system

Abstract

The system according to this embodiment aims to analyze the user's purchase history and preferences, recommend the most suitable products, and support efficient product searching and inventory management. [Solution] The system according to the embodiment comprises an analysis unit, a recommendation unit, a data acquisition unit, a guidance unit, and an inventory management unit. The analysis unit analyzes the user's purchase history and preferences. The recommendation unit recommends the most suitable products based on the results analyzed by the analysis unit. The data acquisition unit grasps the store layout and product placement. The guidance unit provides real-time guidance on the location of necessary products based on the information grasped by the data acquisition unit. The inventory management unit learns the frequency of use of products consumed in daily life and issues notifications before inventory runs out.

Description

Technical Field

[0006] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the selection of products to be purchased, the search for products in a store, and the inventory management of daily necessities are not efficiently performed.

[0005] The system according to the embodiment aims to analyze the purchase history and preferences of a user, recommend optimal products, and support efficient product search and inventory management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a recommendation unit, a data acquisition unit, a guidance unit, and an inventory management unit. The analysis unit analyzes the user's purchase history and preferences. The recommendation unit recommends the most suitable products based on the results analyzed by the analysis unit. The data acquisition unit grasps the store layout and product placement. The guidance unit provides real-time guidance on the location of necessary products based on the information grasped by the data acquisition unit. The inventory management unit learns the frequency of use of products consumed in daily life and issues notifications before inventory runs out. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's purchase history and preferences, recommend the most suitable products, and support efficient product searching and inventory management. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F 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 support system according to an embodiment of the present invention is a system for efficiently shopping using AI. This shopping support system analyzes the user's purchase history and preferences and recommends the most suitable products. Next, the shopping support system understands the store layout and product placement and guides the user to the location of necessary products in real time. Furthermore, the shopping support system learns the frequency of use of products consumed in daily life and issues notifications before the stock runs out. This helps the user to remember to purchase necessary products. For example, the shopping support system analyzes the user's purchase history and preferences. For example, it learns data on products purchased in the past and the user's preferences and recommends products that are likely to be purchased next. This reduces the user's indecision in choosing products to buy. Next, the shopping support system understands the store layout and product placement and guides the user to the location of necessary products in real time. For example, if a user asks "Where is the milk?" using their smartphone, the shopping support system will show the location of the milk in the store and guide the user to the shortest route. This allows the user to find products efficiently. Furthermore, the shopping support system learns the frequency of use of products consumed in daily life and issues notifications before the stock runs out. For example, the system learns the frequency of use of consumables such as toilet paper and detergent, and notifies the user when the stock is running low. The shopping support system also automatically creates shopping lists, helping users remember to purchase necessary items. This saves users the trouble of making multiple shopping trips. This system makes shopping more efficient and convenient for users. For a wide range of users, including housewives, business people, and students, it reduces the time spent searching for items in department stores and supermarkets. Furthermore, it simplifies inventory management of daily necessities, preventing users from making extra shopping trips due to unnoticed shortages. In short, the shopping support system can significantly improve the user's shopping experience.

[0029] The shopping support system according to this embodiment comprises an analysis unit, a recommendation unit, a tracking unit, a guidance unit, and an inventory management unit. The analysis unit analyzes the user's purchase history and preferences. For example, the analysis unit learns data on products purchased in the past and the user's preferences, and analyzes products that the user is likely to purchase next. The analysis unit can also analyze the purchase frequency of specific products based on the user's purchase history. Furthermore, the analysis unit can recommend related products based on the user's preferences. For example, the analysis unit analyzes products that the user is likely to purchase next based on data on products purchased in the past. The recommendation unit recommends the most suitable products based on the results analyzed by the analysis unit. For example, the recommendation unit recommends products that the user is likely to purchase next based on the user's preferences. Furthermore, the recommendation unit can also recommend related products based on the user's purchase history. Furthermore, the recommendation unit can also recommend specific products based on the user's preferences. For example, the recommendation unit recommends products that the user is likely to purchase next based on the user's preferences. The tracking unit grasps the store layout and product placement. The information gathering unit, for example, gathers information on the placement of products within a store and provides guidance to the user. The information gathering unit can also gather information on product placement based on the store layout. Furthermore, the information gathering unit can provide guidance to the user based on the product placement within the store. For example, the information gathering unit gathers information on the placement of products within a store and provides guidance to the user. The guidance unit provides real-time guidance on the location of necessary products based on the information gathered by the information gathering unit. For example, when a user asks for the location of a product using their smartphone, the guidance unit shows the location of the product within the store and guides the user along the shortest route. Furthermore, the guidance unit can also guide the user along the optimal route based on the user's location information. Furthermore, the guidance unit can provide guidance to the user based on the location of products within the store. For example, when a user asks for the location of a product using their smartphone, the guidance unit shows the location of the product within the store and guides the user along the shortest route. The inventory management unit learns the frequency of use of products consumed in daily life and issues notifications before inventory runs out. For example, the inventory management unit learns the frequency of use of consumables such as toilet paper and detergent and notifies the user when inventory is low.Furthermore, the inventory management unit can learn the frequency of use of consumables based on the user's purchase history. In addition, the inventory management unit can notify the user when the stock of consumables is running low, based on the frequency of use. For example, the inventory management unit can learn the frequency of use of consumables such as toilet paper and detergent, and notify the user when the stock is running low. This allows the shopping support system according to this embodiment to significantly improve the user's shopping experience.

[0030] The analytics department analyzes users' purchase history and preferences. Specifically, it collects data on products users have purchased in the past and uses this data to learn their preferences and purchasing patterns. For example, it analyzes the products users frequently purchase and their preferences for specific brands to predict the products they are most likely to purchase next. The analytics department uses machine learning algorithms to extract patterns from users' purchase history and predict future purchasing behavior. Furthermore, the analytics department can also recommend relevant products based on user preferences. For example, when analyzing products users are most likely to purchase next based on data from products they have purchased in the past, it can recommend products in the same category or related accessories. The analytics department also analyzes users' purchase frequency to understand how often specific products are purchased. This allows it to predict when users will next purchase and recommend products at the appropriate time. Based on user preferences and purchase history, the analytics department builds a foundation for providing a personalized shopping experience.

[0031] The recommendation department recommends the most suitable products based on the results analyzed by the analytics department. Specifically, the recommendation department considers the user's preferences and purchase history to list products that are likely to be purchased next. For example, it can recommend products in the same category as products the user has previously purchased, or related accessories. Based on the data provided by the analytics department, the recommendation department selects the most attractive products for the user and presents them to the user. The recommendation department can also recommend related products based on the user's purchase history. For example, when recommending products that the user is likely to purchase next based on data of products they have previously purchased, it can recommend products in the same category or related accessories. Furthermore, the recommendation department can recommend specific products based on the user's preferences. For example, if a user has a strong preference for a particular brand or category, it will prioritize recommending products from that brand or category. Based on the user's preferences and purchase history, the recommendation department provides personalized product recommendations to improve the user's shopping experience.

[0032] The information gathering unit understands the store layout and product placement. Specifically, the information gathering unit understands the placement of products within the store and provides information to guide users. For example, the information gathering unit understands the placement of products within the store and provides guidance to users. The information gathering unit can also understand product placement based on the store layout. Furthermore, the information gathering unit can also provide guidance to users based on the placement of products within the store. For example, the information gathering unit understands the placement of products within the store and provides guidance to users. The information gathering unit understands the placement of products within the store and provides information to guide users. For example, the information gathering unit understands the placement of products within the store and provides guidance to users. The information gathering unit can also understand product placement based on the store layout. Furthermore, the information gathering unit can also provide guidance to users based on the placement of products within the store. For example, the information gathering unit understands the placement of products within the store and provides guidance to users.

[0033] The guidance unit provides real-time guidance on the location of necessary products based on the information gathered by the information gathering unit. Specifically, when a user asks for the location of a product using their smartphone, the guidance unit shows the location of the product within the store and guides the user along the shortest route. The guidance unit can also guide the user along the optimal route based on the user's location information. Furthermore, the guidance unit can also provide guidance to the user based on the location of products within the store. For example, when a user asks for the location of a product using their smartphone, the guidance unit shows the location of the product within the store and guides the user along the shortest route. The guidance unit shows the location of the product within the store and guides the user along the shortest route. Furthermore, the guidance unit can also guide the user along the optimal route based on the user's location information. Furthermore, the guidance unit can also provide guidance to the user based on the location of products within the store. For example, when a user asks for the location of a product using their smartphone, the guidance unit shows the location of the product within the store and guides the user along the shortest route.

[0034] The inventory management department learns the frequency of use of consumables in daily life and notifies users before inventory runs out. Specifically, the inventory management department learns the frequency of use of consumables such as toilet paper and detergent and notifies users when inventory is running low. The inventory management department can also learn the frequency of use of consumables based on users' purchase history. Furthermore, the inventory management department can also notify users when inventory is running low based on the frequency of use of consumables. For example, the inventory management department learns the frequency of use of consumables such as toilet paper and detergent and notifies users when inventory is running low. The inventory management department learns the frequency of use of consumables based on users' purchase history and notifies users when inventory is running low. Furthermore, the inventory management department can also notify users when inventory is running low based on the frequency of use of consumables. For example, the inventory management department learns the frequency of use of consumables such as toilet paper and detergent and notifies users when inventory is running low.

[0035] The analysis unit can optimize its analysis algorithm by considering the user's past purchasing patterns when analyzing purchase history. For example, the analysis unit can analyze products that the user is likely to purchase next based on the frequency of products they have purchased in the past. The analysis unit can also analyze products that the user purchases in a particular season based on their past purchasing patterns. Furthermore, the analysis unit can analyze products related to a specific event by analyzing the user's past purchasing patterns. For example, the analysis unit can analyze products that the user is likely to purchase next based on the frequency of products they have purchased in the past. This allows for more accurate product recommendations by considering the user's past purchasing patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0036] The analysis unit can reflect the user's lifestyle and seasonal preferences when analyzing purchase history. For example, the analysis unit can analyze health-oriented products based on the user's lifestyle. It can also analyze cold drinks in summer and hot drinks in winter, taking seasonal preferences into account. Furthermore, the analysis unit can analyze products purchased at specific times of day based on the user's lifestyle. For example, the analysis unit can analyze health-oriented products based on the user's lifestyle. By reflecting the user's lifestyle and seasonal preferences, more appropriate product recommendations become possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0037] The analysis unit can perform analysis of purchase history while taking into account the user's geographical location. For example, the analysis unit can analyze products available for purchase at nearby stores based on the user's current location. The analysis unit can also analyze region-specific products while taking into account the user's geographical location. Furthermore, the analysis unit can analyze products popular in a specific region based on the user's geographical location. For example, the analysis unit can analyze products available for purchase at nearby stores based on the user's current location. This makes it possible to recommend products that are appropriate for the region by taking into account the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0038] The analysis unit can analyze users' social media activity and incorporate relevant data into the analysis when analyzing purchase history. For example, the analysis unit can analyze products of interest based on users' "likes" and comments on social media. The analysis unit can also analyze the purchasing trends of users' social media followers and analyze related products. Furthermore, the analysis unit can analyze products of interest based on the content of users' social media posts. For example, the analysis unit can analyze products of interest based on users' "likes" and comments on social media. This makes it possible to recommend products that are more relevant to users by considering their social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0039] The recommendation system can adjust the level of detail in recommendations based on the importance of the products. For example, it will recommend highly important products with detailed descriptions, while recommending less important products with concise descriptions. Furthermore, the recommendation system can adjust the display order of recommended products according to their importance. For example, it will recommend highly important products with detailed descriptions. By adjusting the level of detail in recommendations according to the importance of the products, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI.

[0040] The recommendation system can apply different recommendation algorithms depending on the product category. For example, for food products, the recommendation system applies a recommendation algorithm that takes into account expiration dates and nutritional value. For clothing products, the recommendation system can also apply a recommendation algorithm that takes into account seasons and trends. Furthermore, for home appliance products, the recommendation system can also apply a recommendation algorithm that takes into account functions and price. For example, for food products, the recommendation system applies a recommendation algorithm that takes into account expiration dates and nutritional value. By applying a recommendation algorithm appropriate to the product category, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without using AI.

[0041] The recommendation department can determine the priority of recommendations based on the timing of product submission. For example, the recommendation department may prioritize new products. It may also prioritize products during sales periods. Furthermore, it may prioritize seasonal products. For example, the recommendation department may prioritize new products. By determining the priority of recommendations based on the timing of product submission, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation department may be performed using AI, for example, or not using AI.

[0042] The recommendation system can adjust the order of recommendations based on product relevance. For example, it may prioritize recommending highly relevant products based on the user's past purchase history. It can also prioritize recommending highly relevant products based on the user's preferences. Furthermore, it can prioritize recommending highly relevant products based on the user's current purchasing patterns. For example, it may prioritize recommending highly relevant products based on the user's past purchase history. By adjusting the order of recommendations based on product relevance, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI.

[0043] The understanding unit can optimize its understanding algorithm by referring to past store data when understanding store layouts. For example, the understanding unit can analyze product placement patterns based on past store data and apply the optimal understanding algorithm. The understanding unit can also analyze product placement patterns in specific seasons from past store data. Furthermore, the understanding unit can analyze product placement patterns in specific events by analyzing past store data. For example, the understanding unit can analyze product placement patterns based on past store data and apply the optimal understanding algorithm. This makes it possible to understand store layouts with higher accuracy by referring to past store data. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without using AI.

[0044] The layout recognition unit can reflect store renovations and seasonal layout changes when recognizing store layouts. For example, the unit can recognize the latest layout based on store renovation information. The unit can also recognize product placement by reflecting seasonal layout changes. Furthermore, the unit can optimize its recognition algorithm in response to store renovations and layout changes. For example, the unit can recognize the latest layout based on store renovation information. This makes it possible to recognize the latest store layout by reflecting store renovations and seasonal layout changes. Some or all of the above processing in the layout recognition unit may be performed using AI, for example, or without using AI.

[0045] The understanding unit can understand store layouts while taking into account the user's geographical location information. For example, the understanding unit can understand the layout of nearby stores based on the user's current location. The understanding unit can also understand region-specific store layouts while taking into account the user's geographical location information. Furthermore, the understanding unit can understand the placement of popular products in a specific region based on the user's geographical location information. For example, the understanding unit can understand the layout of nearby stores based on the user's current location. This makes it possible to understand store layouts that are appropriate for the region by taking into account the user's geographical location information. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without using AI.

[0046] The understanding unit can analyze the store's social media activity and incorporate relevant data into its understanding of the store layout. For example, the understanding unit can understand the placement of products based on the content of the store's social media posts. It can also analyze the purchasing trends of the store's social media followers and understand the placement of related products. Furthermore, the understanding unit can understand the placement of products based on the "likes" and comments on the store's social media posts. For example, the understanding unit can understand the placement of products based on the content of the store's social media posts. This makes it possible to understand a more appropriate store layout by considering the store's social media activity. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without using AI.

[0047] The guidance unit can adjust the level of detail in the guidance based on the importance of the product. For example, the guidance unit provides detailed explanations for highly important products. Conversely, it can provide concise explanations for less important products. Furthermore, the guidance unit can adjust the display order of the guidance according to its importance. For example, it provides detailed explanations for highly important products. By adjusting the level of detail in the guidance according to the importance of the product, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI.

[0048] The guidance unit can apply different guidance algorithms depending on the product category during guidance. For example, for food products, the guidance unit applies a guidance algorithm that takes into account expiration dates and nutritional value. The guidance unit can also apply a guidance algorithm that takes into account seasons and trends for clothing products. Furthermore, the guidance unit can apply a guidance algorithm that takes into account functions and price for home appliance products. For example, the guidance unit applies a guidance algorithm that takes into account expiration dates and nutritional value for food products. By applying a guidance algorithm appropriate to the product category, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI.

[0049] The guidance unit can determine the priority of guidance based on the product submission timing. For example, the guidance unit may prioritize guidance for new products. It may also prioritize guidance for products during sales periods. Furthermore, it may prioritize guidance for seasonal products. For example, the guidance unit may prioritize guidance for new products. This allows for more appropriate guidance by determining the priority of guidance based on the product submission timing. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI.

[0050] The guidance unit can adjust the order of guidance based on the relevance of the products during the guidance process. For example, the guidance unit prioritizes guiding users to highly relevant products based on the user's past purchase history. The guidance unit can also prioritize guiding users to highly relevant products based on the user's preferences. Furthermore, the guidance unit can prioritize guiding users to highly relevant products based on the user's current purchasing patterns. For example, the guidance unit prioritizes guiding users to highly relevant products based on the user's past purchase history. By adjusting the order of guidance based on the relevance of the products, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI.

[0051] The inventory management department can optimize its inventory management algorithm by referring to historical inventory data during inventory management. For example, the inventory management department can analyze product consumption patterns based on historical inventory data and apply the optimal inventory management algorithm. Furthermore, the inventory management department can analyze product consumption patterns in specific seasons from historical inventory data. In addition, the inventory management department can analyze product consumption patterns during specific events by analyzing historical inventory data. For example, the inventory management department can analyze product consumption patterns based on historical inventory data and apply the optimal inventory management algorithm. This allows for more accurate inventory management by referring to historical inventory data. Some or all of the above processes in the inventory management department may be performed using AI, for example, or without AI.

[0052] The inventory management department can learn product consumption patterns during inventory management and issue notifications at the optimal time before inventory runs out. For example, the inventory management department can learn product consumption patterns and issue notifications when inventory is low. It can also learn product consumption patterns and issue notifications at specific time periods. Furthermore, the inventory management department can learn product consumption patterns and issue notifications in conjunction with specific events. For example, the inventory management department can learn product consumption patterns and issue notifications when inventory is low. In this way, by learning product consumption patterns, it becomes possible to issue notifications at the appropriate time before inventory runs out. Some or all of the above processes in the inventory management department may be performed using AI, for example, or not using AI.

[0053] The inventory management department can perform inventory management while taking into account the user's geographical location information. For example, the inventory management department can manage the inventory of products that can be purchased at nearby stores based on the user's current location. The inventory management department can also manage the inventory of region-specific products while taking into account the user's geographical location information. Furthermore, the inventory management department can manage the inventory of products that are popular in a specific region based on the user's geographical location information. For example, the inventory management department can manage the inventory of products that can be purchased at nearby stores based on the user's current location. This makes it possible to perform region-appropriate inventory management by taking into account the user's geographical location information. Some or all of the above processes in the inventory management department may be performed using AI, for example, or without using AI.

[0054] The inventory management department can analyze users' social media activity and incorporate relevant data into inventory management. For example, the inventory management department can manage inventory of products of interest based on users' "likes" and comments on social media. Furthermore, the inventory management department can analyze the purchasing trends of users' social media followers and manage inventory of related products. In addition, the inventory management department can manage inventory of products of interest based on the content of users' social media posts. For example, the inventory management department can manage inventory of products of interest based on users' "likes" and comments on social media. This allows for inventory management of products of greater interest by considering users' social media activity. Some or all of the above processes in the inventory management department may be performed using AI, for example, or without AI.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The shopping support system can also acquire user health data and analyze it in its analysis unit. For example, the analysis unit can recommend health-conscious products based on the user's health data, such as steps taken and heart rate. It can also recommend foods containing specific nutrients based on the user's health condition. Furthermore, the analysis unit can recommend products suitable for use after exercise based on the user's health data. For example, it can recommend a protein drink suitable for use after exercise based on the user's step count data. This enables product recommendations tailored to the user's health condition.

[0057] The shopping support system can further analyze users' social media activity and reflect the results in the analysis unit. For example, the analysis unit can recommend products of interest based on products that users have "liked" or commented on on social media. The analysis unit can also analyze the purchasing trends of users' followers and recommend related products. Furthermore, the analysis unit can recommend products of interest based on the content of users' posts. For example, the analysis unit can recommend related products based on products that users have "liked" on social media. This makes it possible to recommend products that take into account users' social media activity.

[0058] The shopping support system can further optimize its analysis algorithms based on the user's purchase history. For example, the analysis unit can analyze products the user is likely to purchase next based on the frequency of past purchases. It can also analyze products purchased during specific seasons based on the user's past purchasing patterns. Furthermore, the analysis unit can analyze products related to specific events based on the user's past purchasing patterns. For instance, the analysis unit can analyze products the user is likely to purchase next based on the frequency of past purchases. This enables product recommendations that take the user's past purchasing patterns into consideration.

[0059] The shopping support system can also acquire the user's geographical location information and reflect it in the analysis results of the analysis unit. For example, the analysis unit can analyze products available at nearby stores based on the user's current location. Furthermore, the analysis unit can analyze region-specific products, taking the user's geographical location information into consideration. Additionally, the analysis unit can analyze popular products in specific regions based on the user's geographical location information. For example, the analysis unit can analyze products available at nearby stores based on the user's current location. This enables product recommendations that take the user's geographical location information into account.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The analysis unit analyzes the user's purchase history and preferences. For example, it learns data on products purchased in the past and the user's preferences to analyze products that are likely to be purchased next. It can also analyze the purchase frequency of specific products. Step 2: The recommendation unit recommends the most suitable products based on the results analyzed by the analysis unit. For example, it recommends products that the user is likely to purchase next based on their preferences. It can also recommend related products. Step 3: The understanding unit grasps the store layout and product placement. For example, it understands the placement of products within the store and provides guidance to users. It can also understand product placement based on the store layout. Step 4: The guidance unit provides real-time guidance on the location of necessary products based on the information gathered by the information gathering unit. For example, if a user asks for the location of a product using their smartphone, the guidance unit will show the location of the product within the store and guide the user along the shortest route. Step 5: The inventory management department learns the frequency of use of consumable items in daily life and sends notifications before inventory runs out. For example, it learns the frequency of use of consumables such as toilet paper and detergent and notifies users when inventory is running low.

[0062] (Example of form 2) The shopping support system according to an embodiment of the present invention is a system for efficiently shopping using AI. This shopping support system analyzes the user's purchase history and preferences and recommends the most suitable products. Next, the shopping support system understands the store layout and product placement and guides the user to the location of necessary products in real time. Furthermore, the shopping support system learns the frequency of use of products consumed in daily life and issues notifications before the stock runs out. This helps the user to remember to purchase necessary products. For example, the shopping support system analyzes the user's purchase history and preferences. For example, it learns data on products purchased in the past and the user's preferences and recommends products that are likely to be purchased next. This reduces the user's indecision in choosing products to buy. Next, the shopping support system understands the store layout and product placement and guides the user to the location of necessary products in real time. For example, if a user asks "Where is the milk?" using their smartphone, the shopping support system will show the location of the milk in the store and guide the user to the shortest route. This allows the user to find products efficiently. Furthermore, the shopping support system learns the frequency of use of products consumed in daily life and issues notifications before the stock runs out. For example, the system learns the frequency of use of consumables such as toilet paper and detergent, and notifies the user when the stock is running low. The shopping support system also automatically creates shopping lists, helping users remember to purchase necessary items. This saves users the trouble of making multiple shopping trips. This system makes shopping more efficient and convenient for users. For a wide range of users, including housewives, business people, and students, it reduces the time spent searching for items in department stores and supermarkets. Furthermore, it simplifies inventory management of daily necessities, preventing users from making extra shopping trips due to unnoticed shortages. In short, the shopping support system can significantly improve the user's shopping experience.

[0063] The shopping support system according to this embodiment comprises an analysis unit, a recommendation unit, a tracking unit, a guidance unit, and an inventory management unit. The analysis unit analyzes the user's purchase history and preferences. For example, the analysis unit learns data on products purchased in the past and the user's preferences, and analyzes products that the user is likely to purchase next. The analysis unit can also analyze the purchase frequency of specific products based on the user's purchase history. Furthermore, the analysis unit can recommend related products based on the user's preferences. For example, the analysis unit analyzes products that the user is likely to purchase next based on data on products purchased in the past. The recommendation unit recommends the most suitable products based on the results analyzed by the analysis unit. For example, the recommendation unit recommends products that the user is likely to purchase next based on the user's preferences. Furthermore, the recommendation unit can also recommend related products based on the user's purchase history. Furthermore, the recommendation unit can also recommend specific products based on the user's preferences. For example, the recommendation unit recommends products that the user is likely to purchase next based on the user's preferences. The tracking unit grasps the store layout and product placement. The information gathering unit, for example, gathers information on the placement of products within a store and provides guidance to the user. The information gathering unit can also gather information on product placement based on the store layout. Furthermore, the information gathering unit can provide guidance to the user based on the product placement within the store. For example, the information gathering unit gathers information on the placement of products within a store and provides guidance to the user. The guidance unit provides real-time guidance on the location of necessary products based on the information gathered by the information gathering unit. For example, when a user asks for the location of a product using their smartphone, the guidance unit shows the location of the product within the store and guides the user along the shortest route. Furthermore, the guidance unit can also guide the user along the optimal route based on the user's location information. Furthermore, the guidance unit can provide guidance to the user based on the location of products within the store. For example, when a user asks for the location of a product using their smartphone, the guidance unit shows the location of the product within the store and guides the user along the shortest route. The inventory management unit learns the frequency of use of products consumed in daily life and issues notifications before inventory runs out. For example, the inventory management unit learns the frequency of use of consumables such as toilet paper and detergent and notifies the user when inventory is low.Furthermore, the inventory management unit can learn the frequency of use of consumables based on the user's purchase history. In addition, the inventory management unit can notify the user when the stock of consumables is running low, based on the frequency of use. For example, the inventory management unit can learn the frequency of use of consumables such as toilet paper and detergent, and notify the user when the stock is running low. This allows the shopping support system according to this embodiment to significantly improve the user's shopping experience.

[0064] The analytics department analyzes users' purchase history and preferences. Specifically, it collects data on products users have purchased in the past and uses this data to learn their preferences and purchasing patterns. For example, it analyzes the products users frequently purchase and their preferences for specific brands to predict the products they are most likely to purchase next. The analytics department uses machine learning algorithms to extract patterns from users' purchase history and predict future purchasing behavior. Furthermore, the analytics department can also recommend relevant products based on user preferences. For example, when analyzing products users are most likely to purchase next based on data from products they have purchased in the past, it can recommend products in the same category or related accessories. The analytics department also analyzes users' purchase frequency to understand how often specific products are purchased. This allows it to predict when users will next purchase and recommend products at the appropriate time. Based on user preferences and purchase history, the analytics department builds a foundation for providing a personalized shopping experience.

[0065] The recommendation department recommends the most suitable products based on the results analyzed by the analytics department. Specifically, the recommendation department considers the user's preferences and purchase history to list products that are likely to be purchased next. For example, it can recommend products in the same category as products the user has previously purchased, or related accessories. Based on the data provided by the analytics department, the recommendation department selects the most attractive products for the user and presents them to the user. The recommendation department can also recommend related products based on the user's purchase history. For example, when recommending products that the user is likely to purchase next based on data of products they have previously purchased, it can recommend products in the same category or related accessories. Furthermore, the recommendation department can recommend specific products based on the user's preferences. For example, if a user has a strong preference for a particular brand or category, it will prioritize recommending products from that brand or category. Based on the user's preferences and purchase history, the recommendation department provides personalized product recommendations to improve the user's shopping experience.

[0066] The information gathering unit understands the store layout and product placement. Specifically, the information gathering unit understands the placement of products within the store and provides information to guide users. For example, the information gathering unit understands the placement of products within the store and provides guidance to users. The information gathering unit can also understand product placement based on the store layout. Furthermore, the information gathering unit can also provide guidance to users based on the placement of products within the store. For example, the information gathering unit understands the placement of products within the store and provides guidance to users. The information gathering unit understands the placement of products within the store and provides information to guide users. For example, the information gathering unit understands the placement of products within the store and provides guidance to users. The information gathering unit can also understand product placement based on the store layout. Furthermore, the information gathering unit can also provide guidance to users based on the placement of products within the store. For example, the information gathering unit understands the placement of products within the store and provides guidance to users.

[0067] The guidance unit provides real-time guidance on the location of necessary products based on the information gathered by the information gathering unit. Specifically, when a user asks for the location of a product using their smartphone, the guidance unit shows the location of the product within the store and guides the user along the shortest route. The guidance unit can also guide the user along the optimal route based on the user's location information. Furthermore, the guidance unit can also provide guidance to the user based on the location of products within the store. For example, when a user asks for the location of a product using their smartphone, the guidance unit shows the location of the product within the store and guides the user along the shortest route. The guidance unit shows the location of the product within the store and guides the user along the shortest route. Furthermore, the guidance unit can also guide the user along the optimal route based on the user's location information. Furthermore, the guidance unit can also provide guidance to the user based on the location of products within the store. For example, when a user asks for the location of a product using their smartphone, the guidance unit shows the location of the product within the store and guides the user along the shortest route.

[0068] The inventory management department learns the frequency of use of consumables in daily life and notifies users before inventory runs out. Specifically, the inventory management department learns the frequency of use of consumables such as toilet paper and detergent and notifies users when inventory is running low. The inventory management department can also learn the frequency of use of consumables based on users' purchase history. Furthermore, the inventory management department can also notify users when inventory is running low based on the frequency of use of consumables. For example, the inventory management department learns the frequency of use of consumables such as toilet paper and detergent and notifies users when inventory is running low. The inventory management department learns the frequency of use of consumables based on users' purchase history and notifies users when inventory is running low. Furthermore, the inventory management department can also notify users when inventory is running low based on the frequency of use of consumables. For example, the inventory management department learns the frequency of use of consumables such as toilet paper and detergent and notifies users when inventory is running low.

[0069] 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 stressed, the analysis unit can improve the accuracy of the analysis to provide more specific product recommendations. If the user is relaxed, the analysis unit can also adjust the accuracy of the analysis to recommend a wider range of products. Furthermore, if the user is in a hurry, the analysis unit can optimize the accuracy of the analysis to recommend products quickly. For example, if the user is stressed, the analysis unit can improve the accuracy of the analysis to provide more specific product recommendations. This allows for more appropriate product recommendations by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The analysis unit can optimize its analysis algorithm by considering the user's past purchasing patterns when analyzing purchase history. For example, the analysis unit can analyze products that the user is likely to purchase next based on the frequency of products they have purchased in the past. The analysis unit can also analyze products that the user purchases in a particular season based on their past purchasing patterns. Furthermore, the analysis unit can analyze products related to a specific event by analyzing the user's past purchasing patterns. For example, the analysis unit can analyze products that the user is likely to purchase next based on the frequency of products they have purchased in the past. This allows for more accurate product recommendations by considering the user's past purchasing patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0071] The analysis unit can reflect the user's lifestyle and seasonal preferences when analyzing purchase history. For example, the analysis unit can analyze health-oriented products based on the user's lifestyle. It can also analyze cold drinks in summer and hot drinks in winter, taking seasonal preferences into account. Furthermore, the analysis unit can analyze products purchased at specific times of day based on the user's lifestyle. For example, the analysis unit can analyze health-oriented products based on the user's lifestyle. By reflecting the user's lifestyle and seasonal preferences, more appropriate product recommendations become possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0072] 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 nervous, the analysis unit provides a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. This allows for a more visually appealing display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The analysis unit can perform analysis of purchase history while taking into account the user's geographical location. For example, the analysis unit can analyze products available for purchase at nearby stores based on the user's current location. The analysis unit can also analyze region-specific products while taking into account the user's geographical location. Furthermore, the analysis unit can analyze products popular in a specific region based on the user's geographical location. For example, the analysis unit can analyze products available for purchase at nearby stores based on the user's current location. This makes it possible to recommend products that are appropriate for the region by taking into account the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0074] The analysis unit can analyze users' social media activity and incorporate relevant data into the analysis when analyzing purchase history. For example, the analysis unit can analyze products of interest based on users' "likes" and comments on social media. The analysis unit can also analyze the purchasing trends of users' social media followers and analyze related products. Furthermore, the analysis unit can analyze products of interest based on the content of users' social media posts. For example, the analysis unit can analyze products of interest based on users' "likes" and comments on social media. This makes it possible to recommend products that are more relevant to users by considering their social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0075] The recommendation system can estimate the user's emotions and adjust how recommended products are presented based on those emotions. For example, if the user is relaxed, the recommendation system will provide product recommendations with detailed descriptions. If the user is in a hurry, the recommendation system can provide concise and to-the-point recommendations. Furthermore, if the user is excited, the recommendation system can provide visually appealing product recommendations. For example, if the user is relaxed, the recommendation system will provide product recommendations with detailed descriptions. By adjusting how recommended products are presented according to the user's emotions, more appropriate product recommendations become possible. 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.

[0076] The recommendation system can adjust the level of detail in recommendations based on the importance of the products. For example, it will recommend highly important products with detailed descriptions, while recommending less important products with concise descriptions. Furthermore, the recommendation system can adjust the display order of recommended products according to their importance. For example, it will recommend highly important products with detailed descriptions. By adjusting the level of detail in recommendations according to the importance of the products, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI.

[0077] The recommendation system can apply different recommendation algorithms depending on the product category. For example, for food products, the recommendation system applies a recommendation algorithm that takes into account expiration dates and nutritional value. For clothing products, the recommendation system can also apply a recommendation algorithm that takes into account seasons and trends. Furthermore, for home appliance products, the recommendation system can also apply a recommendation algorithm that takes into account functions and price. For example, for food products, the recommendation system applies a recommendation algorithm that takes into account expiration dates and nutritional value. By applying a recommendation algorithm appropriate to the product category, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without using AI.

[0078] The recommendation system can estimate the user's emotions and prioritize recommended products based on those emotions. For example, if the user is relaxed, the recommendation system will prioritize recommending products with detailed descriptions. If the user is in a hurry, the recommendation system can prioritize recommending products that are concise and to the point. Furthermore, if the user is excited, the recommendation system can prioritize recommending visually appealing products. For example, if the user is relaxed, the recommendation system will prioritize recommending products with detailed descriptions. This allows for more appropriate product recommendations by prioritizing products according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The recommendation department can determine the priority of recommendations based on the timing of product submission. For example, the recommendation department may prioritize new products. It may also prioritize products during sales periods. Furthermore, it may prioritize seasonal products. For example, the recommendation department may prioritize new products. By determining the priority of recommendations based on the timing of product submission, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation department may be performed using AI, for example, or not using AI.

[0080] The recommendation system can adjust the order of recommendations based on product relevance. For example, it may prioritize recommending highly relevant products based on the user's past purchase history. It can also prioritize recommending highly relevant products based on the user's preferences. Furthermore, it can prioritize recommending highly relevant products based on the user's current purchasing patterns. For example, it may prioritize recommending highly relevant products based on the user's past purchase history. By adjusting the order of recommendations based on product relevance, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI.

[0081] The understanding unit can estimate the user's emotions and adjust the accuracy of its understanding of the store layout based on the estimated emotions. For example, if the user is feeling stressed, the understanding unit can improve the accuracy of its understanding of the store layout to allow the user to find products more quickly. Similarly, if the user is relaxed, the understanding unit can adjust the accuracy of its understanding of the store layout to allow the user to find a wider range of products. Furthermore, if the user is in a hurry, the understanding unit can optimize the accuracy of its understanding of the store layout to allow the user to find products more quickly. This means that by adjusting the accuracy of the understanding of the store layout according to the user's emotions, it becomes possible to find products more quickly. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The understanding unit can optimize its understanding algorithm by referring to past store data when understanding store layouts. For example, the understanding unit can analyze product placement patterns based on past store data and apply the optimal understanding algorithm. The understanding unit can also analyze product placement patterns in specific seasons from past store data. Furthermore, the understanding unit can analyze product placement patterns in specific events by analyzing past store data. For example, the understanding unit can analyze product placement patterns based on past store data and apply the optimal understanding algorithm. This makes it possible to understand store layouts with higher accuracy by referring to past store data. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without using AI.

[0083] The layout recognition unit can reflect store renovations and seasonal layout changes when recognizing store layouts. For example, the unit can recognize the latest layout based on store renovation information. The unit can also recognize product placement by reflecting seasonal layout changes. Furthermore, the unit can optimize its recognition algorithm in response to store renovations and layout changes. For example, the unit can recognize the latest layout based on store renovation information. This makes it possible to recognize the latest store layout by reflecting store renovations and seasonal layout changes. Some or all of the above processing in the layout recognition unit may be performed using AI, for example, or without using AI.

[0084] The understanding unit can estimate the user's emotions and adjust the display method of the store layout based on the estimated user emotions. For example, if the user is nervous, the understanding unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, it can provide a concise display method if the user is in a hurry. For example, if the understanding unit is nervous, it can provide a simple and highly visible display method. By adjusting the display method of the store layout according to the user's emotions, a more visually appealing display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The understanding unit can understand store layouts while taking into account the user's geographical location information. For example, the understanding unit can understand the layout of nearby stores based on the user's current location. The understanding unit can also understand region-specific store layouts while taking into account the user's geographical location information. Furthermore, the understanding unit can understand the placement of popular products in a specific region based on the user's geographical location information. For example, the understanding unit can understand the layout of nearby stores based on the user's current location. This makes it possible to understand store layouts that are appropriate for the region by taking into account the user's geographical location information. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without using AI.

[0086] The understanding unit can analyze the store's social media activity and incorporate relevant data into its understanding of the store layout. For example, the understanding unit can understand the placement of products based on the content of the store's social media posts. It can also analyze the purchasing trends of the store's social media followers and understand the placement of related products. Furthermore, the understanding unit can understand the placement of products based on the "likes" and comments on the store's social media posts. For example, the understanding unit can understand the placement of products based on the content of the store's social media posts. This makes it possible to understand a more appropriate store layout by considering the store's social media activity. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without using AI.

[0087] The guidance unit can estimate the user's emotions and adjust the way the guidance is presented based on the estimated emotions. For example, if the user is relaxed, the guidance unit will provide guidance that includes detailed explanations. If the user is in a hurry, the guidance unit can provide concise and to-the-point guidance. Furthermore, if the user is excited, the guidance unit can provide visually appealing guidance. For example, if the guidance unit is relaxed, it will provide guidance that includes detailed explanations. By adjusting the way the guidance is presented according to the user's emotions, more appropriate guidance becomes possible. 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.

[0088] The guidance unit can adjust the level of detail in the guidance based on the importance of the product. For example, the guidance unit provides detailed explanations for highly important products. Conversely, it can provide concise explanations for less important products. Furthermore, the guidance unit can adjust the display order of the guidance according to its importance. For example, it provides detailed explanations for highly important products. By adjusting the level of detail in the guidance according to the importance of the product, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI.

[0089] The guidance unit can apply different guidance algorithms depending on the product category during guidance. For example, for food products, the guidance unit applies a guidance algorithm that takes into account expiration dates and nutritional value. The guidance unit can also apply a guidance algorithm that takes into account seasons and trends for clothing products. Furthermore, the guidance unit can apply a guidance algorithm that takes into account functions and price for home appliance products. For example, the guidance unit applies a guidance algorithm that takes into account expiration dates and nutritional value for food products. By applying a guidance algorithm appropriate to the product category, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI.

[0090] The guidance unit can estimate the user's emotions and prioritize guidance based on those emotions. For example, if the user is relaxed, the guidance unit will prioritize guidance that includes detailed explanations. If the user is in a hurry, the guidance unit can also prioritize concise and to-the-point guidance. Furthermore, if the user is excited, the guidance unit can prioritize visually appealing guidance. For example, if the user is relaxed, the guidance unit will prioritize guidance that includes detailed explanations. This allows for more appropriate guidance by prioritizing guidance according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The guidance unit can determine the priority of guidance based on the product submission timing. For example, the guidance unit may prioritize guidance for new products. It may also prioritize guidance for products during sales periods. Furthermore, it may prioritize guidance for seasonal products. For example, the guidance unit may prioritize guidance for new products. This allows for more appropriate guidance by determining the priority of guidance based on the product submission timing. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI.

[0092] The guidance unit can adjust the order of guidance based on the relevance of the products during the guidance process. For example, the guidance unit prioritizes guiding users to highly relevant products based on the user's past purchase history. The guidance unit can also prioritize guiding users to highly relevant products based on the user's preferences. Furthermore, the guidance unit can prioritize guiding users to highly relevant products based on the user's current purchasing patterns. For example, the guidance unit prioritizes guiding users to highly relevant products based on the user's past purchase history. By adjusting the order of guidance based on the relevance of the products, more appropriate guidance becomes possible. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI.

[0093] The inventory management department can estimate the user's emotions and adjust the inventory management notification method based on the estimated emotions. For example, if the user is relaxed, the inventory management department can send a notification with a detailed explanation. If the user is in a hurry, the inventory management department can send a concise and to-the-point notification. Furthermore, if the user is excited, the inventory management department can send a visually appealing notification. For example, if the user is relaxed, the inventory management department can send a notification with a detailed explanation. By adjusting the inventory management notification method according to the user's emotions, more appropriate notifications can be made. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The inventory management department can optimize its inventory management algorithm by referring to historical inventory data during inventory management. For example, the inventory management department can analyze product consumption patterns based on historical inventory data and apply the optimal inventory management algorithm. Furthermore, the inventory management department can analyze product consumption patterns in specific seasons from historical inventory data. In addition, the inventory management department can analyze product consumption patterns during specific events by analyzing historical inventory data. For example, the inventory management department can analyze product consumption patterns based on historical inventory data and apply the optimal inventory management algorithm. This allows for more accurate inventory management by referring to historical inventory data. Some or all of the above processes in the inventory management department may be performed using AI, for example, or without AI.

[0095] The inventory management department can learn product consumption patterns during inventory management and issue notifications at the optimal time before inventory runs out. For example, the inventory management department can learn product consumption patterns and issue notifications when inventory is low. It can also learn product consumption patterns and issue notifications at specific time periods. Furthermore, the inventory management department can learn product consumption patterns and issue notifications in conjunction with specific events. For example, the inventory management department can learn product consumption patterns and issue notifications when inventory is low. In this way, by learning product consumption patterns, it becomes possible to issue notifications at the appropriate time before inventory runs out. Some or all of the above processes in the inventory management department may be performed using AI, for example, or not using AI.

[0096] The inventory management department can estimate the user's emotions and determine inventory management priorities based on those emotions. For example, if the user is relaxed, the inventory management department will prioritize inventory management that includes detailed explanations. If the user is in a hurry, the inventory management department can also prioritize inventory management that is concise and to the point. Furthermore, if the user is excited, the inventory management department can prioritize inventory management that is visually appealing. For example, if the user is relaxed, the inventory management department will prioritize inventory management that includes detailed explanations. This allows for more appropriate inventory management by prioritizing inventory management according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The inventory management department can perform inventory management while taking into account the user's geographical location information. For example, the inventory management department can manage the inventory of products that can be purchased at nearby stores based on the user's current location. The inventory management department can also manage the inventory of region-specific products while taking into account the user's geographical location information. Furthermore, the inventory management department can manage the inventory of products that are popular in a specific region based on the user's geographical location information. For example, the inventory management department can manage the inventory of products that can be purchased at nearby stores based on the user's current location. This makes it possible to perform region-appropriate inventory management by taking into account the user's geographical location information. Some or all of the above processes in the inventory management department may be performed using AI, for example, or without using AI.

[0098] The inventory management department can analyze users' social media activity and incorporate relevant data into inventory management. For example, the inventory management department can manage inventory of products of interest based on users' "likes" and comments on social media. Furthermore, the inventory management department can analyze the purchasing trends of users' social media followers and manage inventory of related products. In addition, the inventory management department can manage inventory of products of interest based on the content of users' social media posts. For example, the inventory management department can manage inventory of products of interest based on users' "likes" and comments on social media. This allows for inventory management of products of greater interest by considering users' social media activity. Some or all of the above processes in the inventory management department may be performed using AI, for example, or without AI.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The shopping support system can also acquire user health data and analyze it in its analysis unit. For example, the analysis unit can recommend health-conscious products based on the user's health data, such as steps taken and heart rate. It can also recommend foods containing specific nutrients based on the user's health condition. Furthermore, the analysis unit can recommend products suitable for use after exercise based on the user's health data. For example, it can recommend a protein drink suitable for use after exercise based on the user's step count data. This enables product recommendations tailored to the user's health condition.

[0101] The shopping support system can further estimate the user's emotions and adjust the analysis results from the analysis unit based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can recommend products with relaxing effects. It can also recommend products suitable as special gifts if the user is happy. Furthermore, if the user is tired, the analysis unit can recommend products suitable for energy replenishment. For instance, if the user is stressed, the analysis unit can recommend relaxing products such as aromatherapy candles or herbal teas. This enables product recommendations tailored to the user's emotions.

[0102] The shopping support system can further estimate the user's purchase intent and adjust how recommended products are displayed in the recommendation section based on that estimated intent. For example, if the user's purchase intent is high, the recommendation section will recommend products with detailed descriptions. If the user's purchase intent is low, the recommendation section can also recommend products with concise descriptions. Furthermore, if the user's purchase intent is moderate, the recommendation section can provide a visually appealing display method. For example, if the user's purchase intent is high, the recommendation section will provide recommendations that include detailed product descriptions and reviews. This enables product recommendations that are tailored to the user's purchase intent.

[0103] The shopping support system can further analyze users' social media activity and reflect the results in the analysis unit. For example, the analysis unit can recommend products of interest based on products that users have "liked" or commented on on social media. The analysis unit can also analyze the purchasing trends of users' followers and recommend related products. Furthermore, the analysis unit can recommend products of interest based on the content of users' posts. For example, the analysis unit can recommend related products based on products that users have "liked" on social media. This makes it possible to recommend products that take into account users' social media activity.

[0104] The shopping support system can further estimate the user's emotions and adjust the guidance method in the guidance section based on the estimated emotions. For example, if the user is relaxed, the guidance section can provide guidance that includes detailed explanations. If the user is in a hurry, the guidance section can provide concise and to-the-point guidance. Furthermore, if the user is excited, the guidance section can provide visually appealing guidance. For example, if the user is relaxed, the guidance section can provide guidance that includes detailed product descriptions and reviews. This enables guidance that is tailored to the user's emotions.

[0105] The shopping support system can further optimize its analysis algorithms based on the user's purchase history. For example, the analysis unit can analyze products the user is likely to purchase next based on the frequency of past purchases. It can also analyze products purchased during specific seasons based on the user's past purchasing patterns. Furthermore, the analysis unit can analyze products related to specific events based on the user's past purchasing patterns. For instance, the analysis unit can analyze products the user is likely to purchase next based on the frequency of past purchases. This enables product recommendations that take the user's past purchasing patterns into consideration.

[0106] The shopping support system can further estimate the user's emotions and adjust the notification method from the inventory management department based on the estimated emotions. For example, if the user is relaxed, the inventory management department can send a notification with a detailed explanation. If the user is in a hurry, the inventory management department can send a concise and to-the-point notification. Furthermore, if the user is excited, the inventory management department can send a visually appealing notification. For example, if the user is relaxed, the inventory management department can send a notification with a detailed product description and review. This enables inventory management notifications that are tailored to the user's emotions.

[0107] The shopping support system can also acquire the user's geographical location information and reflect it in the analysis results of the analysis unit. For example, the analysis unit can analyze products available at nearby stores based on the user's current location. Furthermore, the analysis unit can analyze region-specific products, taking the user's geographical location information into consideration. Additionally, the analysis unit can analyze popular products in specific regions based on the user's geographical location information. For example, the analysis unit can analyze products available at nearby stores based on the user's current location. This enables product recommendations that take the user's geographical location information into account.

[0108] The shopping support system can further estimate the user's emotions and, based on those emotions, prioritize recommended products in the recommendation section. For example, if the user is relaxed, the recommendation section will prioritize recommending products with detailed descriptions. If the user is in a hurry, the recommendation section can prioritize recommending products that are concise and to the point. Furthermore, if the user is excited, the recommendation section can prioritize recommending visually appealing products. For example, if the user is relaxed, the recommendation section will prioritize recommending products that include detailed descriptions and reviews. This enables product recommendations that are tailored to the user's emotions.

[0109] The shopping support system can further estimate the user's purchase intent and adjust the guidance method in the guidance section based on the estimated intent. For example, if the user's purchase intent is high, the guidance section will provide guidance with detailed explanations. If the user's purchase intent is low, the guidance section can provide guidance with concise explanations. Furthermore, if the user's purchase intent is moderate, the guidance section can provide visually appealing display methods. For example, if the user's purchase intent is high, the guidance section will provide guidance that includes detailed product descriptions and reviews. This enables guidance tailored to the user's purchase intent.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The analysis unit analyzes the user's purchase history and preferences. For example, it learns data on products purchased in the past and the user's preferences to analyze products that are likely to be purchased next. It can also analyze the purchase frequency of specific products. Step 2: The recommendation unit recommends the most suitable products based on the results analyzed by the analysis unit. For example, it recommends products that the user is likely to purchase next based on their preferences. It can also recommend related products. Step 3: The understanding unit grasps the store layout and product placement. For example, it understands the placement of products within the store and provides guidance to users. It can also understand product placement based on the store layout. Step 4: The guidance unit provides real-time guidance on the location of necessary products based on the information gathered by the information gathering unit. For example, if a user asks for the location of a product using their smartphone, the guidance unit will show the location of the product within the store and guide the user along the shortest route. Step 5: The inventory management department learns the frequency of use of consumable items in daily life and sends notifications before inventory runs out. For example, it learns the frequency of use of consumables such as toilet paper and detergent and notifies users when inventory is running low.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the analysis unit, recommendation unit, information unit, guidance unit, and inventory management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and the identification processing unit 290 of the data processing unit 12, and analyzes the user's purchase history and preferences. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12, and recommends the optimal product based on the analysis results. The information unit is implemented by the control unit 46A of the smart device 14, and grasps the store layout and product placement. The guidance unit is implemented by the control unit 46A of the smart device 14, and provides real-time guidance on the location of necessary products. The inventory management unit is implemented by the identification processing unit 290 of the data processing unit 12, and learns the frequency of use of consumables and notifies the user when the inventory is low. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the analysis unit, recommendation unit, information unit, guidance unit, and inventory management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and the identification unit 290 of the data processing unit 12, and analyzes the user's purchase history and preferences. The recommendation unit is implemented by the identification unit 290 of the data processing unit 12, and recommends the most suitable products based on the analysis results. The information unit is implemented by the control unit 46A of the smart glasses 214, and grasps the store layout and product placement. The guidance unit is implemented by the control unit 46A of the smart glasses 214, and provides real-time guidance on the location of necessary products. The inventory management unit is implemented by the identification unit 290 of the data processing unit 12, and learns the frequency of use of consumables and notifies the user when inventory is low. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the analysis unit, recommendation unit, information unit, guidance unit, and inventory management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and the identification processing unit 290 of the data processing unit 12, and analyzes the user's purchase history and preferences. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12, and recommends the most suitable products based on the analysis results. The information unit is implemented by the control unit 46A of the headset terminal 314, and grasps the store layout and product placement. The guidance unit is implemented by the control unit 46A of the headset terminal 314, and provides real-time guidance on the location of necessary products. The inventory management unit is implemented by the identification processing unit 290 of the data processing unit 12, and learns the frequency of use of consumables and notifies the user when inventory is low. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the analysis unit, recommendation unit, information gathering unit, guidance unit, and inventory management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and the identification processing unit 290 of the data processing unit 12, and analyzes the user's purchase history and preferences. The recommendation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and recommends the optimal product based on the analysis results. The information gathering unit is implemented by, for example, the control unit 46A of the robot 414, and grasps the store layout and product placement. The guidance unit is implemented by, for example, the control unit 46A of the robot 414, and provides real-time guidance on the location of necessary products. The inventory management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and learns the frequency of use of consumables and notifies the user when the inventory is low. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) An analysis unit that analyzes the user's purchase history and preferences, A recommendation unit recommends the most suitable product based on the results of the analysis performed by the aforementioned analysis unit, A unit that grasps the store layout and product placement, A guidance unit provides real-time guidance on the location of necessary products based on the information grasped by the aforementioned grasping unit. It includes an inventory management unit that learns the frequency of use of consumable items in daily life and sends notifications before inventory runs out. A system characterized by the following features. (Note 2) 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 3) The aforementioned analysis unit, When analyzing purchase history, the analysis algorithm is optimized by considering the user's past purchasing patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, When analyzing purchase history, reflect the user's lifestyle and seasonal preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) 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 6) The aforementioned analysis unit, When analyzing purchase history, the analysis takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, When analyzing purchase history, the system analyzes users' social media activity and incorporates relevant data into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recommendation department, The system estimates the user's emotions and adjusts how recommended products are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recommendation department, When making recommendations, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recommendation department, It estimates the user's emotions and prioritizes recommended products based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recommendation department, When making a recommendation, we will prioritize recommendations based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 14) The gripping part is, The system estimates user emotions and adjusts the accuracy of store layout analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The gripping part is, When determining the store layout, the algorithm is optimized by referring to past store data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The gripping part is, When understanding the store layout, take into account store renovations and seasonal layout changes. The system described in Appendix 1, characterized by the features described herein. (Note 17) The gripping part is, The system estimates the user's emotions and adjusts the display method of the store layout based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The gripping part is, When determining the store layout, the geographical location information of the user should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The gripping part is, When understanding store layouts, analyze the store's social media activity and incorporate relevant data into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guidance unit, It estimates the user's emotions and adjusts the way guidance is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guidance unit, During guidance, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guidance unit, During guidance, different guidance algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guidance unit, It estimates the user's emotions and determines the priority of guidance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guidance unit, During the guidance session, we will prioritize the guidance based on the timing of product submission. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guidance unit, During guidance, the order of guidance will be adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned inventory management department, It estimates the user's emotions and adjusts the inventory management notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned inventory management department, During inventory management, the inventory management algorithm is optimized by referring to past inventory data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned inventory management department, During inventory management, the system learns product consumption patterns and sends notifications at the optimal time before inventory runs out. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned inventory management department, The system estimates user sentiment and prioritizes inventory management based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned inventory management department, When managing inventory, the system takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned inventory management department, Analyze users' social media activity during inventory management and incorporate relevant data into inventory management. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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. An analysis unit that analyzes the user's purchase history and preferences, A recommendation unit recommends the most suitable product based on the results of the analysis performed by the aforementioned analysis unit, A unit that grasps the store layout and product placement, A guidance unit provides real-time guidance on the location of necessary products based on the information grasped by the aforementioned grasping unit. It includes an inventory management unit that learns the frequency of use of consumable items in daily life and sends notifications before inventory runs out. A system characterized by the following features.

2. 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 according to feature 1.

3. The aforementioned analysis unit, When analyzing purchase history, the analysis algorithm is optimized by considering the user's past purchasing patterns. The system according to feature 1.

4. The aforementioned analysis unit, When analyzing purchase history, reflect the user's lifestyle and seasonal preferences. The system according to feature 1.

5. 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 according to feature 1.

6. The aforementioned analysis unit, When analyzing purchase history, the analysis takes into account the user's geographical location. The system according to feature 1.

7. The aforementioned analysis unit, When analyzing purchase history, the system analyzes users' social media activity and incorporates relevant data into the analysis. The system according to feature 1.

8. The aforementioned recommendation department, The system estimates the user's emotions and adjusts how recommended products are presented based on those estimated emotions. The system according to feature 1.

9. The aforementioned recommendation department, When making recommendations, adjust the level of detail based on the importance of the product. The system according to feature 1.

10. The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the product category. The system according to feature 1.

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