implemented by the control unit 46A of the headset terminal 314, which provides route guidance to the store. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12, which sends a notification when a product specified by the user arrives in stock or when there is special sale information. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's purchase history and search history and provides personalized product recommendations. The management unit is implemented by the specific processing unit 290 of the data processing device 12 and provides a management tool for stores to update inventory information. The review unit is implemented by the control unit 46A of the headset terminal 314 and displays user reviews and ratings. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0195] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] Each of the multiple elements described above, including the reception unit, search unit, provision unit, guidance unit, notification unit, recommendation unit, management unit, and review 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, allowing the user to input a specific product name or category. The search unit is implemented by the identification processing unit 290 of the data processing unit 12, which searches for nearby store information based on the input information and displays whether the item is in stock. The provision unit is implemented by the control unit 46A of the robot 414, which provides store information based on the search results. The guidance unit is implemented by the control unit 46A of the robot 414, which provides route guidance to the store. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12, which sends a notification when a product specified by the user arrives in stock or when there is special sale information. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's purchase history and search history and provides personalized product recommendations. The management unit is implemented by the specific processing unit 290 of the data processing device 12 and provides a management tool for stores to update inventory information. The review unit is implemented by the control unit 46A of the robot 414 and displays user reviews and ratings. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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."

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] (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 provisioning unit that provides store information based on the search results obtained by the search unit, A guidance unit provides route guidance to the store based on the information provided by the aforementioned provision unit, It includes a notification unit that sends notifications when a product specified by the user becomes available or when there is special sale information. A system characterized by the following features. (Note 2) The system features a recommendation section that uses AI to analyze users' purchase and search history and provide personalized product recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 3) 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 4) It includes a review section that displays user reviews and ratings. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned search unit, Search within a specified distance from your current location and display whether the item is in stock. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The search results provide information such as the store's address, business hours, contact details, and special offers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned guide section is We will guide you to the shortest route to the store. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned notification unit, The system sends notifications when a product specified by the user arrives at a nearby store or when there is special sale information. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) 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 11) The aforementioned reception unit is Improve the convenience of inputting product names and categories by using voice input and image recognition. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When entering product names or categories, the system prioritizes displaying highly relevant suggestions based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When users enter product names or categories, the system analyzes their social media activity and suggests related products. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching, search results are ranked based on product popularity and ratings. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When searching, different search algorithms are applied for each product category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, When searching, the system prioritizes displaying highly relevant stores by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, When you search, we analyze your social media activity and suggest relevant stores. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how store information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, we adjust the level of detail based on store ratings and reviews. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, a different information provision algorithm is applied for each store category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates user sentiment and prioritizes store information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing information, the system prioritizes displaying highly relevant store information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and suggest relevant store information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide section is The system estimates the user's emotions and adjusts the route guidance method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide section is When providing directions, we will suggest the optimal route considering traffic conditions and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned guide section is When providing directions, different route guidance algorithms are applied depending on the user's mode of transportation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned guide section is The system estimates the user's emotions and determines route guidance priorities based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned guide section is When providing directions, the system prioritizes displaying the optimal route, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned guide section is During navigation, the system analyzes the user's social media activity and suggests relevant routes. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, Estimate the user's emotion and adjust the notification timing based on the estimated user emotion The system according to appended note 1, characterized by the above (Appended note 34) The notification unit When notifying, adjust the notification content considering the popularity and evaluation of the product The system according to appended note 1, characterized by the above (Appended note 35) The notification unit When notifying, apply different notification algorithms for each category of the product The system according to appended note 1, characterized by the above (Appended note 36) The notification unit Estimate the user's emotion and determine the notification priority based on the estimated user emotion The system according to appended note 1, characterized by the above (Appended note 37) The notification unit When notifying, preferentially send relevant notifications considering the user's geographical location information The system according to appended note 1, characterized by the above (Appended note 38) The notification unit When notifying, analyze the user's social media activities and propose relevant notifications The system according to appended note 1, characterized by the above (Appended note 39) The recommendation unit Estimate the user's emotion and adjust the display method of the recommended products based on the estimated user emotion The system according to appended note 1, characterized by the above (Appended note 40) The recommendation unit When recommending, analyze the user's past purchase history and recommend the optimal products The system according to appended note 1, characterized by the above (Appended note 41) The recommendation unit When recommending, apply a recommendation algorithm based on the user's current area of interest The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned recommendation department, It estimates the user's emotions and prioritizes recommended products based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned recommendation department, When making recommendations, the system prioritizes recommending highly relevant products by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned recommendation department, When making recommendations, we analyze the user's social media activity and suggest relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned management department, We provide an automated tool for updating store inventory information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned management department, During management, store sales data is analyzed to optimize inventory management. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned management department, When updating store inventory information, the system prioritizes inventory based on the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned management department, During management, inventory placement is optimized by considering the geographical location of the stores. The system described in Appendix 1, characterized by the features described herein. (Note 49) The aforementioned review section, It estimates user sentiment and adjusts how reviews are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Appendix 50) The review unit adjusts the review detail level in consideration of the evaluation and popularity of the product during review. The system according to Appendix 1, characterized in that. (Appendix 51) The review unit applies different review display algorithms for each category of the product during review. The system according to Appendix 1, characterized in that. (Appendix 52) The review unit estimates the user's emotion and determines the priority of the review based on the estimated user's emotion. The system according to Appendix 1, characterized in that. (Appendix 53) The review unit prioritizes and displays relevant reviews in consideration of the user's geographical location information during review. The system according to Appendix 1, characterized in that. (Appendix 54) The review unit analyzes the user's social media activities and proposes relevant reviews during review. The system according to Appendix 1, characterized in that. (Appendix 55) The review unit prioritizes and displays relevant reviews in consideration of the user's purchase history during review. The system according to Appendix 1, characterized in that. (Appendix 56) The review unit prioritizes and displays relevant reviews in consideration of the user's search history during review. The system according to Appendix 1, characterized in that.

Explanation of Signs

[0231] 10, 210, 310, 410 Data processing system 12 Data processing device 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 provisioning unit that provides store information based on the search results obtained by the search unit, A guidance unit provides route guidance to the store based on the information provided by the aforementioned provision unit, It includes a notification unit that sends notifications when a product specified by the user becomes available or when there is special sale information. A system characterized by the following features.

2. The system includes a recommendation section that uses AI to analyze users' purchase and search history and provide personalized product recommendations. The system according to feature 1.

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

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

5. The aforementioned search unit, Search within a specified distance from your current location and display whether the item is in stock. The system according to feature 1.

6. The aforementioned supply unit is, The search results provide information such as the store's address, business hours, contact details, and special offers. The system according to feature 1.

7. The aforementioned guide section is We will guide you to the shortest route to the store. The system according to feature 1.

8. The aforementioned notification unit, The system sends notifications when a product specified by the user arrives at a nearby store or when there is special sale information. The system according to feature 1.

9. 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.

Images