system

Abstract

The system according to this embodiment aims to guide customers along the optimal shopping path and provide them with commercials for recommended products. [Solution] The system according to this embodiment comprises a reception unit, an acquisition unit, a guidance unit, a generation unit, and a provision unit. The reception unit grasps the customer's needs. The acquisition unit acquires product information and inventory information within the store based on the needs grasped by the reception unit. The guidance unit guides the customer along the optimal shopping route based on the information acquired by the acquisition unit. The generation unit generates commercials for recommended products based on the information guided by the guidance unit. The provision unit provides the commercials generated by the generation unit to the customer.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that there is a shortage of sales floor guides, making it difficult to provide customers with appropriate product information and shopping guidance.

[0005] The system according to the embodiment aims to guide customers to the optimal shopping guidance and provide CM for recommended products.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an acquisition unit, a guidance unit, a generation unit, and a provision unit. The reception unit grasps the customer's needs. The acquisition unit acquires product information and inventory information within the store based on the needs grasped by the reception unit. The guidance unit guides the customer along the optimal shopping route based on the information acquired by the acquisition unit. The generation unit generates commercials for recommended products based on the information guided by the guidance unit. The provision unit provides the commercials generated by the generation unit to the customer. [Effects of the Invention]

[0007] The system according to this embodiment can guide customers along the optimal shopping path and provide them with commercials for recommended products. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An agent AI system according to an embodiment of the present invention is a system that uses AI to improve the in-store shopping experience. When a customer enters a store, this agent AI system understands the customer's needs. Next, the agent AI system guides the customer to the optimal shopping route based on product information and inventory information within the store. In addition to guiding customers to the location of products, the agent AI system also generates commercials for recommended products and guides customers through them while they shop. As a result, customers can enjoy shopping efficiently, and stores can increase sales while reducing human resource costs. For example, when a customer enters a store, the agent AI system understands the customer's needs. At this time, the customer only needs to input the products they want to buy or are interested in. For example, if a customer inputs "I want to buy a smartphone," the agent AI system guides the customer to the optimal shopping route based on that information. Next, the agent AI system guides the customer to the optimal shopping route based on product information and inventory information within the store. For example, the agent AI system guides the customer to the location of the smartphone section, allowing the customer to efficiently find the desired product. In addition, since the agent AI system also understands product inventory information, it can check in real time whether the product is in stock. Furthermore, the agent AI system not only guides customers to the location of products but also generates commercials for recommended products and displays them to customers while they shop. For example, when a customer purchases a smartphone, the agent AI system recommends smartphone-related accessories and services, generates commercials for them, and displays them to the customer. This makes it easier for customers to find products that meet their needs, improving their shopping experience. This system allows stores to increase sales while reducing human resource costs. By understanding customer needs and guiding them along the optimal shopping path, the agent AI system allows customers to enjoy shopping efficiently. In addition, by generating commercials for recommended products and displaying them during shopping, the agent AI system can increase customers' purchasing intent. This allows stores to increase sales while reducing marketing costs.This allows the agent AI system to guide customers along the optimal shopping path based on their needs and provide commercials for recommended products, thereby improving the shopping experience.

[0029] The agent AI system according to this embodiment comprises a reception unit, an acquisition unit, a guidance unit, a generation unit, and a provision unit. The reception unit grasps the customer's needs. For example, the reception unit can grasp the customer's needs by having the customer input products they want to purchase or products they are interested in. The acquisition unit acquires product information and inventory information within the store based on the needs grasped by the reception unit. For example, the acquisition unit can acquire product information and inventory information in real time by linking with the store's database. The guidance unit guides the customer to the optimal shopping route based on the information acquired by the acquisition unit. For example, the guidance unit can guide the customer to the optimal shopping route based on the acquired information. The generation unit generates commercials for recommended products based on the information guided by the guidance unit. For example, the generation unit can generate commercials for recommended products based on the customer's past purchase history and interests. The provision unit provides the commercials generated by the generation unit to the customer. For example, the provision unit can provide the generated commercials to the customer. As a result, the agent AI system according to this embodiment can improve the shopping experience by guiding customers along the optimal shopping path based on their needs and providing commercials for recommended products.

[0030] The reception department understands customer needs. Specifically, the reception department can understand customer needs by having customers input the products they want to purchase or are interested in. For example, customers input the category and specific product name of the product they want to purchase through a smartphone application. This allows the reception department to accurately obtain the product information the customer desires. The reception department can also understand customer interests and preferences by referring to the customer's past purchase and browsing history. For example, it can predict products that the customer might be interested in based on information about products the customer has purchased or viewed in the past. Furthermore, the reception department can understand customer needs in more detail through dialogue with customers. For example, it can use a chatbot to interact with customers and delve deeper into their needs by responding to their specific requests and questions. This allows the reception department to understand customer needs from multiple perspectives and accurately provide information to the next step, the acquisition department.

[0031] The information acquisition unit retrieves product and inventory information from within the store based on the needs identified by the reception unit. Specifically, the information acquisition unit can access the store's database to obtain product and inventory information in real time. For example, it can check the inventory status of a product that a customer has entered as a purchase request and retrieve detailed information about that product. The information acquisition unit can use sensors and RFID tags placed in each section of the store to grasp the location and inventory levels of products in real time. The information acquisition unit can also access product arrival schedules and inventory replenishment schedules by linking with the store's backend system. This allows the information acquisition unit to provide customers with accurate inventory information. Furthermore, the information acquisition unit can integrate inventory information from multiple stores to check the inventory status of the product a customer wants at the nearest store. This allows the information acquisition unit to collect information to provide customers with the best possible shopping experience and provide that information to the next step, the information desk.

[0032] The information desk guides customers along the optimal shopping route based on the information acquired by the information acquisition unit. Specifically, the information desk suggests shopping routes based on the acquired information so that customers can efficiently find the products they are looking for. For example, it displays a map of the store and guides customers to the shortest route from their current location to the location of the desired product. The information desk can also suggest routes that allow customers to move comfortably, taking into account factors such as the store's congestion level and the width of aisles. In addition, the information desk can place related products and recommended products that customers might be interested in along the route, guiding customers to naturally look at those products. Furthermore, the information desk can provide attractive promotions and sales information to customers based on their shopping history and interests. In this way, the information desk can provide customers with the optimal shopping experience and improve customer satisfaction.

[0033] The generation unit generates commercials for recommended products based on the information provided by the guidance unit. Specifically, the generation unit generates commercials for recommended products that are best suited to the customer, based on the customer's past purchase history and interests. For example, it identifies products that the customer is likely to be interested in based on information about products the customer has previously purchased or viewed, and generates a commercial that conveys the appeal of those products. The generation unit can use AI to analyze customer preferences and interests and automatically generate commercials that are appealing to the customer. For example, the generation unit analyzes the categories and brands of products the customer has previously purchased and generates commercials that recommend products in the same category or brand. The generation unit can also generate commercials that emphasize the features and benefits of products to attract the customer's interest. As a result, the generation unit can generate commercials for recommended products that are appealing to the customer and provide this information to the next step, the supply unit.

[0034] The distribution department provides customers with the commercials (CMs) generated by the generation department. Specifically, the distribution department provides the generated CMs to customers at the appropriate time. For example, it may display CMs through a smartphone application while customers are moving around the store. The distribution department can also provide CMs through digital signage or in-store broadcasts when customers reach a specific area within the store. Furthermore, the distribution department can provide CMs along with receipts and coupons while customers are paying at the register. This allows the distribution department to effectively deliver CMs to customers and increase their purchasing intent. In addition, the distribution department can continuously improve the content and delivery methods of CMs by collecting customer reactions and feedback and providing this feedback to the generation and guidance departments. This allows the distribution department to provide customers with the best possible shopping experience and improve customer satisfaction.

[0035] The reception desk allows customers to input the products they wish to purchase or are interested in. For example, by inputting the products a customer wishes to buy or is interested in, the reception desk can accurately understand the customer's needs. For instance, if a customer inputs "I want to buy a smartphone," the reception desk can use that information to guide them through the optimal shopping process. This allows for an accurate understanding of customer needs through the input of products they wish to purchase or are interested in. Some or all of the above-described processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the product information entered by the customer into a generating AI, which can then understand the customer's needs.

[0036] The acquisition unit can acquire product information and inventory information in real time by linking with the store's database. For example, by linking with the store's database and acquiring product information and inventory information in real time, the acquisition unit can provide the latest information. For example, the acquisition unit can acquire product information and inventory information in real time by linking with the store's database. This allows for the provision of the latest information by acquiring product information and inventory information in real time. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input product information and inventory information acquired from the store's database into a generating AI, which can then acquire the information in real time.

[0037] The guidance unit can guide customers to the optimal shopping route based on the acquired information. For example, by guiding customers to the optimal shopping route based on the acquired information, the guidance unit enables efficient shopping. For example, the guidance unit can guide customers to the optimal shopping route based on product information and inventory information within the store. This makes efficient shopping possible by guiding customers to the optimal shopping route. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input acquired information into a generating AI, and the generating AI can guide customers to the optimal shopping route.

[0038] The generation unit can generate commercials for recommended products based on the customer's past purchase history and interests. For example, by generating commercials for recommended products based on the customer's past purchase history and interests, the generation unit can increase the customer's willingness to purchase. For example, the generation unit can generate commercials for recommended products based on the customer's past purchase history and interests. This allows the generation unit to increase the customer's willingness to purchase by generating commercials based on the customer's past purchase history and interests. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input the customer's past purchase history and interests into a generation AI, which can then generate commercials for recommended products.

[0039] The service provider can provide the generated commercials to customers. The service provider can, for example, increase customers' purchasing intent by providing them with the generated commercials. For example, the service provider can provide the generated commercials to customers. By providing the generated commercials to customers, it is possible to increase customers' purchasing intent. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated commercials into a generating AI, and the generating AI can provide them to customers.

[0040] The reception desk can analyze a customer's past purchase history and select the most suitable product suggestion method. For example, the reception desk can suggest products similar to those the customer has previously purchased. For example, the reception desk can suggest complementary products to those the customer has previously purchased. For example, the reception desk can suggest products highly rated by other customers based on reviews of products the customer has previously purchased. This makes it possible to make optimal product suggestions by analyzing the customer's past purchase history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the customer's past purchase history into a generating AI, which can then select the most suitable product suggestion method.

[0041] The reception desk can make optimal product suggestions based on the customer's current location. For example, if the customer is in a specific area within the store, the reception desk can suggest products related to that area. For example, if the customer is at the store entrance, the reception desk can suggest popular or sale items. For example, if the customer is near the checkout counter, the reception desk can suggest additional products to encourage purchase. This allows for more appropriate product suggestions by basing them on the customer's current location. Some or all of the above processing at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the customer's current location into a generating AI, which can then make optimal product suggestions.

[0042] The reception desk can analyze a customer's social media activity and suggest relevant products. For example, the reception desk can suggest products that the customer is talking about on social media. For example, the reception desk can suggest products that are featured by influencers the customer follows. For example, the reception desk can suggest products that are popular in communities the customer participates in. This makes it possible to suggest relevant products by analyzing the customer's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the customer's social media activity into a generating AI, which can then suggest relevant products.

[0043] The reception desk can provide coupons for relevant products based on the customer's purchase history. For example, the reception desk can provide coupons for products related to products the customer has previously purchased. For example, the reception desk can provide coupons to encourage the customer to repurchase products they have previously purchased. For example, the reception desk can provide coupons for products highly rated by other customers based on reviews of products the customer has previously purchased. This can increase the customer's willingness to purchase by providing coupons based on their purchase history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the customer's purchase history into a generating AI, and the generating AI can provide coupons for relevant products.

[0044] The acquisition unit can monitor the store's inventory status in real time and acquire optimal product information. For example, the acquisition unit can prioritize acquiring products with low stock and propose them to customers. For example, the acquisition unit can prioritize acquiring products with high stock and propose them to customers as sale information. For example, the acquisition unit can prioritize acquiring newly arrived products and propose them to customers. This allows the system to provide the latest information by monitoring the store's inventory status in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input store inventory data into a generating AI, which can monitor the inventory status in real time and acquire optimal product information.

[0045] The acquisition unit can prioritize the acquisition of inventory information for related products based on the customer's purchase history. For example, the acquisition unit can prioritize the acquisition of inventory information for products related to products the customer has previously purchased. For example, the acquisition unit can prioritize the acquisition of inventory information for complementary products to products the customer has previously purchased. For example, the acquisition unit can prioritize the acquisition of inventory information for products highly rated by other customers based on reviews of products the customer has previously purchased. This makes it possible to provide more appropriate information by acquiring inventory information based on the customer's purchase history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the customer's purchase history into a generating AI, and the generating AI can prioritize the acquisition of inventory information for related products.

[0046] The acquisition unit can acquire highly relevant product information by considering the geographical location information of the store. For example, the acquisition unit can prioritize acquiring information on popular products near the store. For example, the acquisition unit can prioritize acquiring information on products in high demand in the area surrounding the store. For example, the acquisition unit can prioritize acquiring product information that matches the store's location conditions. This makes it possible to provide more appropriate information by considering the geographical location information of the store. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the geographical location information of the store into a generating AI, and the generating AI can acquire highly relevant product information.

[0047] The acquisition unit can analyze a customer's social media activity and acquire inventory information for related products. For example, the acquisition unit can acquire inventory information for products that the customer is talking about on social media. For example, the acquisition unit can acquire inventory information for products that are featured by influencers that the customer follows. For example, the acquisition unit can acquire inventory information for products that are popular in communities that the customer participates in. In this way, inventory information for related products can be acquired by analyzing the customer's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the customer's social media activity into a generating AI, and the generating AI can acquire inventory information for related products.

[0048] The guidance system can guide customers along the optimal shopping path based on their past purchase history. For example, the guidance system can prioritize guiding customers to sections of products related to products they have previously purchased. For example, the guidance system can prioritize guiding customers to sections of complementary products to products they have previously purchased. For example, the guidance system can prioritize guiding customers to sections of products highly rated by other customers based on reviews of products they have previously purchased. This allows for more appropriate guidance by guiding customers along the shopping path based on their past purchase history. Some or all of the above processing in the guidance system may be performed using AI, for example, or without AI. For example, the guidance system can input the customer's past purchase history into a generating AI, which can then guide customers along the optimal shopping path.

[0049] The guidance unit can monitor the store's congestion level in real time and guide customers along the optimal shopping route. For example, the guidance unit can guide customers along a route that avoids crowded areas. For example, the guidance unit can guide customers along a route that prioritizes less crowded areas. For example, the guidance unit can adjust the optimal shopping route in real time according to the congestion level. This allows for more appropriate shopping routes to be guided by monitoring the store's congestion level in real time. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input store congestion data into a generating AI, which can monitor the data in real time and guide customers along the optimal shopping route.

[0050] The guidance system can guide customers along the optimal shopping route by considering their geographical location. For example, if a customer is at the entrance of a store, the guidance system can prioritize guiding them to popular or sale items. If a customer is in a specific area within the store, the guidance system can prioritize guiding them to products related to that area. If a customer is near the checkout counter, the guidance system can prioritize guiding them to additional products to encourage purchases. This allows for a more appropriate shopping route by considering the customer's geographical location. Some or all of the above processing in the guidance system may be performed using AI, for example, or without AI. For example, the guidance system can input the customer's geographical location information into a generating AI, which can then guide the customer along the optimal shopping route.

[0051] The guidance unit can analyze a customer's social media activity and guide them through relevant shopping routes. For example, the guidance unit can guide customers to the sales floor of products they are talking about on social media. For example, the guidance unit can guide customers to the sales floor of products recommended by influencers they follow. For example, the guidance unit can prioritize guiding customers to products popular in communities they participate in. In this way, by analyzing a customer's social media activity, relevant shopping routes can be guided. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input a customer's social media activity into a generating AI, which can then guide them through relevant shopping routes.

[0052] The generation unit can generate commercials (CMs) for optimally recommended products based on the customer's past purchase history. For example, the generation unit can generate CMs for products related to products the customer has previously purchased. For example, the generation unit can generate CMs for complementary products to products the customer has previously purchased. For example, the generation unit can generate CMs for products highly rated by other customers based on reviews of products the customer has previously purchased. This allows for the provision of more appropriate recommended product CMs by generating CMs based on the customer's past purchase history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's past purchase history into a generation AI, which can then generate CMs for optimally recommended products.

[0053] The generation unit can generate commercials (CMs) for relevant products based on the customer's current location information. For example, if the customer is in a specific area within a store, the generation unit can generate CMs for products related to that area. For example, if the customer is at the entrance of the store, the generation unit can generate CMs for popular or sale items. For example, if the customer is near the checkout counter, the generation unit can generate CMs for additional products to encourage purchase. This allows for the provision of more appropriate product CMs by generating them based on the customer's current location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's current location information into a generation AI, which can then generate CMs for relevant products.

[0054] The generation unit can analyze a customer's social media activity and generate commercials for relevant products. For example, the generation unit can generate commercials for products that the customer is talking about on social media. For example, the generation unit can generate commercials for products that are featured by influencers the customer follows. For example, the generation unit can prioritize generating commercials for products that are popular in communities the customer participates in. In this way, by analyzing the customer's social media activity, it is possible to provide commercials for relevant products. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's social media activity into a generation AI, and the generation AI can generate commercials for relevant products.

[0055] The generation unit can generate commercials (CMs) for relevant products based on the customer's purchase history. For example, the generation unit can generate CMs for products related to products the customer has previously purchased. For example, the generation unit can generate CMs for complementary products to products the customer has previously purchased. For example, the generation unit can generate CMs for products highly rated by other customers based on reviews of products the customer has previously purchased. This allows for the provision of more appropriate product CMs by generating them based on the customer's purchase history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's purchase history into a generation AI, which can then generate CMs for relevant products.

[0056] The service provider can provide the most suitable commercials (CMs) based on the customer's past purchase history. For example, the service provider can provide CMs for products related to products the customer has previously purchased. For example, the service provider can provide CMs for complementary products to products the customer has previously purchased. For example, the service provider can provide CMs for products highly rated by other customers based on reviews of products the customer has previously purchased. This allows for the provision of more appropriate CMs by providing them based on the customer's past purchase history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the customer's past purchase history into a generating AI, which can then provide the most suitable CMs.

[0057] The service provider can provide relevant commercials (CMs) based on the customer's current location information. For example, if the customer is in a specific area within a store, the service provider can provide commercials for products related to that area. For example, if the customer is at the entrance of the store, the service provider can provide commercials for popular or sale items. For example, if the customer is near the checkout counter, the service provider can provide commercials for additional products to encourage purchase. By providing commercials based on the customer's current location information, more appropriate commercials can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the customer's current location information into a generating AI, and the generating AI can provide relevant commercials.

[0058] The service provider can analyze a customer's social media activity and provide relevant commercials. For example, the service provider can provide commercials for products that the customer is talking about on social media. For example, the service provider can provide commercials for products that are featured by influencers the customer follows. For example, the service provider can prioritize providing commercials for products that are popular in communities the customer participates in. In this way, relevant commercials can be provided by analyzing the customer's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the customer's social media activity into a generating AI, and the generating AI can provide relevant commercials.

[0059] The service provider can provide relevant commercials (CMs) based on the customer's purchase history. For example, the service provider can provide CMs for products related to products the customer has previously purchased. For example, the service provider can provide CMs for complementary products to products the customer has previously purchased. For example, the service provider can provide CMs for products highly rated by other customers based on reviews of products the customer has previously purchased. This allows for the provision of more appropriate CMs by providing them based on the customer's purchase history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the customer's purchase history into a generating AI, and the generating AI can provide relevant CMs.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can not only understand customer needs but also analyze their past purchase history and interests to suggest the most suitable products. For example, it can suggest products related to items the customer has purchased in the past. It can also suggest complementary products to items the customer has previously purchased. Furthermore, based on reviews of products the customer has purchased in the past, it can suggest products that have received high ratings from other customers. In this way, analyzing the customer's past purchase history and interests enables more appropriate product suggestions.

[0062] The information acquisition unit not only retrieves product and inventory information in real time by linking with the store's database, but can also acquire optimal product information based on the customer's current location. For example, if a customer is in a specific area within the store, it can prioritize acquiring product information related to that area. Also, if a customer is near the store entrance, it can prioritize acquiring popular or sale items. Furthermore, if a customer is near the checkout counter, it can prioritize acquiring additional product information to encourage purchase. In this way, by acquiring product information based on the customer's current location, it becomes possible to provide more appropriate information.

[0063] The information desk not only guides customers along the optimal shopping route based on acquired information, but can also monitor the store's congestion level in real time and guide them along the optimal route. For example, it can guide customers along a route that avoids crowded areas. It can also prioritize guiding customers through less crowded areas. Furthermore, it can adjust the optimal shopping route in real time according to the congestion level. This allows for more appropriate shopping routes to be guided by monitoring the store's congestion level in real time.

[0064] The generation unit can not only generate advertisements for recommended products based on the customer's past purchase history and interests, but can also analyze the customer's social media activity and generate advertisements for related products. For example, it can generate advertisements for products that the customer is talking about on social media. It can also generate advertisements for products that are featured by influencers the customer follows. Furthermore, it can prioritize generating advertisements for products that are popular in the communities the customer participates in. In this way, by analyzing the customer's social media activity, it can provide advertisements for relevant products.

[0065] The service provider can not only deliver generated commercials to customers, but also offer coupons for related products based on the customer's purchase history. For example, it can offer coupons for products related to products the customer has previously purchased. It can also offer coupons to encourage repeat purchases of products the customer has previously bought. Furthermore, it can offer coupons for products highly rated by other customers based on the customer's reviews of products they have previously purchased. In this way, by offering coupons based on the customer's purchase history, it is possible to increase their willingness to purchase.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk understands the customer's needs. For example, by having the customer input the products they want to purchase or are interested in, the reception desk can understand the customer's needs. Step 2: The acquisition unit acquires product and inventory information from within the store based on the needs identified by the reception unit. For example, it can acquire product and inventory information in real time by linking with the store's database. Step 3: The guidance unit guides the customer to the optimal shopping route based on the information acquired by the acquisition unit. For example, it can guide the customer to the optimal shopping route based on the acquired information. Step 4: The generation unit generates commercials for recommended products based on the information provided by the guidance unit. For example, it can generate commercials for recommended products based on the customer's past purchase history and interests. Step 5: The supply unit provides the CM generated by the generation unit to the customer. For example, the generated CM can be provided to the customer.

[0068] (Example of form 2) An agent AI system according to an embodiment of the present invention is a system that uses AI to improve the in-store shopping experience. When a customer enters a store, this agent AI system understands the customer's needs. Next, the agent AI system guides the customer to the optimal shopping route based on product information and inventory information within the store. In addition to guiding customers to the location of products, the agent AI system also generates commercials for recommended products and guides customers through them while they shop. As a result, customers can enjoy shopping efficiently, and stores can increase sales while reducing human resource costs. For example, when a customer enters a store, the agent AI system understands the customer's needs. At this time, the customer only needs to input the products they want to buy or are interested in. For example, if a customer inputs "I want to buy a smartphone," the agent AI system guides the customer to the optimal shopping route based on that information. Next, the agent AI system guides the customer to the optimal shopping route based on product information and inventory information within the store. For example, the agent AI system guides the customer to the location of the smartphone section, allowing the customer to efficiently find the desired product. In addition, since the agent AI system also understands product inventory information, it can check in real time whether the product is in stock. Furthermore, the agent AI system not only guides customers to the location of products but also generates commercials for recommended products and displays them to customers while they shop. For example, when a customer purchases a smartphone, the agent AI system recommends smartphone-related accessories and services, generates commercials for them, and displays them to the customer. This makes it easier for customers to find products that meet their needs, improving their shopping experience. This system allows stores to increase sales while reducing human resource costs. By understanding customer needs and guiding them along the optimal shopping path, the agent AI system allows customers to enjoy shopping efficiently. In addition, by generating commercials for recommended products and displaying them during shopping, the agent AI system can increase customers' purchasing intent. This allows stores to increase sales while reducing marketing costs.This allows the agent AI system to guide customers along the optimal shopping path based on their needs and provide commercials for recommended products, thereby improving the shopping experience.

[0069] The agent AI system according to this embodiment comprises a reception unit, an acquisition unit, a guidance unit, a generation unit, and a provision unit. The reception unit grasps the customer's needs. For example, the reception unit can grasp the customer's needs by having the customer input products they want to purchase or products they are interested in. The acquisition unit acquires product information and inventory information within the store based on the needs grasped by the reception unit. For example, the acquisition unit can acquire product information and inventory information in real time by linking with the store's database. The guidance unit guides the customer to the optimal shopping route based on the information acquired by the acquisition unit. For example, the guidance unit can guide the customer to the optimal shopping route based on the acquired information. The generation unit generates commercials for recommended products based on the information guided by the guidance unit. For example, the generation unit can generate commercials for recommended products based on the customer's past purchase history and interests. The provision unit provides the commercials generated by the generation unit to the customer. For example, the provision unit can provide the generated commercials to the customer. As a result, the agent AI system according to this embodiment can improve the shopping experience by guiding customers along the optimal shopping path based on their needs and providing commercials for recommended products.

[0070] The reception department understands customer needs. Specifically, the reception department can understand customer needs by having customers input the products they want to purchase or are interested in. For example, customers input the category and specific product name of the product they want to purchase through a smartphone application. This allows the reception department to accurately obtain the product information the customer desires. The reception department can also understand customer interests and preferences by referring to the customer's past purchase and browsing history. For example, it can predict products that the customer might be interested in based on information about products the customer has purchased or viewed in the past. Furthermore, the reception department can understand customer needs in more detail through dialogue with customers. For example, it can use a chatbot to interact with customers and delve deeper into their needs by responding to their specific requests and questions. This allows the reception department to understand customer needs from multiple perspectives and accurately provide information to the next step, the acquisition department.

[0071] The information acquisition unit retrieves product and inventory information from within the store based on the needs identified by the reception unit. Specifically, the information acquisition unit can access the store's database to obtain product and inventory information in real time. For example, it can check the inventory status of a product that a customer has entered as a purchase request and retrieve detailed information about that product. The information acquisition unit can use sensors and RFID tags placed in each section of the store to grasp the location and inventory levels of products in real time. The information acquisition unit can also access product arrival schedules and inventory replenishment schedules by linking with the store's backend system. This allows the information acquisition unit to provide customers with accurate inventory information. Furthermore, the information acquisition unit can integrate inventory information from multiple stores to check the inventory status of the product a customer wants at the nearest store. This allows the information acquisition unit to collect information to provide customers with the best possible shopping experience and provide that information to the next step, the information desk.

[0072] The information desk guides customers along the optimal shopping route based on the information acquired by the information acquisition unit. Specifically, the information desk suggests shopping routes based on the acquired information so that customers can efficiently find the products they are looking for. For example, it displays a map of the store and guides customers to the shortest route from their current location to the location of the desired product. The information desk can also suggest routes that allow customers to move comfortably, taking into account factors such as the store's congestion level and the width of aisles. In addition, the information desk can place related products and recommended products that customers might be interested in along the route, guiding customers to naturally look at those products. Furthermore, the information desk can provide attractive promotions and sales information to customers based on their shopping history and interests. In this way, the information desk can provide customers with the optimal shopping experience and improve customer satisfaction.

[0073] The generation unit generates commercials for recommended products based on the information provided by the guidance unit. Specifically, the generation unit generates commercials for recommended products that are best suited to the customer, based on the customer's past purchase history and interests. For example, it identifies products that the customer is likely to be interested in based on information about products the customer has previously purchased or viewed, and generates a commercial that conveys the appeal of those products. The generation unit can use AI to analyze customer preferences and interests and automatically generate commercials that are appealing to the customer. For example, the generation unit analyzes the categories and brands of products the customer has previously purchased and generates commercials that recommend products in the same category or brand. The generation unit can also generate commercials that emphasize the features and benefits of products to attract the customer's interest. As a result, the generation unit can generate commercials for recommended products that are appealing to the customer and provide this information to the next step, the supply unit.

[0074] The distribution department provides customers with the commercials (CMs) generated by the generation department. Specifically, the distribution department provides the generated CMs to customers at the appropriate time. For example, it may display CMs through a smartphone application while customers are moving around the store. The distribution department can also provide CMs through digital signage or in-store broadcasts when customers reach a specific area within the store. Furthermore, the distribution department can provide CMs along with receipts and coupons while customers are paying at the register. This allows the distribution department to effectively deliver CMs to customers and increase their purchasing intent. In addition, the distribution department can continuously improve the content and delivery methods of CMs by collecting customer reactions and feedback and providing this feedback to the generation and guidance departments. This allows the distribution department to provide customers with the best possible shopping experience and improve customer satisfaction.

[0075] The reception desk allows customers to input the products they wish to purchase or are interested in. For example, by inputting the products a customer wishes to buy or is interested in, the reception desk can accurately understand the customer's needs. For instance, if a customer inputs "I want to buy a smartphone," the reception desk can use that information to guide them through the optimal shopping process. This allows for an accurate understanding of customer needs through the input of products they wish to purchase or are interested in. Some or all of the above-described processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the product information entered by the customer into a generating AI, which can then understand the customer's needs.

[0076] The acquisition unit can acquire product information and inventory information in real time by linking with the store's database. For example, by linking with the store's database and acquiring product information and inventory information in real time, the acquisition unit can provide the latest information. For example, the acquisition unit can acquire product information and inventory information in real time by linking with the store's database. This allows for the provision of the latest information by acquiring product information and inventory information in real time. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input product information and inventory information acquired from the store's database into a generating AI, which can then acquire the information in real time.

[0077] The guidance unit can guide customers to the optimal shopping route based on the acquired information. For example, by guiding customers to the optimal shopping route based on the acquired information, the guidance unit enables efficient shopping. For example, the guidance unit can guide customers to the optimal shopping route based on product information and inventory information within the store. This makes efficient shopping possible by guiding customers to the optimal shopping route. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input acquired information into a generating AI, and the generating AI can guide customers to the optimal shopping route.

[0078] The generation unit can generate commercials for recommended products based on the customer's past purchase history and interests. For example, by generating commercials for recommended products based on the customer's past purchase history and interests, the generation unit can increase the customer's willingness to purchase. For example, the generation unit can generate commercials for recommended products based on the customer's past purchase history and interests. This allows the generation unit to increase the customer's willingness to purchase by generating commercials based on the customer's past purchase history and interests. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input the customer's past purchase history and interests into a generation AI, which can then generate commercials for recommended products.

[0079] The service provider can provide the generated commercials to customers. The service provider can, for example, increase customers' purchasing intent by providing them with the generated commercials. For example, the service provider can provide the generated commercials to customers. By providing the generated commercials to customers, it is possible to increase customers' purchasing intent. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated commercials into a generating AI, and the generating AI can provide them to customers.

[0080] The reception desk can estimate the customer's emotions and adjust the timing of product recommendations based on those emotions. For example, if the customer is stressed, the reception desk can prioritize recommending products that promote relaxation. If the customer is excited, the reception desk can recommend the latest trending products. If the customer is in a hurry, the reception desk can recommend products that can be purchased quickly. By adjusting the timing of product recommendations according to the customer's emotions, more appropriate product recommendations can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input customer emotion data into a generative AI, which can estimate the emotion and adjust the timing of product recommendations.

[0081] The reception desk can analyze a customer's past purchase history and select the most suitable product suggestion method. For example, the reception desk can suggest products similar to those the customer has previously purchased. For example, the reception desk can suggest complementary products to those the customer has previously purchased. For example, the reception desk can suggest products highly rated by other customers based on reviews of products the customer has previously purchased. This makes it possible to make optimal product suggestions by analyzing the customer's past purchase history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the customer's past purchase history into a generating AI, which can then select the most suitable product suggestion method.

[0082] The reception desk can make optimal product suggestions based on the customer's current location. For example, if the customer is in a specific area within the store, the reception desk can suggest products related to that area. For example, if the customer is at the store entrance, the reception desk can suggest popular or sale items. For example, if the customer is near the checkout counter, the reception desk can suggest additional products to encourage purchase. This allows for more appropriate product suggestions by basing them on the customer's current location. Some or all of the above processing at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the customer's current location into a generating AI, which can then make optimal product suggestions.

[0083] The reception desk can estimate the customer's emotions and determine the priority of products to suggest based on the estimated emotions. For example, if the customer is relaxed, the reception desk can prioritize suggesting products with a relaxing effect. If the customer is excited, the reception desk can prioritize suggesting products with a high level of entertainment. If the customer is tired, the reception desk can prioritize suggesting products that can refresh them. This allows for more appropriate product suggestions by prioritizing products according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input customer emotion data into a generative AI, which can estimate the emotions and determine the priority of products.

[0084] The reception desk can analyze a customer's social media activity and suggest relevant products. For example, the reception desk can suggest products that the customer is talking about on social media. For example, the reception desk can suggest products that are featured by influencers the customer follows. For example, the reception desk can suggest products that are popular in communities the customer participates in. This makes it possible to suggest relevant products by analyzing the customer's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the customer's social media activity into a generating AI, which can then suggest relevant products.

[0085] The reception desk can provide coupons for relevant products based on the customer's purchase history. For example, the reception desk can provide coupons for products related to products the customer has previously purchased. For example, the reception desk can provide coupons to encourage the customer to repurchase products they have previously purchased. For example, the reception desk can provide coupons for products highly rated by other customers based on reviews of products the customer has previously purchased. This can increase the customer's willingness to purchase by providing coupons based on their purchase history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the customer's purchase history into a generating AI, and the generating AI can provide coupons for relevant products.

[0086] The acquisition unit can estimate the customer's emotions and adjust the timing of product information acquisition based on the estimated emotions. For example, if the customer is relaxed, the acquisition unit can provide product information at a slow pace. If the customer is in a hurry, the acquisition unit can provide product information quickly. If the customer is excited, the acquisition unit can provide visually stimulating product information. By adjusting the timing of product information acquisition according to the customer's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input customer emotion data into the generative AI, which can estimate the emotions and adjust the timing of product information acquisition.

[0087] The acquisition unit can monitor the store's inventory status in real time and acquire optimal product information. For example, the acquisition unit can prioritize acquiring products with low stock and propose them to customers. For example, the acquisition unit can prioritize acquiring products with high stock and propose them to customers as sale information. For example, the acquisition unit can prioritize acquiring newly arrived products and propose them to customers. This allows the system to provide the latest information by monitoring the store's inventory status in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input store inventory data into a generating AI, which can monitor the inventory status in real time and acquire optimal product information.

[0088] The acquisition unit can prioritize the acquisition of inventory information for related products based on the customer's purchase history. For example, the acquisition unit can prioritize the acquisition of inventory information for products related to products the customer has previously purchased. For example, the acquisition unit can prioritize the acquisition of inventory information for complementary products to products the customer has previously purchased. For example, the acquisition unit can prioritize the acquisition of inventory information for products highly rated by other customers based on reviews of products the customer has previously purchased. This makes it possible to provide more appropriate information by acquiring inventory information based on the customer's purchase history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the customer's purchase history into a generating AI, and the generating AI can prioritize the acquisition of inventory information for related products.

[0089] The acquisition unit can estimate the customer's emotions and determine the priority of products to acquire based on the estimated customer emotions. For example, if the customer is relaxed, the acquisition unit will prioritize acquiring inventory information for products with a relaxing effect. For example, if the customer is excited, the acquisition unit can prioritize acquiring inventory information for highly entertaining products. For example, if the customer is tired, the acquisition unit can prioritize acquiring inventory information for refreshing products. This makes it possible to provide more appropriate information by determining the priority of products according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input customer emotion data into the generative AI, which can estimate the emotions and determine the priority of products.

[0090] The acquisition unit can acquire highly relevant product information by considering the geographical location information of the store. For example, the acquisition unit can prioritize acquiring information on popular products near the store. For example, the acquisition unit can prioritize acquiring information on products in high demand in the area surrounding the store. For example, the acquisition unit can prioritize acquiring product information that matches the store's location conditions. This makes it possible to provide more appropriate information by considering the geographical location information of the store. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the geographical location information of the store into a generating AI, and the generating AI can acquire highly relevant product information.

[0091] The acquisition unit can analyze a customer's social media activity and acquire inventory information for related products. For example, the acquisition unit can acquire inventory information for products that the customer is talking about on social media. For example, the acquisition unit can acquire inventory information for products that are featured by influencers that the customer follows. For example, the acquisition unit can acquire inventory information for products that are popular in communities that the customer participates in. In this way, inventory information for related products can be acquired by analyzing the customer's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the customer's social media activity into a generating AI, and the generating AI can acquire inventory information for related products.

[0092] The guidance unit can estimate the customer's emotions and adjust the shopping route guidance method based on the estimated emotions. For example, if the customer is relaxed, the guidance unit will guide them at a leisurely pace. If the customer is in a hurry, the guidance unit can emphasize the shortest route. If the customer is excited, the guidance unit can guide them with a visually stimulating shopping route. This allows for more appropriate guidance by adjusting the shopping route guidance method according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI or not using AI. For example, the guidance unit can input customer emotion data into a generative AI, which can estimate the emotion and adjust the shopping route guidance method.

[0093] The guidance system can guide customers along the optimal shopping path based on their past purchase history. For example, the guidance system can prioritize guiding customers to sections of products related to products they have previously purchased. For example, the guidance system can prioritize guiding customers to sections of complementary products to products they have previously purchased. For example, the guidance system can prioritize guiding customers to sections of products highly rated by other customers based on reviews of products they have previously purchased. This allows for more appropriate guidance by guiding customers along the shopping path based on their past purchase history. Some or all of the above processing in the guidance system may be performed using AI, for example, or without AI. For example, the guidance system can input the customer's past purchase history into a generating AI, which can then guide customers along the optimal shopping path.

[0094] The guidance unit can monitor the store's congestion level in real time and guide customers along the optimal shopping route. For example, the guidance unit can guide customers along a route that avoids crowded areas. For example, the guidance unit can guide customers along a route that prioritizes less crowded areas. For example, the guidance unit can adjust the optimal shopping route in real time according to the congestion level. This allows for more appropriate shopping routes to be guided by monitoring the store's congestion level in real time. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input store congestion data into a generating AI, which can monitor the data in real time and guide customers along the optimal shopping route.

[0095] The guidance system can estimate the customer's emotions and prioritize shopping routes based on those emotions. For example, if a customer is relaxed, the guidance system will prioritize shopping routes that promote relaxation. If a customer is excited, the guidance system can prioritize shopping routes that offer entertainment. If a customer is tired, the guidance system can prioritize shopping routes that promote refreshment. By prioritizing shopping routes according to the customer's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance system may be performed using AI or not. For example, the guidance system can input customer emotion data into a generative AI, which can estimate the emotion and determine the priority of shopping routes.

[0096] The guidance system can guide customers along the optimal shopping route by considering their geographical location. For example, if a customer is at the entrance of a store, the guidance system can prioritize guiding them to popular or sale items. If a customer is in a specific area within the store, the guidance system can prioritize guiding them to products related to that area. If a customer is near the checkout counter, the guidance system can prioritize guiding them to additional products to encourage purchases. This allows for a more appropriate shopping route by considering the customer's geographical location. Some or all of the above processing in the guidance system may be performed using AI, for example, or without AI. For example, the guidance system can input the customer's geographical location information into a generating AI, which can then guide the customer along the optimal shopping route.

[0097] The guidance unit can analyze a customer's social media activity and guide them through relevant shopping routes. For example, the guidance unit can guide customers to the sales floor of products they are talking about on social media. For example, the guidance unit can guide customers to the sales floor of products recommended by influencers they follow. For example, the guidance unit can prioritize guiding customers to products popular in communities they participate in. In this way, by analyzing a customer's social media activity, relevant shopping routes can be guided. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input a customer's social media activity into a generating AI, which can then guide them through relevant shopping routes.

[0098] The generation unit can estimate the customer's emotions and adjust the method of generating the commercial based on the estimated emotions. For example, if the customer is relaxed, the generation unit can generate a commercial that proceeds at a leisurely pace. For example, if the customer is in a hurry, the generation unit can generate a commercial that emphasizes the shortest route. For example, if the customer is excited, the generation unit can generate a commercial with visually stimulating effects. In this way, by adjusting the method of generating the commercial according to the customer's emotions, more appropriate commercials can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input customer emotion data into the generation AI, which can estimate the emotions and adjust the method of generating the commercial.

[0099] The generation unit can generate commercials (CMs) for optimally recommended products based on the customer's past purchase history. For example, the generation unit can generate CMs for products related to products the customer has previously purchased. For example, the generation unit can generate CMs for complementary products to products the customer has previously purchased. For example, the generation unit can generate CMs for products highly rated by other customers based on reviews of products the customer has previously purchased. This allows for the provision of more appropriate recommended product CMs by generating CMs based on the customer's past purchase history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's past purchase history into a generation AI, which can then generate CMs for optimally recommended products.

[0100] The generation unit can generate commercials (CMs) for relevant products based on the customer's current location information. For example, if the customer is in a specific area within a store, the generation unit can generate CMs for products related to that area. For example, if the customer is at the entrance of the store, the generation unit can generate CMs for popular or sale items. For example, if the customer is near the checkout counter, the generation unit can generate CMs for additional products to encourage purchase. This allows for the provision of more appropriate product CMs by generating them based on the customer's current location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's current location information into a generation AI, which can then generate CMs for relevant products.

[0101] The generation unit can estimate the customer's emotions and determine the priority of commercials based on the estimated emotions. For example, if the customer is relaxed, the generation unit will prioritize generating commercials for products with a relaxing effect. For example, if the customer is excited, the generation unit can prioritize generating commercials for highly entertaining products. For example, if the customer is tired, the generation unit can prioritize generating commercials for refreshing products. This allows for the provision of more appropriate commercials by prioritizing them according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input customer emotion data into the generation AI, which can estimate the emotions and determine the priority of commercials.

[0102] The generation unit can analyze a customer's social media activity and generate commercials for relevant products. For example, the generation unit can generate commercials for products that the customer is talking about on social media. For example, the generation unit can generate commercials for products that are featured by influencers the customer follows. For example, the generation unit can prioritize generating commercials for products that are popular in communities the customer participates in. In this way, by analyzing the customer's social media activity, it is possible to provide commercials for relevant products. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's social media activity into a generation AI, and the generation AI can generate commercials for relevant products.

[0103] The generation unit can generate commercials (CMs) for relevant products based on the customer's purchase history. For example, the generation unit can generate CMs for products related to products the customer has previously purchased. For example, the generation unit can generate CMs for complementary products to products the customer has previously purchased. For example, the generation unit can generate CMs for products highly rated by other customers based on reviews of products the customer has previously purchased. This allows for the provision of more appropriate product CMs by generating them based on the customer's purchase history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's purchase history into a generation AI, which can then generate CMs for relevant products.

[0104] The delivery unit can estimate the customer's emotions and adjust the way the commercials are delivered based on the estimated emotions. For example, if the customer is relaxed, the delivery unit can deliver the commercials at a relaxed pace. If the customer is in a hurry, the delivery unit can deliver the commercials quickly. If the customer is excited, the delivery unit can deliver the commercials with visually stimulating effects. By adjusting the way the commercials are delivered according to the customer's emotions, more appropriate commercials can be delivered. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input customer emotion data into a generative AI, which can estimate the emotions and adjust the way the commercials are delivered.

[0105] The service provider can provide the most suitable commercials (CMs) based on the customer's past purchase history. For example, the service provider can provide CMs for products related to products the customer has previously purchased. For example, the service provider can provide CMs for complementary products to products the customer has previously purchased. For example, the service provider can provide CMs for products highly rated by other customers based on reviews of products the customer has previously purchased. This allows for the provision of more appropriate CMs by providing them based on the customer's past purchase history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the customer's past purchase history into a generating AI, which can then provide the most suitable CMs.

[0106] The service provider can provide relevant commercials (CMs) based on the customer's current location information. For example, if the customer is in a specific area within a store, the service provider can provide commercials for products related to that area. For example, if the customer is at the entrance of the store, the service provider can provide commercials for popular or sale items. For example, if the customer is near the checkout counter, the service provider can provide commercials for additional products to encourage purchase. By providing commercials based on the customer's current location information, more appropriate commercials can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the customer's current location information into a generating AI, and the generating AI can provide relevant commercials.

[0107] The delivery unit can estimate the customer's emotions and adjust the timing of the commercial (CM) delivery based on the estimated emotions. For example, if the customer is relaxed, the delivery unit can deliver the CM at a relaxed pace. If the customer is in a hurry, the delivery unit can deliver the CM quickly. If the customer is excited, the delivery unit can deliver the CM at a visually stimulating pace. By adjusting the timing of the CM delivery according to the customer's emotions, more appropriate CMs can be delivered. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input customer emotion data into a generative AI, which can estimate the emotion and adjust the timing of the CM delivery.

[0108] The service provider can analyze a customer's social media activity and provide relevant commercials. For example, the service provider can provide commercials for products that the customer is talking about on social media. For example, the service provider can provide commercials for products that are featured by influencers the customer follows. For example, the service provider can prioritize providing commercials for products that are popular in communities the customer participates in. In this way, relevant commercials can be provided by analyzing the customer's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the customer's social media activity into a generating AI, and the generating AI can provide relevant commercials.

[0109] The service provider can provide relevant commercials (CMs) based on the customer's purchase history. For example, the service provider can provide CMs for products related to products the customer has previously purchased. For example, the service provider can provide CMs for complementary products to products the customer has previously purchased. For example, the service provider can provide CMs for products highly rated by other customers based on reviews of products the customer has previously purchased. This allows for the provision of more appropriate CMs by providing them based on the customer's purchase history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the customer's purchase history into a generating AI, and the generating AI can provide relevant CMs.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can not only understand customer needs but also analyze their past purchase history and interests to suggest the most suitable products. For example, it can suggest products related to items the customer has purchased in the past. It can also suggest complementary products to items the customer has previously purchased. Furthermore, based on reviews of products the customer has purchased in the past, it can suggest products that have received high ratings from other customers. In this way, analyzing the customer's past purchase history and interests enables more appropriate product suggestions.

[0112] The information acquisition unit not only retrieves product and inventory information in real time by linking with the store's database, but can also acquire optimal product information based on the customer's current location. For example, if a customer is in a specific area within the store, it can prioritize acquiring product information related to that area. Also, if a customer is near the store entrance, it can prioritize acquiring popular or sale items. Furthermore, if a customer is near the checkout counter, it can prioritize acquiring additional product information to encourage purchase. In this way, by acquiring product information based on the customer's current location, it becomes possible to provide more appropriate information.

[0113] The information desk not only guides customers along the optimal shopping route based on acquired information, but can also monitor the store's congestion level in real time and guide them along the optimal route. For example, it can guide customers along a route that avoids crowded areas. It can also prioritize guiding customers through less crowded areas. Furthermore, it can adjust the optimal shopping route in real time according to the congestion level. This allows for more appropriate shopping routes to be guided by monitoring the store's congestion level in real time.

[0114] The generation unit can not only generate advertisements for recommended products based on the customer's past purchase history and interests, but can also analyze the customer's social media activity and generate advertisements for related products. For example, it can generate advertisements for products that the customer is talking about on social media. It can also generate advertisements for products that are featured by influencers the customer follows. Furthermore, it can prioritize generating advertisements for products that are popular in the communities the customer participates in. In this way, by analyzing the customer's social media activity, it can provide advertisements for relevant products.

[0115] The service provider can not only deliver generated commercials to customers, but also offer coupons for related products based on the customer's purchase history. For example, it can offer coupons for products related to products the customer has previously purchased. It can also offer coupons to encourage repeat purchases of products the customer has previously bought. Furthermore, it can offer coupons for products highly rated by other customers based on the customer's reviews of products they have previously purchased. In this way, by offering coupons based on the customer's purchase history, it is possible to increase their willingness to purchase.

[0116] The reception desk can estimate the customer's emotions and adjust the timing of product recommendations based on those estimates. For example, if a customer is stressed, it can prioritize recommending products that promote relaxation. If a customer is excited, it can recommend the latest trendy products. Furthermore, if a customer is in a hurry, it can recommend products that can be purchased quickly. By adjusting the timing of product recommendations according to the customer's emotions, more appropriate product recommendations can be made.

[0117] The information acquisition unit can estimate the customer's emotions and adjust the timing of product information acquisition based on those emotions. For example, if the customer is relaxed, product information can be provided at a slow pace. If the customer is in a hurry, product information can be provided quickly. Furthermore, if the customer is excited, visually stimulating product information can be provided. By adjusting the timing of product information acquisition according to the customer's emotions, more appropriate information can be provided.

[0118] The guidance system can estimate customer emotions and adjust the shopping route guidance based on those estimates. For example, if a customer is relaxed, it can guide them at a leisurely pace. If a customer is in a hurry, it can emphasize the shortest route. Furthermore, if a customer is excited, it can guide them with a visually stimulating shopping route. By adjusting the shopping route guidance according to customer emotions, more appropriate guidance becomes possible.

[0119] The generation unit can estimate the customer's emotions and adjust the commercial generation method based on those emotions. For example, if the customer is relaxed, it can generate a commercial that progresses at a leisurely pace. If the customer is in a hurry, it can generate a commercial that emphasizes the shortest route. Furthermore, if the customer is excited, it can generate a commercial with visually stimulating effects. In this way, by adjusting the commercial generation method according to the customer's emotions, more appropriate commercials can be generated.

[0120] The delivery department can estimate the customer's emotions and adjust the way the commercials are delivered based on those estimates. For example, if the customer is relaxed, the commercials can be delivered at a leisurely pace. If the customer is in a hurry, the commercials can be delivered quickly. Furthermore, if the customer is excited, the commercials can be delivered with visually stimulating effects. By adjusting the way commercials are delivered according to the customer's emotions, more appropriate commercials can be delivered.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk understands the customer's needs. For example, by having the customer input the products they want to purchase or are interested in, the reception desk can understand the customer's needs. Step 2: The acquisition unit acquires product and inventory information from within the store based on the needs identified by the reception unit. For example, it can acquire product and inventory information in real time by linking with the store's database. Step 3: The guidance unit guides the customer to the optimal shopping route based on the information acquired by the acquisition unit. For example, it can guide the customer to the optimal shopping route based on the acquired information. Step 4: The generation unit generates commercials for recommended products based on the information provided by the guidance unit. For example, it can generate commercials for recommended products based on the customer's past purchase history and interests. Step 5: The supply unit provides the CM generated by the generation unit to the customer. For example, the generated CM can be provided to the customer.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, acquisition unit, guidance unit, generation unit, and provision unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and grasps the customer's needs. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires product information and inventory information within the store. The guidance unit is implemented by the control unit 46A of the smart device 14 and guides the customer to the optimal shopping route. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates commercials for recommended products. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated commercials to the customer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, acquisition unit, guidance unit, generation unit, and provision unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and grasps the customer's needs. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires product information and inventory information within the store. The guidance unit is implemented by the control unit 46A of the smart glasses 214 and guides the customer to the optimal shopping route. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates commercials for recommended products. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated commercials to the customer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, acquisition unit, guidance unit, generation unit, and provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and grasps the customer's needs. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires product information and inventory information within the store. The guidance unit is implemented by the control unit 46A of the headset terminal 314 and guides the customer to the optimal shopping route. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates commercials for recommended products. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated commercials to the customer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, acquisition unit, guidance unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and grasps the customer's needs. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and acquires product information and inventory information within the store. The guidance unit is implemented by the control unit 46A of the robot 414 and guides the customer along the optimal shopping route. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates commercials for recommended products. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated commercials to the customer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The reception department understands customer needs, Based on the needs identified by the aforementioned reception unit, an acquisition unit acquires product information and inventory information within the store. Based on the information acquired by the aforementioned acquisition unit, a guidance unit guides the customer along the optimal shopping route. Based on the information provided by the aforementioned guidance unit, a generation unit generates a commercial for a recommended product. The system comprises a supply unit that provides the CM generated by the generation unit to the customer. A system characterized by the following features. (Note 2) The aforementioned reception unit is Customers enter the products they want to buy or are interested in. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, The system integrates with the store's database to retrieve product and inventory information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned guide section is Based on the information gathered, guide customers through the most suitable shopping path. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate commercials for recommended products based on the customer's past purchase history and interests. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the generated CM to the customer. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate customer emotions and adjust the timing of product recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the customer's past purchase history and select the most suitable product recommendation method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Based on the customer's current location, we provide optimal product recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is We estimate customer emotions and prioritize the products we suggest based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is We analyze customers' social media activity and suggest relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Based on the customer's purchase history, we offer coupons for relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, We estimate customer emotions and adjust the timing of product information acquisition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The acquisition unit is, We monitor store inventory in real time and obtain the most suitable product information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, Based on the customer's purchase history, inventory information for related products is prioritized for retrieval. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, We estimate customer emotions and determine the priority of products to acquire based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, Take into account the geographical location of the store to retrieve highly relevant product information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The acquisition unit is, Analyze customer social media activity and retrieve inventory information for related products. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned guide section is The system estimates customer emotions and adjusts the shopping guide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide section is Based on the customer's past purchase history, we guide them through the optimal shopping path. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide section is The system monitors store congestion in real time and guides customers along the optimal shopping route. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide section is The system estimates customer emotions and prioritizes shopping paths based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide section is We guide customers along the optimal shopping route, taking their geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide section is Analyze customers' social media activity and guide them through relevant shopping pathways. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is We estimate customer emotions and adjust the way we generate commercials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is Based on the customer's past purchase history, we generate commercials for the most suitable recommended products. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is Based on the customer's current location, generate commercials for relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is We estimate customer emotions and prioritize commercials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is Analyze customer social media activity and generate commercials for relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is Generate commercials for relevant products based on the customer's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate customer emotions and adjust the way we deliver commercials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, We provide the most suitable commercials based on the customer's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, Based on the customer's current location, relevant commercials are provided. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, We estimate customer emotions and adjust the timing of our commercials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, Analyze customers' social media activity and deliver relevant commercials. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, Provide relevant commercials based on the customer's purchase history. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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. The reception department understands customer needs, Based on the needs identified by the aforementioned reception unit, an acquisition unit acquires product information and inventory information within the store. Based on the information acquired by the aforementioned acquisition unit, a guidance unit guides the customer along the optimal shopping route. Based on the information provided by the aforementioned guidance unit, a generation unit generates a commercial for a recommended product. The system comprises a supply unit that provides the CM generated by the generation unit to the customer. A system characterized by the following features.

2. The aforementioned reception unit is Customers enter the products they want to buy or are interested in. The system according to feature 1.

3. The acquisition unit is, The system integrates with the store's database to retrieve product and inventory information in real time. The system according to feature 1.

4. The aforementioned guide section is Based on the information gathered, guide customers through the most suitable shopping path. The system according to feature 1.

5. The generating unit is Generate commercials for recommended products based on the customer's past purchase history and interests. The system according to feature 1.

6. The aforementioned supply unit is, Provide the generated CM to the customer. The system according to feature 1.

7. The aforementioned reception unit is We estimate customer emotions and adjust the timing of product recommendations based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the customer's past purchase history and select the most suitable product recommendation method. The system according to feature 1.

9. The aforementioned reception unit is Based on the customer's current location, we provide optimal product recommendations. The system according to feature 1.

10. The aforementioned reception unit is We estimate customer emotions and prioritize the products we suggest based on those estimated emotions. The system according to feature 1.

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