system

Abstract

The system according to this embodiment aims to enable users to efficiently find desired products in offline stores. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a navigation unit. The reception unit receives user input. The analysis unit analyzes the store's inventory status based on the information received by the reception unit. The generation unit generates a route based on the information analyzed by the analysis unit. The navigation unit navigates based on the route generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the 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 difficult to efficiently find a target product in an offline store.

[0005] The system according to the embodiment aims to efficiently find a target product in an offline store.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a navigation unit. The reception unit receives user input. The analysis unit analyzes the store's inventory status based on the information received by the reception unit. The generation unit generates a route based on the information analyzed by the analysis unit. The navigation unit navigates based on the route generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to efficiently find their desired products in offline stores. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The shopping navigation AI according to an embodiment of the present invention is a system that improves the shopping experience in offline stores. This shopping navigation AI analyzes the inventory status in the store and generates and navigates the user to the store by inputting desired or preferred products. For example, if a user inputs a specific product name or characteristic such as "red sweater" or "casual jacket," the generating AI analyzes the store's inventory status and generates the optimal route to reach the desired product. The generating AI understands the placement and inventory status of products in the store and proposes a route that allows the user to find products efficiently. For example, if a user is looking for a red sweater in a Uniqlo store, the generating AI identifies the location where the red sweater is located and proposes the shortest route to that location. Furthermore, the generating AI interactively incorporates the user's real-time feedback and reflects it in the route. For example, if a user says, "This sweater is a little big," the generating AI will use that information to suggest other sweaters of different sizes. Also, if a user says, "I don't like the material of this jacket," the generating AI will suggest jackets made of different materials. This system allows users to quickly find the products they are looking for, improving the offline shopping experience. Furthermore, the generative AI learns user preferences and can provide more personalized suggestions. For example, if a user prefers casual styles, the generative AI will prioritize suggesting casual items. The generative AI also has the ability to show and compare similar products and explain the differences in features and materials. This allows users to compare multiple products and choose the best one for them. For example, the generative AI might explain, "This sweater is made of wool, and this sweater is made of cotton," helping users understand the difference in materials. In this way, the shopping navigation AI improves the offline shopping experience by understanding user preferences and suggesting the optimal route while incorporating real-time feedback.

[0029] The shopping navigation AI according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a navigation unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit receives information entered by the user using a smartphone or tablet, for example. The reception unit can also receive voice input from the user using voice recognition technology. Furthermore, the reception unit can analyze images taken by the user using image recognition technology and accept them as input information. For example, the reception unit analyzes an image of a product taken by the user with a smartphone and accepts information about that product as input. The analysis unit analyzes the store's inventory status based on the information received by the reception unit. The analysis unit, for example, works in conjunction with the store's inventory management system to grasp the inventory status in real time. The analysis unit analyzes the inventory status of the product entered by the user based on the inventory data obtained from the inventory management system. For example, the analysis unit analyzes the inventory data obtained from the inventory management system to check whether the product entered by the user is in stock. The analysis unit can also grasp the latest inventory status by considering the frequency of inventory data updates. The generation unit generates routes based on the information analyzed by the analysis unit. For example, the generation unit uses generation AI to generate the optimal route so that users can find products efficiently. The generation unit understands the placement and inventory status of products in the store and proposes routes so that users can reach their desired products. For example, the generation unit uses generation AI to analyze the placement information of products in the store and generate the shortest route. The generation unit can also generate personalized routes based on the user's preferences. For example, if the generation unit prefers casual styles, it will generate a route that prioritizes suggesting casual products. The navigation unit navigates based on the route generated by the generation unit. The navigation unit uses, for example, voice guidance or visual guidance to guide the user to their desired product. The navigation unit provides real-time navigation information so that users do not get lost in the store.For example, the navigation unit can determine the user's current location and guide them to the desired product in real time. The navigation unit can also incorporate the user's real-time feedback and reflect it in the route. For example, if the user says, "This sweater is a little big," the navigation unit will use that information to suggest other sweaters of different sizes. In this way, the shopping navigation AI according to the embodiment improves the shopping experience in offline stores by generating and navigating the optimal route based on the user's input. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's real-time feedback into a generating AI and have the generating AI adjust the route based on that feedback.

[0030] The reception desk receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception desk can, for example, receive information entered by the user using a smartphone or tablet. The reception desk can also receive voice input from the user using speech recognition technology. Furthermore, the reception desk can analyze images taken by the user using image recognition technology and accept them as input information. For example, the reception desk can analyze an image of a product taken by the user with a smartphone and accept information about that product as input. The reception desk utilizes advanced natural language processing and image analysis technologies to process the information entered by the user quickly and accurately. For example, in the case of voice input, the speech recognition engine converts the user's speech into text and analyzes that text to understand the user's intent. In the case of image input, the image recognition engine extracts the features of the product and identifies the product by matching it with information in the database. This allows the reception desk to support diverse input formats and respond flexibly to user needs. Furthermore, the reception desk can learn the user's past input history and behavior patterns to provide more personalized services. For example, if a user has frequently searched for products from a particular brand in the past, products from that brand will be displayed preferentially. Furthermore, the reception desk can analyze user input in real time and provide immediate feedback. This allows users to shop smoothly and efficiently. To protect user privacy, the reception desk properly manages entered information and uses encryption technology as needed to ensure data security. This allows users to use the service with peace of mind.

[0031] The analysis department analyzes store inventory status based on information received by the reception department. For example, the analysis department integrates with the store's inventory management system to grasp inventory status in real time. Based on inventory data obtained from the inventory management system, the analysis department analyzes the inventory status of products entered by users. For example, the analysis department analyzes inventory data obtained from the inventory management system to check whether the product entered by the user is in stock. The analysis department can also grasp the latest inventory status by considering the frequency of inventory data updates. The analysis department utilizes AI technology to perform inventory data analysis efficiently and accurately. For example, it can use machine learning algorithms to learn patterns in inventory data and optimize demand forecasting and inventory replenishment. This allows the analysis department to quickly grasp the inventory status of products users are looking for and provide appropriate information. Furthermore, the analysis department can integrate inventory data from multiple stores and present the inventory status of the store closest to the user. This allows users to find products efficiently. The analysis department can also predict inventory fluctuations by considering past sales data and seasonal trends, and suggest products to users at the appropriate time. For example, if a particular product experiences seasonally high demand, the system will notify users before it goes out of stock. The analytics department can also provide personalized product suggestions based on users' purchase history and preferences. This allows users to efficiently find products that suit their tastes. The analytics department implements appropriate access controls and data encryption to ensure data security and privacy, and manages user information securely.

[0032] The generation unit generates routes based on information analyzed by the analysis unit. For example, the generation unit uses generational AI to generate the optimal route so that users can efficiently find products. The generation unit understands the placement and inventory status of products within the store and proposes a route so that users can reach their desired products. For example, the generation unit uses generational AI to analyze the placement information of products within the store and generate the shortest route. The generation unit can also generate personalized routes based on user preferences. For example, if the generation unit prefers casual styles, it will generate a route that prioritizes suggesting casual products. The generation unit utilizes AI technology to generate the optimal route based on user input information and past behavior history. For example, it analyzes data on stores the user has visited and products they have purchased in the past to learn the user's preferences and purchasing patterns. This allows the generation unit to propose the most efficient and satisfying route for the user. Furthermore, the generation unit can dynamically adjust the route considering real-time updated inventory information and store congestion. For example, if a specific product is out of stock or a particular area in the store is crowded, the route generation unit immediately recalculates the route and provides the user with the optimal route. Furthermore, the generation unit can continuously improve the accuracy and efficiency of routes by incorporating user feedback. For instance, if a user provides feedback such as "this route is a detour," the route generation algorithm is adjusted based on that information and reflected in future route suggestions. This allows the generation unit to provide flexible and personalized routes tailored to user needs, improving the shopping experience.

[0033] The navigation unit navigates based on routes generated by the generation unit. The navigation unit guides users to their desired products using, for example, voice and visual guidance. The navigation unit provides real-time navigation information to prevent users from getting lost in the store. For example, the navigation unit tracks the user's current location and provides real-time directions to the desired product. The navigation unit can also incorporate real-time user feedback and reflect it in the route. For instance, if a user comments that "this sweater is a little too big," the navigation unit will use that information to suggest other sweaters in different sizes. The navigation unit utilizes GPS and beacon technology to accurately determine the user's current location. This allows it to track the user's location in the store in real time and provide optimal navigation information. Furthermore, the navigation unit detects the user's speed and direction and provides guidance information at the appropriate time. For example, if a user stops in front of a specific product, it will display detailed information about that product and suggest related products. The navigation unit can also continuously improve navigation information based on user feedback. For example, if a user provides feedback such as "This guidance is difficult to understand," the guidance method will be adjusted based on that information and reflected in future navigation. The navigation unit can provide information to the user by combining multiple means, such as voice guidance, visual guidance, vibration notifications, and flashing lights. This allows users to receive navigation information not only through sight and hearing, but also through touch. To protect user privacy, the navigation unit appropriately manages location information and movement history, and ensures data security using encryption technology as needed. This allows users to use the service with peace of mind.

[0034] The feedback collection unit can incorporate real-time user feedback. For example, it can collect feedback by having users enter text comments. For example, it can accept text comments entered by users using smartphones or tablets. The feedback collection unit can also collect voice feedback. For example, it can convert voice feedback from users into text using speech recognition technology and collect it. Furthermore, the feedback collection unit can also collect rating scores. For example, it can accept rating scores entered by users as a number of stars. This allows for the provision of more personalized routes by reflecting real-time user feedback. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or not using AI. For example, the feedback collection unit can input user voice feedback into a generating AI and have the generating AI perform analysis of the feedback.

[0035] The history utilization unit can utilize the user's past purchase history and ratings. For example, the history utilization unit collects and utilizes information such as the user's purchase date and time, purchased items, and purchase frequency. For example, the history utilization unit suggests related products based on information about products the user has purchased in the past. The history utilization unit can also utilize the user's ratings. For example, the history utilization unit suggests products that match the user's preferences based on the star ratings and comment ratings the user has given in the past. Furthermore, the history utilization unit can also utilize survey results. For example, the history utilization unit suggests products that match the user's preferences based on the results of surveys the user has answered in the past. In this way, by utilizing the user's past purchase history and ratings, it becomes possible to make more appropriate product suggestions. Some or all of the above processing in the history utilization unit may be performed using AI, for example, or without using AI. For example, the history utilization unit can input the user's purchase history data into a generating AI and have the generating AI execute suggestions for related products.

[0036] The explanatory section can explain the differences in the functions and materials of products. For example, the explanatory section can perform performance comparisons of products. For example, the explanatory section can compare the performance of different products and explain them to the user. The explanatory section can also explain the properties of materials. For example, the explanatory section can explain the differences between wool and cotton sweaters. Furthermore, the explanatory section can explain examples of product use. For example, the explanatory section can explain in what situations a particular jacket is used. This makes it easier for users to compare products by explaining the differences in their functions and materials. Some or all of the above processing in the explanatory section may be performed using AI, for example, or not using AI. For example, the explanatory section can input product performance data into a generating AI and have the generating AI perform the performance comparison explanation.

[0037] The analysis unit can understand inventory status by linking with the store's inventory management system. For example, the analysis unit can acquire inventory data in real time from the inventory management system. For example, the analysis unit can understand the inventory quantity and inventory update frequency of products by linking with the inventory management system. The analysis unit can also understand the latest inventory status by considering the inventory data update frequency. For example, the analysis unit can understand the inventory status of products in real time based on the inventory data update frequency. This allows for accurate understanding of inventory status by linking with the store's inventory management system. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input inventory data acquired from the inventory management system into a generating AI and have the generating AI perform an analysis of the inventory status.

[0038] The generation unit can generate the optimal route based on the user's preferences. For example, the generation unit can collect the user's past preference data and use it to generate the route. For example, the generation unit can generate a route that suits the user's preferences based on data of products the user has purchased in the past. The generation unit can also utilize survey results. For example, the generation unit can generate a route that suits the user's preferences based on the results of surveys the user has answered in the past. Furthermore, the generation unit can also utilize behavioral history. For example, the generation unit can generate a route that suits the user's preferences based on the user's browsing history of stores and products they have visited in the past. This allows for a more personalized shopping experience by generating routes based on the user's preferences. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user preference data into a generation AI and have the generation AI generate the optimal route.

[0039] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display product names and features that the user has frequently entered in the past as suggestions. For example, the reception desk can display related product names and features as suggestions based on product names and features that the user has entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can suggest the optimal input method based on the input methods the user has used in the past. Furthermore, the reception desk can predict and suggest product names and features that the user will use during specific time periods based on the user's past input history. For example, the reception desk analyzes the user's past input history and predicts and suggests product names and features that will be used during specific time periods. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's input history data into a generating AI and have the generating AI perform the task of suggesting the optimal input method.

[0040] The reception unit can present input suggestions based on the user's current areas of interest when input is received. For example, the reception unit can display relevant product names and features as suggestions based on the product categories the user has recently searched for. The reception unit can also suggest product names and features related to products the user has recently purchased. For example, the reception unit can suggest relevant product names and features as suggestions based on products the user has recently purchased. Furthermore, the reception unit can present relevant product names and features as suggestions based on reviews and ratings of products the user has recently viewed. For example, the reception unit can present relevant product names and features as suggestions based on reviews and ratings of products the user has recently viewed. This allows for the provision of more appropriate input suggestions by presenting input suggestions based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's areas of interest data into a generating AI and have the generating AI perform the task of presenting input suggestions.

[0041] The reception desk can present highly relevant input suggestions when receiving input, taking into account the user's geographical location. For example, the reception desk can display relevant product names and features as suggestions based on the inventory status of the store the user is currently in. The reception desk can also suggest relevant product names and features as suggestions based on the inventory status of stores the user has visited in the past. Furthermore, the reception desk can present relevant product names and features as suggestions based on popular products and trends in the area the user is currently in. By presenting input suggestions while considering the user's geographical location, more appropriate input suggestions can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the generating AI and have the AI ​​suggest input candidates.

[0042] The reception unit can analyze the user's social media activity when input is received and present relevant input suggestions. For example, the reception unit can display products or features mentioned by the user on social media as suggestions. For example, the reception unit can display relevant product names and features as suggestions based on products or features mentioned by the user on social media. The reception unit can also suggest products or features recommended by influencers that the user follows. For example, the reception unit can suggest relevant product names and features as suggestions based on products or features recommended by influencers that the user follows. Furthermore, the reception unit can present relevant product names and features as suggestions based on reviews and ratings shared by the user on social media. For example, the reception unit can present relevant product names and features as suggestions based on reviews and ratings shared by the user on social media. In this way, relevant input suggestions can be provided by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the presentation of input suggestions.

[0043] The analysis unit can predict the current inventory status by referring to past inventory data during analysis. For example, the analysis unit can predict the current inventory status based on past inventory data and provide it to the user. The analysis unit can also analyze past inventory data and predict the likelihood of a particular product running out of stock. For example, the analysis unit can analyze past inventory data and predict the likelihood of a particular product running out of stock. Furthermore, the analysis unit can refer to past inventory data to predict the arrival time of a particular product. For example, the analysis unit refers to past inventory data to predict the arrival time of a particular product. This allows for accurate prediction of the current inventory status by referring to past inventory data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past inventory data into a generative AI and have the generative AI perform a prediction of the current inventory status.

[0044] The analysis unit can apply different analysis algorithms to each product category during analysis. For example, the analysis unit can apply different analysis algorithms to each category such as clothing, accessories, and shoes to improve accuracy. The analysis unit can also apply different demand forecasting algorithms to each product category to optimize inventory management. Furthermore, the analysis unit can analyze different sales data for each product category to grasp trends. For example, the analysis unit analyzes different sales data for each product category to grasp trends. This improves the accuracy of the analysis by applying different analysis algorithms to each product category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data for each product category into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0045] The analysis unit can perform analysis while considering the geographical distribution of products. For example, the analysis unit can predict demand in a specific region while considering the geographical distribution of products. The analysis unit can also optimize inventory management in a specific region based on the geographical distribution of products. Furthermore, the analysis unit can analyze the geographical distribution of products and formulate sales strategies for each region. By performing analysis while considering the geographical distribution of products, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input geographical distribution data of products into a generative AI and have the generative AI perform the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the product during the analysis process. For example, the analysis unit can refer to relevant literature on the product to understand the product's characteristics and trends. The analysis unit can also perform demand forecasting for the product based on relevant literature. Furthermore, the analysis unit can perform competitive analysis of the product by referring to relevant literature. This improves the accuracy of the analysis by referring to relevant literature on the product. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input relevant literature data on the product into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0047] The generation unit can improve the accuracy of route generation by considering the interrelationships between products. For example, the generation unit can generate routes that place related products close together. The generation unit can also generate routes that efficiently visit products by considering the interrelationships between products. Furthermore, the generation unit can generate routes tailored to user preferences based on the interrelationships between products. This allows for the provision of more efficient routes by considering the interrelationships between products. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input product interrelationship data into a generation AI and have the generation AI perform improvements to the accuracy of route generation.

[0048] The generation unit can generate the optimal route by considering the placement information of products when generating a route. For example, the generation unit can generate the shortest route based on the placement information of products within a store. The generation unit can also generate a route that efficiently visits products by considering the placement information of products. Furthermore, the generation unit can generate a route tailored to the user's preferences based on the placement information of products. In this way, the optimal route can be provided by considering the placement information of products. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the placement information of products into a generation AI and have the generation AI generate the optimal route.

[0049] The generation unit can perform route generation while considering the geographical distribution of products. For example, the generation unit can predict demand in a specific region while considering the geographical distribution of products. The generation unit can also optimize inventory management in a specific region based on the geographical distribution of products. Furthermore, the generation unit can analyze the geographical distribution of products and formulate sales strategies for each region. This allows for the provision of more appropriate routes by considering the geographical distribution of products. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input geographical distribution data of products into a generation AI and have the generation AI perform route generation.

[0050] The generation unit can improve the accuracy of route generation by referring to relevant literature on the product. For example, the generation unit can refer to relevant literature on the product to understand the product's characteristics and trends. The generation unit can also perform demand forecasting for the product based on relevant literature. Furthermore, the generation unit can perform competitive analysis of the product by referring to relevant literature. This improves the accuracy of generation by referring to relevant literature on the product. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input relevant literature data on the product into the generation AI and have the generation AI perform the accuracy improvement.

[0051] The navigation unit can select the optimal navigation method by referring to the user's past travel history during navigation. For example, the navigation unit can propose the optimal navigation method based on routes previously used by the user. The navigation unit can also propose a navigation method that avoids congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and propose the most efficient navigation method. In this way, the navigation unit can provide the optimal navigation method by referring to the user's past travel history. Some or all of the above processing in the navigation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the navigation unit can input the user's travel history data into a generative AI and have the generative AI select the navigation method.

[0052] The navigation unit can customize the navigation method based on the user's current location information during navigation. For example, the navigation unit can suggest the optimal navigation method based on the user's current location. The navigation unit can also update the user's current location information in real time and suggest the optimal route. Furthermore, if the user gets lost, the navigation unit can perform navigation again based on the current location. This allows for more appropriate navigation by customizing the navigation method based on the user's current location information. Some or all of the above processing in the navigation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the navigation unit can input the user's location data into a generative AI and have the generative AI perform the customization of the navigation method.

[0053] The navigation unit can select the optimal navigation method during navigation by considering the user's geographical location information. For example, the navigation unit can propose the optimal navigation method by considering the geographical characteristics of the area where the user is currently located. The navigation unit can also propose the optimal navigation method based on the geographical characteristics of areas the user has visited in the past. Furthermore, the navigation unit can propose the optimal navigation method by considering the traffic conditions in the area where the user is currently located. In this way, the optimal navigation method can be provided by considering the user's geographical location information. Some or all of the above processing in the navigation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the navigation unit can input the user's geographical location information into a generative AI and have the generative AI select the navigation method.

[0054] The navigation unit can analyze the user's social media activity during navigation and suggest navigation options. For example, the navigation unit can suggest the optimal navigation option based on places mentioned by the user on social media. The navigation unit can also suggest navigation options based on places recommended by influencers the user follows. Furthermore, the navigation unit can suggest the optimal navigation option based on reviews and ratings shared by the user on social media. In this way, the navigation unit can provide the optimal navigation option by analyzing the user's social media activity. Some or all of the above processing in the navigation unit may be performed using, for example, generative AI, or without generative AI. For example, the navigation unit can input the user's social media activity data into a generative AI and have the generative AI suggest navigation options.

[0055] The feedback reception unit can select the optimal feedback reception method by referring to the user's past feedback history when receiving feedback. For example, the feedback reception unit can propose the optimal feedback reception method based on the feedback the user has previously entered. For example, the feedback reception unit can propose the optimal feedback reception method based on the feedback the user has previously entered. The feedback reception unit can also predict and propose trends in feedback entered at specific time periods based on the user's past feedback history. For example, the feedback reception unit can predict and propose trends in feedback entered at specific time periods based on the user's past feedback history. Furthermore, the feedback reception unit can analyze the user's past feedback history and propose the most efficient feedback reception method. For example, the feedback reception unit can analyze the user's past feedback history and propose the most efficient feedback reception method. In this way, the optimal feedback reception method can be provided by referring to the user's past feedback history. Some or all of the above processing in the feedback reception unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback reception unit can input the user's feedback history data into a generative AI and have the generative AI select the feedback reception method.

[0056] The feedback reception unit can select the optimal feedback reception method by considering the user's geographical location information when receiving feedback. For example, the feedback reception unit can propose the optimal feedback reception method by considering the characteristics of the area where the user is currently located. The feedback reception unit can also propose the optimal feedback reception method based on the characteristics of areas the user has visited in the past. For example, the feedback reception unit can propose the optimal feedback reception method based on the characteristics of areas the user has visited in the past. Furthermore, the feedback reception unit can propose the optimal feedback reception method by considering the trends of the area where the user is currently located. For example, the feedback reception unit can propose the optimal feedback reception method by considering the trends of the area where the user is currently located. In this way, the optimal feedback reception method can be provided by considering the user's geographical location information. Some or all of the above processing in the feedback reception unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback reception unit can input the user's geographical location information into a generative AI and have the generative AI select the feedback reception method.

[0057] The history utilization unit can optimize the history algorithm by referring to past history data when utilizing history. For example, the history utilization unit can select the optimal history algorithm based on past history data. The history utilization unit can also analyze past history data and optimize the history algorithm to be used in a specific time period. Furthermore, the history utilization unit can optimize the history algorithm for a specific product by referring to past history data. In this way, the history algorithm can be optimized by referring to past history data. Some or all of the above processing in the history utilization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the history utilization unit can input past history data into a generative AI and have the generative AI perform the optimization of the history algorithm.

[0058] The history utilization unit can weight historical data based on the product submission date when utilizing history data. For example, the history utilization unit weights historical data based on the product submission date. The history utilization unit can also prioritize the use of historical data for specific products, taking into account the product submission date. Furthermore, the history utilization unit can optimize the weighting of historical data based on the product submission date. For example, the history utilization unit optimizes the weighting of historical data based on the product submission date. This allows for the provision of more appropriate historical data by weighting historical data based on the product submission date. Some or all of the above processing in the history utilization unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the history utilization unit can input product submission date data into a generation AI and have the generation AI perform the weighting of historical data.

[0059] The explanation unit can select the optimal explanation method by referring to the user's past explanation history during an explanation. For example, the explanation unit can suggest the optimal explanation method based on the explanations the user has received in the past. The explanation unit can also predict and suggest an explanation method to be used at a specific time of day based on the user's past explanation history. Furthermore, the explanation unit can analyze the user's past explanation history and suggest the most efficient explanation method. In this way, the optimal explanation method can be provided by referring to the user's past explanation history. Some or all of the above processing in the explanation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the explanation unit can input the user's explanation history data into a generative AI and have the generative AI select an explanation method.

[0060] The explanation unit can select the optimal explanation method while considering the user's geographical location information. For example, the explanation unit can propose the optimal explanation method by considering the characteristics of the area where the user is currently located. The explanation unit can also propose the optimal explanation method based on the characteristics of areas the user has visited in the past. Furthermore, the explanation unit can also propose the optimal explanation method by considering the trends of the area where the user is currently located. In this way, the optimal explanation method can be provided by considering the user's geographical location information. Some or all of the above processing in the explanation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the explanation unit can input the user's geographical location information into a generative AI and have the generative AI select the explanation method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The reception desk not only receives user input but can also provide appropriate feedback based on that input. For example, if a user is looking for a specific product, the reception desk can immediately provide information on the product's availability and similar products. The reception desk can also suggest relevant promotions and discounts based on the information the user has entered. Furthermore, the reception desk can analyze user input and proactively provide the information the user is looking for. This allows users to quickly obtain the information they need, improving their shopping experience.

[0063] The feedback department not only gathers real-time user feedback but can also use that feedback to suggest product improvements. For example, if a user comments that they "don't like the color" of a particular product, the feedback department can provide that information to the manufacturer and suggest increasing the product's color variations. The feedback department can also compile user feedback and analyze trends in popular and unpopular products. Furthermore, based on user feedback, the feedback department can provide product reviews and ratings to other users. This allows users to make better purchases by referring to the opinions of other users.

[0064] The history utilization unit not only uses users' past purchase history and ratings, but can also analyze user behavior patterns to predict future purchasing behavior. For example, if a user tends to purchase certain products during a particular season, the history utilization unit can suggest related products as that season approaches. Furthermore, based on a user's past purchase history, the history utilization unit can suggest new products that the user might be interested in. In addition, the history utilization unit can analyze user rating data, extract the characteristics of products that users have given high ratings to, and suggest similar products. This makes it easier for users to find products that suit their preferences, improving their shopping experience.

[0065] The product description section can not only explain the differences in product functions and materials, but also offer customized suggestions tailored to user needs. For example, if a user is looking for a product suitable for a specific purpose, the description section can suggest the most suitable product for that purpose. Furthermore, if a user prioritizes a particular function, the description section can compare products with that function and suggest the best one. Additionally, if a user has a preference for a specific brand or design, the description section can suggest products related to that brand or design. This makes it easier for users to find products that meet their needs, improving their shopping experience.

[0066] The analytics department not only monitors inventory levels in conjunction with the store's inventory management system, but can also forecast demand based on inventory data. For example, it can analyze past sales data to predict when demand for a particular product will increase. It can also suggest replenishment of specific products before they run out, based on inventory data. Furthermore, the analytics department can monitor inventory data in real time and respond quickly to inventory fluctuations. This allows stores to streamline inventory management and provide users with always up-to-date inventory information.

[0067] The generation unit can not only generate the optimal route based on user preferences, but also optimize the route based on the user's browsing history. For example, it can analyze the user's browsing history of stores and products they have visited in the past and prioritize incorporating products that the user is likely to be interested in into the route. It can also analyze the user's behavior patterns and suggest a route that allows them to efficiently navigate through stores. Furthermore, it can prioritize suggesting products from specific brands or categories based on the user's preferences. This allows users to efficiently find products that match their interests and preferences, improving their shopping experience.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, it accepts information entered by the user using a smartphone or tablet. It can also accept voice input using speech recognition technology, or analyze captured images using image recognition technology and accept them as input information. Step 2: The analysis unit analyzes the store's inventory status based on the information received by the reception unit. The analysis unit works in conjunction with the store's inventory management system to grasp the inventory status in real time and check whether the product entered by the user is in stock. It can also take into account the frequency of inventory data updates to grasp the latest inventory status. Step 3: The generation unit generates a route based on the information analyzed by the analysis unit. The generation unit uses generation AI to generate the optimal route so that users can efficiently find products. It understands the placement and inventory status of products within the store and proposes a route so that users can reach their desired products. It can also generate personalized routes based on the user's preferences. Step 4: The navigation unit navigates based on the route generated by the generation unit. The navigation unit guides the user to the desired product using voice and visual guidance. It understands the user's current location and provides real-time directions to the desired product. It can also incorporate real-time user feedback and reflect it in the route.

[0070] (Example of form 2) The shopping navigation AI according to an embodiment of the present invention is a system that improves the shopping experience in offline stores. This shopping navigation AI analyzes the inventory status in the store and generates and navigates the user to the store by inputting desired or preferred products. For example, if a user inputs a specific product name or characteristic such as "red sweater" or "casual jacket," the generating AI analyzes the store's inventory status and generates the optimal route to reach the desired product. The generating AI understands the placement and inventory status of products in the store and proposes a route that allows the user to find products efficiently. For example, if a user is looking for a red sweater in a Uniqlo store, the generating AI identifies the location where the red sweater is located and proposes the shortest route to that location. Furthermore, the generating AI interactively incorporates the user's real-time feedback and reflects it in the route. For example, if a user says, "This sweater is a little big," the generating AI will use that information to suggest other sweaters of different sizes. Also, if a user says, "I don't like the material of this jacket," the generating AI will suggest jackets made of different materials. This system allows users to quickly find the products they are looking for, improving the offline shopping experience. Furthermore, the generative AI learns user preferences and can provide more personalized suggestions. For example, if a user prefers casual styles, the generative AI will prioritize suggesting casual items. The generative AI also has the ability to show and compare similar products and explain the differences in features and materials. This allows users to compare multiple products and choose the best one for them. For example, the generative AI might explain, "This sweater is made of wool, and this sweater is made of cotton," helping users understand the difference in materials. In this way, the shopping navigation AI improves the offline shopping experience by understanding user preferences and suggesting the optimal route while incorporating real-time feedback.

[0071] The shopping navigation AI according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a navigation unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit receives information entered by the user using a smartphone or tablet, for example. The reception unit can also receive voice input from the user using voice recognition technology. Furthermore, the reception unit can analyze images taken by the user using image recognition technology and accept them as input information. For example, the reception unit analyzes an image of a product taken by the user with a smartphone and accepts information about that product as input. The analysis unit analyzes the store's inventory status based on the information received by the reception unit. The analysis unit, for example, works in conjunction with the store's inventory management system to grasp the inventory status in real time. The analysis unit analyzes the inventory status of the product entered by the user based on the inventory data obtained from the inventory management system. For example, the analysis unit analyzes the inventory data obtained from the inventory management system to check whether the product entered by the user is in stock. The analysis unit can also grasp the latest inventory status by considering the frequency of inventory data updates. The generation unit generates routes based on the information analyzed by the analysis unit. For example, the generation unit uses generation AI to generate the optimal route so that users can find products efficiently. The generation unit understands the placement and inventory status of products in the store and proposes routes so that users can reach their desired products. For example, the generation unit uses generation AI to analyze the placement information of products in the store and generate the shortest route. The generation unit can also generate personalized routes based on the user's preferences. For example, if the generation unit prefers casual styles, it will generate a route that prioritizes suggesting casual products. The navigation unit navigates based on the route generated by the generation unit. The navigation unit uses, for example, voice guidance or visual guidance to guide the user to their desired product. The navigation unit provides real-time navigation information so that users do not get lost in the store.For example, the navigation unit can determine the user's current location and guide them to the desired product in real time. The navigation unit can also incorporate the user's real-time feedback and reflect it in the route. For example, if the user says, "This sweater is a little big," the navigation unit will use that information to suggest other sweaters of different sizes. In this way, the shopping navigation AI according to the embodiment improves the shopping experience in offline stores by generating and navigating the optimal route based on the user's input. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's real-time feedback into a generating AI and have the generating AI adjust the route based on that feedback.

[0072] The reception desk receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception desk can, for example, receive information entered by the user using a smartphone or tablet. The reception desk can also receive voice input from the user using speech recognition technology. Furthermore, the reception desk can analyze images taken by the user using image recognition technology and accept them as input information. For example, the reception desk can analyze an image of a product taken by the user with a smartphone and accept information about that product as input. The reception desk utilizes advanced natural language processing and image analysis technologies to process the information entered by the user quickly and accurately. For example, in the case of voice input, the speech recognition engine converts the user's speech into text and analyzes that text to understand the user's intent. In the case of image input, the image recognition engine extracts the features of the product and identifies the product by matching it with information in the database. This allows the reception desk to support diverse input formats and respond flexibly to user needs. Furthermore, the reception desk can learn the user's past input history and behavior patterns to provide more personalized services. For example, if a user has frequently searched for products from a particular brand in the past, products from that brand will be displayed preferentially. Furthermore, the reception desk can analyze user input in real time and provide immediate feedback. This allows users to shop smoothly and efficiently. To protect user privacy, the reception desk properly manages entered information and uses encryption technology as needed to ensure data security. This allows users to use the service with peace of mind.

[0073] The analysis department analyzes store inventory status based on information received by the reception department. For example, the analysis department integrates with the store's inventory management system to grasp inventory status in real time. Based on inventory data obtained from the inventory management system, the analysis department analyzes the inventory status of products entered by users. For example, the analysis department analyzes inventory data obtained from the inventory management system to check whether the product entered by the user is in stock. The analysis department can also grasp the latest inventory status by considering the frequency of inventory data updates. The analysis department utilizes AI technology to perform inventory data analysis efficiently and accurately. For example, it can use machine learning algorithms to learn patterns in inventory data and optimize demand forecasting and inventory replenishment. This allows the analysis department to quickly grasp the inventory status of products users are looking for and provide appropriate information. Furthermore, the analysis department can integrate inventory data from multiple stores and present the inventory status of the store closest to the user. This allows users to find products efficiently. The analysis department can also predict inventory fluctuations by considering past sales data and seasonal trends, and suggest products to users at the appropriate time. For example, if a particular product experiences seasonally high demand, the system will notify users before it goes out of stock. The analytics department can also provide personalized product suggestions based on users' purchase history and preferences. This allows users to efficiently find products that suit their tastes. The analytics department implements appropriate access controls and data encryption to ensure data security and privacy, and manages user information securely.

[0074] The generation unit generates routes based on information analyzed by the analysis unit. For example, the generation unit uses generational AI to generate the optimal route so that users can efficiently find products. The generation unit understands the placement and inventory status of products within the store and proposes a route so that users can reach their desired products. For example, the generation unit uses generational AI to analyze the placement information of products within the store and generate the shortest route. The generation unit can also generate personalized routes based on user preferences. For example, if the generation unit prefers casual styles, it will generate a route that prioritizes suggesting casual products. The generation unit utilizes AI technology to generate the optimal route based on user input information and past behavior history. For example, it analyzes data on stores the user has visited and products they have purchased in the past to learn the user's preferences and purchasing patterns. This allows the generation unit to propose the most efficient and satisfying route for the user. Furthermore, the generation unit can dynamically adjust the route considering real-time updated inventory information and store congestion. For example, if a specific product is out of stock or a particular area in the store is crowded, the route generation unit immediately recalculates the route and provides the user with the optimal route. Furthermore, the generation unit can continuously improve the accuracy and efficiency of routes by incorporating user feedback. For instance, if a user provides feedback such as "this route is a detour," the route generation algorithm is adjusted based on that information and reflected in future route suggestions. This allows the generation unit to provide flexible and personalized routes tailored to user needs, improving the shopping experience.

[0075] The navigation unit navigates based on routes generated by the generation unit. The navigation unit guides users to their desired products using, for example, voice and visual guidance. The navigation unit provides real-time navigation information to prevent users from getting lost in the store. For example, the navigation unit tracks the user's current location and provides real-time directions to the desired product. The navigation unit can also incorporate real-time user feedback and reflect it in the route. For instance, if a user comments that "this sweater is a little too big," the navigation unit will use that information to suggest other sweaters in different sizes. The navigation unit utilizes GPS and beacon technology to accurately determine the user's current location. This allows it to track the user's location in the store in real time and provide optimal navigation information. Furthermore, the navigation unit detects the user's speed and direction and provides guidance information at the appropriate time. For example, if a user stops in front of a specific product, it will display detailed information about that product and suggest related products. The navigation unit can also continuously improve navigation information based on user feedback. For example, if a user provides feedback such as "This guidance is difficult to understand," the guidance method will be adjusted based on that information and reflected in future navigation. The navigation unit can provide information to the user by combining multiple means, such as voice guidance, visual guidance, vibration notifications, and flashing lights. This allows users to receive navigation information not only through sight and hearing, but also through touch. To protect user privacy, the navigation unit appropriately manages location information and movement history, and ensures data security using encryption technology as needed. This allows users to use the service with peace of mind.

[0076] The feedback collection unit can incorporate real-time user feedback. For example, it can collect feedback by having users enter text comments. For example, it can accept text comments entered by users using smartphones or tablets. The feedback collection unit can also collect voice feedback. For example, it can convert voice feedback from users into text using speech recognition technology and collect it. Furthermore, the feedback collection unit can also collect rating scores. For example, it can accept rating scores entered by users as a number of stars. This allows for the provision of more personalized routes by reflecting real-time user feedback. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or not using AI. For example, the feedback collection unit can input user voice feedback into a generating AI and have the generating AI perform analysis of the feedback.

[0077] The history utilization unit can utilize the user's past purchase history and ratings. For example, the history utilization unit collects and utilizes information such as the user's purchase date and time, purchased items, and purchase frequency. For example, the history utilization unit suggests related products based on information about products the user has purchased in the past. The history utilization unit can also utilize the user's ratings. For example, the history utilization unit suggests products that match the user's preferences based on the star ratings and comment ratings the user has given in the past. Furthermore, the history utilization unit can also utilize survey results. For example, the history utilization unit suggests products that match the user's preferences based on the results of surveys the user has answered in the past. In this way, by utilizing the user's past purchase history and ratings, it becomes possible to make more appropriate product suggestions. Some or all of the above processing in the history utilization unit may be performed using AI, for example, or without using AI. For example, the history utilization unit can input the user's purchase history data into a generating AI and have the generating AI execute suggestions for related products.

[0078] The explanatory section can explain the differences in the functions and materials of products. For example, the explanatory section can perform performance comparisons of products. For example, the explanatory section can compare the performance of different products and explain them to the user. The explanatory section can also explain the properties of materials. For example, the explanatory section can explain the differences between wool and cotton sweaters. Furthermore, the explanatory section can explain examples of product use. For example, the explanatory section can explain in what situations a particular jacket is used. This makes it easier for users to compare products by explaining the differences in their functions and materials. Some or all of the above processing in the explanatory section may be performed using AI, for example, or not using AI. For example, the explanatory section can input product performance data into a generating AI and have the generating AI perform the performance comparison explanation.

[0079] The analysis unit can understand inventory status by linking with the store's inventory management system. For example, the analysis unit can acquire inventory data in real time from the inventory management system. For example, the analysis unit can understand the inventory quantity and inventory update frequency of products by linking with the inventory management system. The analysis unit can also understand the latest inventory status by considering the inventory data update frequency. For example, the analysis unit can understand the inventory status of products in real time based on the inventory data update frequency. This allows for accurate understanding of inventory status by linking with the store's inventory management system. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input inventory data acquired from the inventory management system into a generating AI and have the generating AI perform an analysis of the inventory status.

[0080] The generation unit can generate the optimal route based on the user's preferences. For example, the generation unit can collect the user's past preference data and use it to generate the route. For example, the generation unit can generate a route that suits the user's preferences based on data of products the user has purchased in the past. The generation unit can also utilize survey results. For example, the generation unit can generate a route that suits the user's preferences based on the results of surveys the user has answered in the past. Furthermore, the generation unit can also utilize behavioral history. For example, the generation unit can generate a route that suits the user's preferences based on the user's browsing history of stores and products they have visited in the past. This allows for a more personalized shopping experience by generating routes based on the user's preferences. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user preference data into a generation AI and have the generation AI generate the optimal route.

[0081] The reception desk can estimate the user's emotions and adjust the input reception method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the reception desk estimates that the user is stressed, it can provide simple input options. The reception desk can also provide detailed input options and suggest customizable input methods if the user is relaxed. For example, if the reception desk estimates that the user is relaxed, it can provide detailed input options. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of product names and features. For example, if the reception desk estimates that the user is in a hurry, it can prioritize voice input. This allows for the provision of more appropriate input methods by adjusting the input reception method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0082] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display product names and features that the user has frequently entered in the past as suggestions. For example, the reception desk can display related product names and features as suggestions based on product names and features that the user has entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can suggest the optimal input method based on the input methods the user has used in the past. Furthermore, the reception desk can predict and suggest product names and features that the user will use during specific time periods based on the user's past input history. For example, the reception desk analyzes the user's past input history and predicts and suggests product names and features that will be used during specific time periods. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's input history data into a generating AI and have the generating AI perform the task of suggesting the optimal input method.

[0083] The reception unit can present input suggestions based on the user's current areas of interest when input is received. For example, the reception unit can display relevant product names and features as suggestions based on the product categories the user has recently searched for. The reception unit can also suggest product names and features related to products the user has recently purchased. For example, the reception unit can suggest relevant product names and features as suggestions based on products the user has recently purchased. Furthermore, the reception unit can present relevant product names and features as suggestions based on reviews and ratings of products the user has recently viewed. For example, the reception unit can present relevant product names and features as suggestions based on reviews and ratings of products the user has recently viewed. This allows for the provision of more appropriate input suggestions by presenting input suggestions based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's areas of interest data into a generating AI and have the generating AI perform the task of presenting input suggestions.

[0084] The reception desk can estimate the user's emotions and prioritize input based on the estimated emotions. For example, if the user is in a hurry, the reception desk can prioritize inputting the most important product names or features. For example, if the reception desk estimates that the user is in a hurry, it can prioritize inputting important product names or features. The reception desk can also provide detailed input options and suggest customizable input methods if the user is relaxed. For example, if the reception desk estimates that the user is relaxed, it can provide detailed input options. Furthermore, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the reception desk estimates that the user is stressed, it can provide simple input options. This allows for a more appropriate input method by prioritizing input according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0085] The reception desk can present highly relevant input suggestions when receiving input, taking into account the user's geographical location. For example, the reception desk can display relevant product names and features as suggestions based on the inventory status of the store the user is currently in. The reception desk can also suggest relevant product names and features as suggestions based on the inventory status of stores the user has visited in the past. Furthermore, the reception desk can present relevant product names and features as suggestions based on popular products and trends in the area the user is currently in. By presenting input suggestions while considering the user's geographical location, more appropriate input suggestions can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the generating AI and have the AI ​​suggest input candidates.

[0086] The reception unit can analyze the user's social media activity when input is received and present relevant input suggestions. For example, the reception unit can display products or features mentioned by the user on social media as suggestions. For example, the reception unit can display relevant product names and features as suggestions based on products or features mentioned by the user on social media. The reception unit can also suggest products or features recommended by influencers that the user follows. For example, the reception unit can suggest relevant product names and features as suggestions based on products or features recommended by influencers that the user follows. Furthermore, the reception unit can present relevant product names and features as suggestions based on reviews and ratings shared by the user on social media. For example, the reception unit can present relevant product names and features as suggestions based on reviews and ratings shared by the user on social media. In this way, relevant input suggestions can be provided by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the presentation of input suggestions.

[0087] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis and prioritize providing the most important information. For example, if the analysis unit estimates that the user is in a hurry, it can perform a rapid analysis and prioritize providing important information. The analysis unit can also perform a detailed analysis and provide customized information if the user is relaxed. For example, if the analysis unit estimates that the user is relaxed, it can perform a detailed analysis and provide customized information. Furthermore, if the user is stressed, the analysis unit can provide a simple analysis result to avoid information overload. For example, if the analysis unit estimates that the user is stressed, it can provide a simple analysis result. This allows for more appropriate analysis results by adjusting the accuracy of the analysis 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. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0088] The analysis unit can predict the current inventory status by referring to past inventory data during analysis. For example, the analysis unit can predict the current inventory status based on past inventory data and provide it to the user. The analysis unit can also analyze past inventory data and predict the likelihood of a particular product running out of stock. For example, the analysis unit can analyze past inventory data and predict the likelihood of a particular product running out of stock. Furthermore, the analysis unit can refer to past inventory data to predict the arrival time of a particular product. For example, the analysis unit refers to past inventory data to predict the arrival time of a particular product. This allows for accurate prediction of the current inventory status by referring to past inventory data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past inventory data into a generative AI and have the generative AI perform a prediction of the current inventory status.

[0089] The analysis unit can apply different analysis algorithms to each product category during analysis. For example, the analysis unit can apply different analysis algorithms to each category such as clothing, accessories, and shoes to improve accuracy. The analysis unit can also apply different demand forecasting algorithms to each product category to optimize inventory management. Furthermore, the analysis unit can analyze different sales data for each product category to grasp trends. For example, the analysis unit analyzes different sales data for each product category to grasp trends. This improves the accuracy of the analysis by applying different analysis algorithms to each product category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data for each product category into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0090] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the analysis unit estimates that the user is tense, it can provide a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the analysis unit estimates that the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the analysis unit estimates that the user is in a hurry, it can provide a display method that gets straight to the point. For example, if the analysis unit estimates that the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0091] The analysis unit can perform analysis while considering the geographical distribution of products. For example, the analysis unit can predict demand in a specific region while considering the geographical distribution of products. The analysis unit can also optimize inventory management in a specific region based on the geographical distribution of products. Furthermore, the analysis unit can analyze the geographical distribution of products and formulate sales strategies for each region. By performing analysis while considering the geographical distribution of products, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input geographical distribution data of products into a generative AI and have the generative AI perform the analysis.

[0092] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the product during the analysis process. For example, the analysis unit can refer to relevant literature on the product to understand the product's characteristics and trends. The analysis unit can also perform demand forecasting for the product based on relevant literature. Furthermore, the analysis unit can perform competitive analysis of the product by referring to relevant literature. This improves the accuracy of the analysis by referring to relevant literature on the product. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input relevant literature data on the product into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0093] The generation unit can estimate the user's emotions and adjust the route generation criteria based on the estimated emotions. For example, if the user is in a hurry, the generation unit will prioritize generating the shortest route. For example, if the generation unit estimates that the user is in a hurry, it will prioritize generating the shortest route. The generation unit can also generate a route that allows the user to stroll through the store at a leisurely pace if the user is relaxed. For example, if the generation unit estimates that the user is relaxed, it will generate a route that allows the user to stroll through the store at a leisurely pace. Furthermore, if the generation unit is feeling stressed, it can generate a route that avoids crowds. For example, if the generation unit estimates that the user is stressed, it will generate a route that avoids crowds. In this way, by adjusting the route generation criteria according to the user's emotions, a more appropriate route can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0094] The generation unit can improve the accuracy of route generation by considering the interrelationships between products. For example, the generation unit can generate routes that place related products close together. The generation unit can also generate routes that efficiently visit products by considering the interrelationships between products. Furthermore, the generation unit can generate routes tailored to user preferences based on the interrelationships between products. This allows for the provision of more efficient routes by considering the interrelationships between products. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input product interrelationship data into a generation AI and have the generation AI perform improvements to the accuracy of route generation.

[0095] The generation unit can generate the optimal route by considering the placement information of products when generating a route. For example, the generation unit can generate the shortest route based on the placement information of products within a store. The generation unit can also generate a route that efficiently visits products by considering the placement information of products. Furthermore, the generation unit can generate a route tailored to the user's preferences based on the placement information of products. In this way, the optimal route can be provided by considering the placement information of products. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the placement information of products into a generation AI and have the generation AI generate the optimal route.

[0096] The generation unit can estimate the user's emotions and adjust the display method of the generated route based on the estimated user emotions. For example, if the user is tense, the generation unit can provide a simple and highly visible display method. For example, if the generation unit estimates that the user is tense, it can provide a simple and highly visible display method. The generation unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the generation unit estimates that the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the generation unit estimates that the user is in a hurry, it can provide a display method that gets to the point. For example, if the generation unit estimates that the user is in a hurry, it can provide a display method that gets to the point. In this way, by adjusting the display method of the route according to the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0097] The generation unit can perform route generation while considering the geographical distribution of products. For example, the generation unit can predict demand in a specific region while considering the geographical distribution of products. The generation unit can also optimize inventory management in a specific region based on the geographical distribution of products. Furthermore, the generation unit can analyze the geographical distribution of products and formulate sales strategies for each region. This allows for the provision of more appropriate routes by considering the geographical distribution of products. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input geographical distribution data of products into a generation AI and have the generation AI perform route generation.

[0098] The generation unit can improve the accuracy of route generation by referring to relevant literature on the product. For example, the generation unit can refer to relevant literature on the product to understand the product's characteristics and trends. The generation unit can also perform demand forecasting for the product based on relevant literature. Furthermore, the generation unit can perform competitive analysis of the product by referring to relevant literature. This improves the accuracy of generation by referring to relevant literature on the product. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input relevant literature data on the product into the generation AI and have the generation AI perform the accuracy improvement.

[0099] The navigation unit can estimate the user's emotions and adjust the navigation method based on the estimated emotions. For example, if the user is tense, the navigation unit can provide a simple and highly visible navigation method. For example, if the navigation unit estimates that the user is tense, it can provide a simple and highly visible navigation method. The navigation unit can also provide a navigation method that includes detailed information if the user is relaxed. For example, if the navigation unit estimates that the user is relaxed, it can provide a navigation method that includes detailed information. Furthermore, if the navigation unit estimates that the user is in a hurry, it can provide a concise navigation method. For example, if the navigation unit estimates that the user is in a hurry, it can provide a concise navigation method. In this way, by adjusting the navigation method according to the user's emotions, a more appropriate navigation method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0100] The navigation unit can select the optimal navigation method by referring to the user's past travel history during navigation. For example, the navigation unit can propose the optimal navigation method based on routes previously used by the user. The navigation unit can also propose a navigation method that avoids congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and propose the most efficient navigation method. In this way, the navigation unit can provide the optimal navigation method by referring to the user's past travel history. Some or all of the above processing in the navigation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the navigation unit can input the user's travel history data into a generative AI and have the generative AI select the navigation method.

[0101] The navigation unit can customize the navigation method based on the user's current location information during navigation. For example, the navigation unit can suggest the optimal navigation method based on the user's current location. The navigation unit can also update the user's current location information in real time and suggest the optimal route. Furthermore, if the user gets lost, the navigation unit can perform navigation again based on the current location. This allows for more appropriate navigation by customizing the navigation method based on the user's current location information. Some or all of the above processing in the navigation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the navigation unit can input the user's location data into a generative AI and have the generative AI perform the customization of the navigation method.

[0102] The navigation unit can estimate the user's emotions and determine navigation priorities based on the estimated emotions. For example, if the user is in a hurry, the navigation unit will prioritize providing the most important navigation information. For example, if the navigation unit estimates that the user is in a hurry, it will prioritize providing the most important navigation information. The navigation unit can also provide detailed navigation information if the user is relaxed. For example, if the navigation unit estimates that the user is relaxed, it will provide detailed navigation information. Furthermore, if the user is stressed, the navigation unit can provide simple navigation information. For example, if the navigation unit estimates that the user is stressed, it will provide simple navigation information. This allows for the provision of more appropriate navigation information by determining navigation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0103] The navigation unit can select the optimal navigation method during navigation by considering the user's geographical location information. For example, the navigation unit can propose the optimal navigation method by considering the geographical characteristics of the area where the user is currently located. The navigation unit can also propose the optimal navigation method based on the geographical characteristics of areas the user has visited in the past. Furthermore, the navigation unit can propose the optimal navigation method by considering the traffic conditions in the area where the user is currently located. In this way, the optimal navigation method can be provided by considering the user's geographical location information. Some or all of the above processing in the navigation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the navigation unit can input the user's geographical location information into a generative AI and have the generative AI select the navigation method.

[0104] The navigation unit can analyze the user's social media activity during navigation and suggest navigation options. For example, the navigation unit can suggest the optimal navigation option based on places mentioned by the user on social media. The navigation unit can also suggest navigation options based on places recommended by influencers the user follows. Furthermore, the navigation unit can suggest the optimal navigation option based on reviews and ratings shared by the user on social media. In this way, the navigation unit can provide the optimal navigation option by analyzing the user's social media activity. Some or all of the above processing in the navigation unit may be performed using, for example, generative AI, or without generative AI. For example, the navigation unit can input the user's social media activity data into a generative AI and have the generative AI suggest navigation options.

[0105] The feedback reception unit can estimate the user's emotions and adjust the feedback reception method based on the estimated emotions. For example, if the user is relaxed, the feedback reception unit can provide an interface for detailed feedback. For example, if the feedback reception unit estimates that the user is relaxed, it can provide an interface for detailed feedback. The feedback reception unit can also provide an interface for simple feedback if the user is in a hurry. For example, if the feedback reception unit estimates that the user is in a hurry, it can provide an interface for simple feedback. Furthermore, if the feedback reception unit is stressed, it can provide a simple feedback input method. For example, if the feedback reception unit estimates that the user is stressed, it can provide a simple feedback input method. In this way, by adjusting the feedback reception method according to the user's emotions, a more appropriate feedback reception method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the feedback reception unit may be performed using AI, for example, or without AI. For example, the feedback reception section can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0106] The feedback reception unit can select the optimal feedback reception method by referring to the user's past feedback history when receiving feedback. For example, the feedback reception unit can propose the optimal feedback reception method based on the feedback the user has previously entered. For example, the feedback reception unit can propose the optimal feedback reception method based on the feedback the user has previously entered. The feedback reception unit can also predict and propose trends in feedback entered at specific time periods based on the user's past feedback history. For example, the feedback reception unit can predict and propose trends in feedback entered at specific time periods based on the user's past feedback history. Furthermore, the feedback reception unit can analyze the user's past feedback history and propose the most efficient feedback reception method. For example, the feedback reception unit can analyze the user's past feedback history and propose the most efficient feedback reception method. In this way, the optimal feedback reception method can be provided by referring to the user's past feedback history. Some or all of the above processing in the feedback reception unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback reception unit can input the user's feedback history data into a generative AI and have the generative AI select the feedback reception method.

[0107] The feedback reception unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is in a hurry, the feedback reception unit will prioritize receiving the most important feedback. For example, if the feedback reception unit estimates that the user is in a hurry, it will prioritize receiving the most important feedback. The feedback reception unit can also accept detailed feedback if the user is relaxed. For example, if the feedback reception unit estimates that the user is relaxed, it will accept detailed feedback. Furthermore, if the feedback reception unit is stressed, it can prioritize receiving simple feedback. For example, if the feedback reception unit estimates that the user is stressed, it will prioritize receiving simple feedback. This allows for the reception of more appropriate feedback by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback reception unit may be performed using AI, for example, or without AI. For example, the feedback reception section can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0108] The feedback reception unit can select the optimal feedback reception method by considering the user's geographical location information when receiving feedback. For example, the feedback reception unit can propose the optimal feedback reception method by considering the characteristics of the area where the user is currently located. The feedback reception unit can also propose the optimal feedback reception method based on the characteristics of areas the user has visited in the past. For example, the feedback reception unit can propose the optimal feedback reception method based on the characteristics of areas the user has visited in the past. Furthermore, the feedback reception unit can propose the optimal feedback reception method by considering the trends of the area where the user is currently located. For example, the feedback reception unit can propose the optimal feedback reception method by considering the trends of the area where the user is currently located. In this way, the optimal feedback reception method can be provided by considering the user's geographical location information. Some or all of the above processing in the feedback reception unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback reception unit can input the user's geographical location information into a generative AI and have the generative AI select the feedback reception method.

[0109] The history utilization unit can estimate the user's emotions and select history data based on the estimated emotions. For example, if the user is relaxed, the history utilization unit will select detailed history data. For example, if the history utilization unit estimates that the user is relaxed, it will select detailed history data. The history utilization unit can also prioritize selecting the most important history data if the user is in a hurry. For example, if the history utilization unit estimates that the user is in a hurry, it will prioritize selecting the most important history data. Furthermore, if the user is stressed, the history utilization unit can select simpler history data. For example, if the history utilization unit estimates that the user is stressed, it will select simpler history data. In this way, by selecting history data according to the user's emotions, more appropriate history data can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history utilization unit may be performed using AI, for example, or without AI. For example, the history utilization unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0110] The history utilization unit can optimize the history algorithm by referring to past history data when utilizing history. For example, the history utilization unit can select the optimal history algorithm based on past history data. The history utilization unit can also analyze past history data and optimize the history algorithm to be used in a specific time period. Furthermore, the history utilization unit can optimize the history algorithm for a specific product by referring to past history data. In this way, the history algorithm can be optimized by referring to past history data. Some or all of the above processing in the history utilization unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the history utilization unit can input past history data into a generative AI and have the generative AI perform the optimization of the history algorithm.

[0111] The history utilization unit can estimate the user's emotions and adjust the frequency of history utilization based on the estimated emotions. For example, if the user is relaxed, the history utilization unit will frequently utilize history data. For example, if the history utilization unit estimates that the user is relaxed, it will frequently utilize history data. The history utilization unit can also prioritize the use of the most important history data if the user is in a hurry. For example, if the history utilization unit estimates that the user is in a hurry, it will prioritize the use of the most important history data. Furthermore, if the user is stressed, the history utilization unit can also utilize simple history data. For example, if the history utilization unit estimates that the user is stressed, it will utilize simple history data. This allows for more appropriate history utilization by adjusting the frequency of history utilization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the history utilization unit may be performed using AI, for example, or without AI. For example, the history utilization unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0112] The history utilization unit can weight historical data based on the product submission date when utilizing history data. For example, the history utilization unit weights historical data based on the product submission date. The history utilization unit can also prioritize the use of historical data for specific products, taking into account the product submission date. Furthermore, the history utilization unit can optimize the weighting of historical data based on the product submission date. For example, the history utilization unit optimizes the weighting of historical data based on the product submission date. This allows for the provision of more appropriate historical data by weighting historical data based on the product submission date. Some or all of the above processing in the history utilization unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the history utilization unit can input product submission date data into a generation AI and have the generation AI perform the weighting of historical data.

[0113] The explanation unit can estimate the user's emotions and adjust its explanation method based on the estimated emotions. For example, if the user is nervous, the explanation unit can provide a simple and easy-to-understand explanation. For example, if the explanation unit estimates that the user is nervous, it can provide a simple and easy-to-understand explanation. The explanation unit can also provide an explanation that includes detailed information if the user is relaxed. For example, if the explanation unit estimates that the user is relaxed, it can provide an explanation that includes detailed information. Furthermore, if the explanation unit estimates that the user is in a hurry, it can provide an explanation that gets to the point. For example, if the explanation unit estimates that the user is in a hurry, it can provide an explanation that gets to the point. In this way, by adjusting the explanation method according to the user's emotions, a more appropriate explanation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0114] The explanation unit can select the optimal explanation method by referring to the user's past explanation history during an explanation. For example, the explanation unit can suggest the optimal explanation method based on the explanations the user has received in the past. The explanation unit can also predict and suggest an explanation method to be used at a specific time of day based on the user's past explanation history. Furthermore, the explanation unit can analyze the user's past explanation history and suggest the most efficient explanation method. In this way, the optimal explanation method can be provided by referring to the user's past explanation history. Some or all of the above processing in the explanation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the explanation unit can input the user's explanation history data into a generative AI and have the generative AI select an explanation method.

[0115] The descriptive unit can estimate the user's emotions and determine the priority of explanations based on the estimated emotions. For example, if the user is in a hurry, the descriptive unit will prioritize providing the most important explanations. For example, if the descriptive unit estimates that the user is in a hurry, it will prioritize providing the most important explanations. The descriptive unit can also provide detailed explanations if the user is relaxed. For example, if the descriptive unit estimates that the user is relaxed, it will provide detailed explanations. Furthermore, if the descriptive unit is stressed, it can provide simple explanations. For example, if the descriptive unit estimates that the user is stressed, it will provide simple explanations. This allows for the provision of more appropriate explanations by prioritizing explanations 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. Some or all of the above processing in the descriptive unit may be performed using AI, for example, or not using AI. For example, the descriptive unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0116] The explanation unit can select the optimal explanation method while considering the user's geographical location information. For example, the explanation unit can propose the optimal explanation method by considering the characteristics of the area where the user is currently located. The explanation unit can also propose the optimal explanation method based on the characteristics of areas the user has visited in the past. Furthermore, the explanation unit can also propose the optimal explanation method by considering the trends of the area where the user is currently located. In this way, the optimal explanation method can be provided by considering the user's geographical location information. Some or all of the above processing in the explanation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the explanation unit can input the user's geographical location information into a generative AI and have the generative AI select the explanation method.

[0117] The descriptive unit can estimate the user's emotions and determine the priority of explanations based on the estimated emotions. For example, if the user is in a hurry, the descriptive unit will prioritize providing the most important explanations. For example, if the descriptive unit estimates that the user is in a hurry, it will prioritize providing the most important explanations. The descriptive unit can also provide detailed explanations if the user is relaxed. For example, if the descriptive unit estimates that the user is relaxed, it will provide detailed explanations. Furthermore, if the descriptive unit is stressed, it can provide simple explanations. For example, if the descriptive unit estimates that the user is stressed, it will provide simple explanations. This allows for the provision of more appropriate explanations by prioritizing explanations 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. Some or all of the above processing in the descriptive unit may be performed using AI, for example, or not using AI. For example, the descriptive unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The reception desk not only receives user input but can also provide appropriate feedback based on that input. For example, if a user is looking for a specific product, the reception desk can immediately provide information on the product's availability and similar products. The reception desk can also suggest relevant promotions and discounts based on the information the user has entered. Furthermore, the reception desk can analyze user input and proactively provide the information the user is looking for. This allows users to quickly obtain the information they need, improving their shopping experience.

[0120] The feedback department not only gathers real-time user feedback but can also use that feedback to suggest product improvements. For example, if a user comments that they "don't like the color" of a particular product, the feedback department can provide that information to the manufacturer and suggest increasing the product's color variations. The feedback department can also compile user feedback and analyze trends in popular and unpopular products. Furthermore, based on user feedback, the feedback department can provide product reviews and ratings to other users. This allows users to make better purchases by referring to the opinions of other users.

[0121] The history utilization unit not only uses users' past purchase history and ratings, but can also analyze user behavior patterns to predict future purchasing behavior. For example, if a user tends to purchase certain products during a particular season, the history utilization unit can suggest related products as that season approaches. Furthermore, based on a user's past purchase history, the history utilization unit can suggest new products that the user might be interested in. In addition, the history utilization unit can analyze user rating data, extract the characteristics of products that users have given high ratings to, and suggest similar products. This makes it easier for users to find products that suit their preferences, improving their shopping experience.

[0122] The product description section can not only explain the differences in product functions and materials, but also offer customized suggestions tailored to user needs. For example, if a user is looking for a product suitable for a specific purpose, the description section can suggest the most suitable product for that purpose. Furthermore, if a user prioritizes a particular function, the description section can compare products with that function and suggest the best one. Additionally, if a user has a preference for a specific brand or design, the description section can suggest products related to that brand or design. This makes it easier for users to find products that meet their needs, improving their shopping experience.

[0123] The analytics department not only monitors inventory levels in conjunction with the store's inventory management system, but can also forecast demand based on inventory data. For example, it can analyze past sales data to predict when demand for a particular product will increase. It can also suggest replenishment of specific products before they run out, based on inventory data. Furthermore, the analytics department can monitor inventory data in real time and respond quickly to inventory fluctuations. This allows stores to streamline inventory management and provide users with always up-to-date inventory information.

[0124] The generation unit can not only generate the optimal route based on user preferences, but also optimize the route based on the user's browsing history. For example, it can analyze the user's browsing history of stores and products they have visited in the past and prioritize incorporating products that the user is likely to be interested in into the route. It can also analyze the user's behavior patterns and suggest a route that allows them to efficiently navigate through stores. Furthermore, it can prioritize suggesting products from specific brands or categories based on the user's preferences. This allows users to efficiently find products that match their interests and preferences, improving their shopping experience.

[0125] The reception system can estimate the user's emotions and adjust the input process based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. For instance, if the reception system estimates the user is stressed, it can offer simple input options. Alternatively, if the user is relaxed, it can offer more detailed input options and suggest customizable input methods. For example, if the reception system estimates the user is relaxed, it can offer detailed input options. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick input of product names and features. For example, if the reception system estimates the user is in a hurry, it can prioritize voice input. This allows for a more appropriate input method by adjusting the input process according to the user's emotions.

[0126] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis and prioritize providing the most important information. For example, if the analysis unit estimates that the user is in a hurry, it can perform a rapid analysis and prioritize providing important information. The analysis unit can also perform a detailed analysis and provide customized information if the user is relaxed. For example, if the analysis unit estimates that the user is relaxed, it can perform a detailed analysis and provide customized information. Furthermore, if the user is stressed, the analysis unit can provide a simple analysis result to avoid information overload. For example, if the analysis unit estimates that the user is stressed, it can provide a simple analysis result. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0127] The generation unit can estimate the user's emotions and adjust the route generation criteria based on those emotions. For example, if the user is in a hurry, the generation unit will prioritize generating the shortest route. For example, if the generation unit estimates that the user is in a hurry, it will prioritize generating the shortest route. The generation unit can also generate a route that allows the user to stroll through the store at a leisurely pace if the user is relaxed. For example, if the generation unit estimates that the user is relaxed, it will generate a route that allows the user to stroll through the store at a leisurely pace. Furthermore, if the generation unit is stressed, it can generate a route that avoids crowds. For example, if the generation unit estimates that the user is stressed, it will generate a route that avoids crowds. In this way, by adjusting the route generation criteria according to the user's emotions, a more appropriate route can be provided.

[0128] The navigation unit can estimate the user's emotions and adjust the navigation method based on those emotions. For example, if the user is tense, the navigation unit can provide a simple and easy-to-understand navigation method. For example, if the navigation unit estimates that the user is tense, it can provide a simple and easy-to-understand navigation method. The navigation unit can also provide a navigation method that includes detailed information if the user is relaxed. For example, if the navigation unit estimates that the user is relaxed, it can provide a navigation method that includes detailed information. Furthermore, if the navigation unit estimates that the user is in a hurry, it can provide a navigation method that gets to the point. For example, if the navigation unit estimates that the user is in a hurry, it can provide a navigation method that gets to the point. In this way, by adjusting the navigation method according to the user's emotions, a more appropriate navigation method can be provided.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, it accepts information entered by the user using a smartphone or tablet. It can also accept voice input using speech recognition technology, or analyze captured images using image recognition technology and accept them as input information. Step 2: The analysis unit analyzes the store's inventory status based on the information received by the reception unit. The analysis unit works in conjunction with the store's inventory management system to grasp the inventory status in real time and check whether the product entered by the user is in stock. It can also take into account the frequency of inventory data updates to grasp the latest inventory status. Step 3: The generation unit generates a route based on the information analyzed by the analysis unit. The generation unit uses generation AI to generate the optimal route so that users can efficiently find products. It understands the placement and inventory status of products within the store and proposes a route so that users can reach their desired products. It can also generate personalized routes based on the user's preferences. Step 4: The navigation unit navigates based on the route generated by the generation unit. The navigation unit guides the user to the desired product using voice and visual guidance. It understands the user's current location and provides real-time directions to the desired product. It can also incorporate real-time user feedback and reflect it in the route.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, navigation unit, feedback reception unit, history utilization unit, and explanation unit, is implemented, for example, 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 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the store's inventory status. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal route. The navigation unit is implemented by the control unit 46A of the smart device 14 and navigates the user. The feedback reception unit is implemented by the control unit 46A of the smart device 14 and incorporates the user's real-time feedback. The history utilization unit is implemented by the specific processing unit 290 of the data processing unit 12 and utilizes the user's past purchase history and evaluations. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12 and explains the differences in product functions and materials. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, navigation unit, feedback reception unit, history utilization unit, and explanation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the store's inventory status. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal route. The navigation unit is implemented by the control unit 46A of the smart glasses 214 and navigates the user. The feedback reception unit is implemented by the control unit 46A of the smart glasses 214 and incorporates the user's real-time feedback. The history utilization unit is implemented by the specific processing unit 290 of the data processing unit 12 and utilizes the user's past purchase history and evaluations. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12 and explains the differences in product functions and materials. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, navigation unit, feedback reception unit, history utilization unit, and explanation 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 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the store's inventory status. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal route. The navigation unit is implemented by the control unit 46A of the headset terminal 314 and navigates the user. The feedback reception unit is implemented by the control unit 46A of the headset terminal 314 and incorporates the user's real-time feedback. The history utilization unit is implemented by the specific processing unit 290 of the data processing unit 12 and utilizes the user's past purchase history and evaluations. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12 and explains the differences in product functions and materials. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, navigation unit, feedback reception unit, history utilization unit, and explanation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the store's inventory status. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal route. The navigation unit is implemented by the control unit 46A of the robot 414 and navigates the user. The feedback reception unit is implemented by the control unit 46A of the robot 414 and incorporates the user's real-time feedback. The history utilization unit is implemented by the specific processing unit 290 of the data processing unit 12 and utilizes the user's past purchase history and evaluations. The explanation unit is implemented by the specific processing unit 290 of the data processing unit 12 and explains the differences in product functions and materials. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) A reception area that receives user input, An analysis unit analyzes the store's inventory status based on the information received by the reception unit, A generation unit that generates a route based on the information analyzed by the analysis unit, The system includes a navigation unit that navigates based on the route generated by the generation unit. A system characterized by the following features. (Note 2) It includes a feedback section to incorporate real-time user comments. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a history utilization section that makes use of the user's past purchase history and ratings. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a section that explains the differences in product functions and materials. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Integrate with the store's inventory management system to monitor inventory status. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generates the optimal route based on user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the input processing method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When input is received, input suggestions are presented based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system will display highly relevant input suggestions, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and suggests relevant input options. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, historical inventory data is referenced to predict the current inventory situation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied to each product category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the geographical distribution of the products will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature on the product to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the route generation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating routes, we improve the accuracy of the generation by considering the interrelationships between products. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating routes, we improve the accuracy of the generation by considering the interrelationships between products. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a route, the optimal route is generated by taking into account the placement information of the products. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts how the generated routes are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is Route generation takes into account the geographical distribution of products. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During route generation, we improve the accuracy of the generation by referring to relevant literature for the product. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned navigation unit is It estimates the user's emotions and adjusts the navigation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned navigation unit is During navigation, the system selects the optimal navigation method by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned navigation unit is During navigation, the navigation method is customized based on the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned navigation unit is It estimates the user's emotions and determines navigation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned navigation unit is During navigation, the system selects the optimal navigation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned navigation unit is During navigation, the system analyzes the user's social media activity and suggests navigation methods. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback submission department is, We estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback submission department is, When receiving feedback, the system will refer to the user's past feedback history to select the most suitable method for receiving it. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback submission department is, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback submission department is, When receiving feedback, the system will select the most suitable feedback collection method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned history utilization unit is: The system estimates the user's emotions and selects historical data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned history utilization unit is: When utilizing historical data, the historical algorithm is optimized by referring to past historical data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned history utilization unit is: It estimates the user's emotions and adjusts the frequency of using their history based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned history utilization unit is: When using the history, weight the history data based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 40) The above explanatory section is, It estimates the user's emotions and adjusts the explanation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The above explanatory section is, During the explanation, the system selects the most suitable explanation method by referring to the user's past explanation history. The system described in Appendix 1, characterized by the features described herein. (Note 42) The above explanatory section is, It estimates the user's emotions and determines the priority of explanations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The above explanatory section is, During the explanation, the optimal explanation method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, An analysis unit analyzes the store's inventory status based on the information received by the reception unit, A generation unit that generates a route based on the information analyzed by the analysis unit, The system includes a navigation unit that navigates based on the route generated by the generation unit. A system characterized by the following features.

2. It includes a feedback section to incorporate real-time user comments. The system according to feature 1.

3. It includes a history utilization section that makes use of the user's past purchase history and ratings. The system according to feature 1.

4. It includes a section that explains the differences in product functions and materials. The system according to feature 1.

5. The aforementioned analysis unit, Integrate with the store's inventory management system to monitor inventory status. The system according to feature 1.

6. The generating unit is Generates the optimal route based on user preferences. The system according to feature 1.

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

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When input is received, input suggestions are presented based on the user's current areas of interest. The system according to feature 1.

Images