system

Abstract

The system according to this embodiment aims to efficiently manage inventory, forecast demand, and handle customer inquiries. [Solution] The system according to the embodiment comprises a confirmation unit, a notification unit, a forecasting unit, a creation unit, and a guidance unit. The confirmation unit confirms the inventory status. The notification unit notifies of stock shortages based on the inventory status confirmed by the confirmation unit. The forecasting unit forecasts demand based on sales data, seasonal factors, and trend analysis. The creation unit creates shelf layouts based on the forecasts obtained by the forecasting unit. The guidance unit guides customers to the location of products in response to vague questions.

Description

Technical Field

[0003]

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

Background Art

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

[0007] The system according to this embodiment can efficiently perform inventory management, demand forecasting, and customer service. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The retail industry support system according to an embodiment of the present invention is a system that utilizes generative AI to address the challenges facing the modern retail industry and strengthen its competitiveness. This retail industry support system, as an in-store operation, uses patrol devices and mobile terminals to check inventory status and stockouts and provides immediate notification. Next, as a desk operation, it automates shelf layout creation, performs demand forecasting based on sales data, seasonal factors, and trend analysis, and assists with automatic ordering. Furthermore, in response to product inquiries, it guides customers to the exact location of products, even for vague questions. For example, as an in-store operation, the retail industry support system uses patrol devices and mobile terminals to check inventory status and stockouts. In this case, the patrol device patrols the store and acquires images of the shelves. Staff use mobile terminals to take pictures of the shelves. These images are input into the generative AI, which uses image recognition technology to analyze inventory status and stockouts. For example, if there is no product on the shelf, the generative AI detects the stockout and provides immediate notification. Next, as a desk operation, the retail industry support system automates shelf layout creation. The generative AI performs demand forecasting based on sales data, seasonal factors, and trend analysis and proposes the optimal shelf layout. For example, if a particular product experiences seasonally high demand, the generating AI will suggest shelf placement to ensure that product is prominently displayed. The generating AI also assists with automated ordering, automatically reordering items when inventory levels are low. Furthermore, the retail industry support system guides customers to the exact location of products, even in response to vague inquiries. For instance, if a customer asks, "Where are the red shirts?", the generating AI will use a store map to locate the product and direct the customer. This system improves inventory management efficiency, optimizes shelf placement, enhances customer service, and strengthens the competitiveness of the retail industry. Thus, the retail industry support system can address the challenges facing the modern retail sector and enhance its competitiveness.

[0029] The retail industry support system according to this embodiment comprises a confirmation unit, a notification unit, a forecasting unit, a creation unit, and a guidance unit. The confirmation unit confirms the inventory status. The confirmation unit acquires images of shelves using, for example, a patrol device or a mobile terminal. The patrol device includes, for example, a robot or a drone. The mobile terminal includes, for example, a smartphone or a tablet. The confirmation unit inputs the acquired images into a generation AI, which analyzes the inventory status using image recognition technology. For example, if there are no products on the shelves, the generation AI detects the stockout and immediately notifies the customer. The notification unit notifies the customer of the stockout based on the inventory status confirmed by the confirmation unit. The notification unit provides notification by means such as email or an alert. The notification unit can use the generation AI to provide notifications of stockouts. The forecasting unit forecasts demand based on sales data, seasonal factors, and trend analysis. The forecasting unit uses, for example, sales data such as past sales history, sales amount, and sales quantity. Seasonal factors include, for example, seasonal demand fluctuations, specific events, and holidays. Trend analysis includes, for example, trend analysis from past data and referencing external data. The forecasting unit can perform demand forecasting using generative AI. The creation unit creates shelf layouts based on the forecasts obtained by the forecasting unit. The creation unit creates shelf layouts considering, for example, product placement criteria and shelf layout update frequency. The creation unit can propose the optimal shelf layout using generative AI. The guidance unit guides customers to the location of products in response to vague questions. For example, if a customer asks, "Where are the red shirts?", the generative AI will identify the location of the product based on a map of the store and guide the customer. The guidance unit can accurately guide customers even in response to vague questions using generative AI. As a result, the retail industry support system according to this embodiment improves the efficiency of inventory management, optimizes shelf layouts, and enhances customer service.

[0030] The verification unit checks the inventory status. The verification unit acquires images of shelves using, for example, patrol devices or mobile terminals. Patrol devices include, for example, robots or drones. These patrol devices are programmed to automatically patrol the store and periodically acquire images of shelves. Robots move along the floor, stopping in front of shelves and taking high-resolution images using cameras. Drones can fly through the air and acquire images of the tops of shelves or places that are out of reach. Mobile terminals include, for example, smartphones or tablets. Store staff use these mobile terminals to manually take images of shelves and send them to the verification unit. The verification unit inputs the acquired images into a generating AI, which analyzes the inventory status using image recognition technology. The generating AI automatically identifies the presence and quantity of products in the images and grasps the inventory status in real time. For example, if there are no products on a shelf, the generating AI detects the shortage and notifies the verification unit immediately. The generating AI reads the product labels or barcodes in the images to determine whether a particular product is out of stock. This allows the verification unit to efficiently and accurately check inventory status, enabling early detection and response to stockouts. Furthermore, the verification unit can save acquired image data and manage the history of past inventory status. This can be used to analyze inventory management trends and identify the root causes of problems.

[0031] The notification unit notifies of stock shortages based on the inventory status confirmed by the verification unit. The notification unit sends notifications via means such as email or alerts. The notification unit can use a generation AI to send stock shortage notifications. Specifically, the generation AI analyzes the inventory status data received from the verification unit and immediately generates a notification when a stock shortage occurs. The notification unit sends stock shortage information to store staff and managers via means such as email, SMS, or app push notifications. This allows store staff to respond quickly and replenish or reorder the out-of-stock items. The notification unit can also customize the content of notifications, for example, by including detailed information about the out-of-stock items, replenishment priority, and recommended replenishment methods. Furthermore, the notification unit can manage the notification history and record past notification content and response status. This allows the notification unit to support the efficiency and tracking of stock shortage responses, improving the accuracy and reliability of inventory management.

[0032] The forecasting unit performs demand forecasting based on sales data, seasonal factors, and trend analysis. The forecasting unit uses sales data such as past sales history, sales figures, and sales quantities. This data is obtained from store POS systems and sales management systems and input into the forecasting unit. Seasonal factors include, for example, seasonal demand fluctuations, specific events, and holidays. The forecasting unit considers these seasonal factors to predict demand peaks and dips. Trend analysis includes, for example, trend analysis from historical data and referencing external data. The forecasting unit can also perform demand forecasting using generative AI. Generative AI analyzes historical sales data, seasonal factors, and trend data to build models for predicting future demand. For example, based on historical sales data, generative AI can predict when and how much of a particular product will sell and suggest the optimal timing for inventory replenishment. Furthermore, generative AI can refer to external data, such as weather information, economic indicators, and consumer purchasing trends, to improve the accuracy of demand forecasting. This allows the forecasting unit to provide accurate and reliable demand forecasts, supporting optimized inventory management and maximizing sales opportunities.

[0033] The creation unit creates shelf layouts based on predictions obtained by the forecasting unit. The creation unit creates shelf layouts considering factors such as product placement criteria and shelf layout update frequency. The creation unit can propose optimal shelf layouts using a generation AI. The generation AI executes algorithms to optimize product placement and quantity based on demand forecast data provided by the forecasting unit. For example, the generation AI maximizes sales opportunities by placing high-demand products in easily visible locations and low-demand products at the bottom or back of the shelves. The generation AI can also propose shelf layout update timings considering product replenishment frequency and turnover rate. This enables the creation unit to achieve efficient and effective shelf layouts, supporting optimized inventory management and sales promotion. Furthermore, the creation unit can perform shelf layout simulations and compare the effects of different placement patterns. This allows for the selection of the optimal shelf layout and its implementation in actual store operations.

[0034] The information desk guides customers to the location of products in response to their vague questions. For example, if a customer asks, "Where are the red shirts?", the information desk uses a generative AI to identify the product's location based on a store map and guide the customer. The generative AI uses natural language processing technology to analyze the customer's question and understand its intent. For example, it extracts the keyword "red shirt" and identifies which area of ​​the store the product is located in. The generative AI refers to a store map database to search for the location of the relevant product. Based on the search results, the information desk provides specific guidance to the customer. For example, it can display a map showing the product's location via a smartphone app and guide the customer with voice guidance and vibration notifications. The information desk can also provide visual guidance to customers using in-store digital signage and information displays. This allows the information desk to respond to customer questions quickly and accurately, improving customer satisfaction and the shopping experience. Furthermore, the information desk can record the customer's question history and use it to improve future services and develop marketing strategies.

[0035] The verification unit acquires images of shelves using a patrol device or a mobile terminal. The verification unit, for example, patrols the store using a patrol device and acquires images of shelves. The patrol device includes, for example, a robot or a drone. The verification unit takes pictures of shelves using, for example, a mobile terminal. The mobile terminal includes, for example, a smartphone or a tablet. The verification unit inputs the acquired images into a generating AI, which analyzes the inventory status using image recognition technology. For example, if there are no products on a shelf, the generating AI detects the shortage and notifies the verification unit immediately. This makes it easy to check the inventory status by acquiring images of the shelves. Some or all of the above processing in the verification unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the verification unit can input the acquired images into the generating AI and analyze the inventory status using image recognition technology.

[0036] The notification unit detects shortages based on images acquired by the verification unit and immediately notifies the customer. For example, the notification unit inputs images acquired by the verification unit into a generation AI, which uses image recognition technology to detect shortages. If the notification unit detects a shortage, it immediately sends a notification. The notification unit sends notifications by means such as email or alerts. The notification unit can use the generation AI to send notifications of shortages. This enables a quick response by immediately notifying the customer of shortages. Some or all of the above-described processes in the notification unit may be performed using the generation AI or not. For example, the notification unit can input images acquired by the verification unit into a generation AI, which uses image recognition technology to detect shortages and immediately sends a notification.

[0037] The forecasting unit performs demand forecasting based on sales data, seasonal factors, and trend analysis. The forecasting unit uses sales data such as past sales history, sales figures, and sales quantities. Seasonal factors include seasonal demand fluctuations, specific events, and holidays. Trend analysis includes trend analysis from historical data and referencing external data. The forecasting unit can perform demand forecasting using generative AI. This enables appropriate inventory management through demand forecasting. Some or all of the above-described processes in the forecasting unit may be performed using generative AI, or not. For example, the forecasting unit can perform demand forecasting based on sales data, seasonal factors, and trend analysis, and then use generative AI to make the optimal forecast.

[0038] The creation unit proposes the optimal shelf layout based on the predictions obtained by the prediction unit. The creation unit creates the shelf layout considering, for example, product placement criteria and shelf layout update frequency. The creation unit can propose the optimal shelf layout using a generation AI. This makes product placement more efficient by proposing the optimal shelf layout. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can create a shelf layout based on the predictions obtained by the prediction unit and propose the optimal shelf layout using a generation AI.

[0039] The information desk identifies and guides customers to the location of products in response to their vague questions. For example, if a customer asks, "Where are the red shirts?", the information desk uses a generative AI to identify the product's location based on a map of the store and guide the customer. The information desk can use generative AI to accurately guide customers even in response to their vague questions. This improves customer service by providing accurate guidance even to vague questions. Some or all of the above-described processes in the information desk may be performed using generative AI or not. For example, the information desk can use generative AI to identify and guide customers to the location of products in response to their vague questions.

[0040] The verification unit dynamically changes the resolution of the images it acquires according to the type and importance of the product. For example, for expensive products, the verification unit acquires high-resolution images for detailed verification. For everyday products, the verification unit acquires standard-resolution images for efficient verification. For seasonal or limited-edition products, the verification unit acquires particularly high-resolution images to rigorously check inventory status. This allows for efficient inventory verification by changing the image resolution according to the type and importance of the product. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit can input the image resolution according to the type and importance of the product to the generation AI, which can then dynamically change it.

[0041] The verification unit improves the accuracy of inventory status by taking images from different angles. For example, the verification unit takes images from the front and side of a shelf to accurately confirm the placement of products. For example, the verification unit takes images from above to check the inventory status of products on the upper shelves. For example, the verification unit takes images from below the shelf to check the inventory status of products in hard-to-see locations. By taking images from different angles, the accuracy of inventory status is improved. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit can input images taken from different angles into a generation AI, which can then analyze the inventory status.

[0042] The verification unit analyzes inventory status by adding environmental information such as temperature and humidity to the acquired images. For example, the verification unit adds temperature information to check the inventory status of products that require temperature control. For example, the verification unit adds humidity information to check the inventory status of products that require humidity control. For example, the verification unit comprehensively analyzes the environmental information to perform inventory management to maintain product quality. In this way, by adding environmental information, inventory management that maintains product quality becomes possible. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit can input environmental information such as temperature and humidity to the generation AI, and the generation AI can analyze the inventory status.

[0043] The verification unit adds a function to automatically read product barcodes or 2D codes (e.g., QR code®) from the acquired image. The verification unit, for example, automatically reads barcodes in the image and obtains product inventory information. The verification unit, for example, automatically reads 2D codes in the image and obtains detailed product information. The verification unit, for example, updates the inventory status in real time by linking with the inventory management system by reading barcodes or 2D codes. This allows the system to update the inventory status in real time by linking with the inventory management system by reading barcodes or 2D codes. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit can input barcodes or 2D codes contained in the acquired image into the generation AI, which can then automatically read them.

[0044] The notification unit allows the user to select different notification methods, such as voice or vibration, when issuing a notification. For example, the notification unit can select voice notification and inform the user of the inventory status by voice. For example, the notification unit can select vibration notification and inform the user of the inventory status by vibration. For example, the notification unit can select visual notification and inform the user of the inventory status by displaying it on the screen. This allows for notifications tailored to the user's situation by allowing the user to select different notification methods. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, when issuing a notification, the notification unit can input different notification methods, such as voice or vibration, into the generation AI, which can then select one.

[0045] The notification unit includes alternatives for out-of-stock items in its notifications. For example, the notification unit suggests alternative products for out-of-stock items and notifies the user. For example, the notification unit notifies the user of the expected restock date for out-of-stock items. For example, the notification unit notifies the user of the stock status at other stores as an alternative for out-of-stock items. This improves user convenience by including alternatives for out-of-stock items. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input alternatives for out-of-stock items into the notification content and the generation AI can make suggestions.

[0046] The notification unit is designed to display notification content in multiple languages ​​when it sends a notification. For example, the notification unit can display the notification content in English and Japanese and inform the user. For example, the notification unit can display the notification content in Chinese and Korean and inform the user. For example, the notification unit can display the notification content in French and Spanish and inform the user. This allows the system to accommodate users who speak different languages ​​by displaying the notification content in multiple languages. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input the content to be notified into a generation AI, which can then display it in multiple languages.

[0047] The notification unit includes historical inventory data relevant to the notification content when it sends a notification. For example, the notification unit displays historical inventory data to inform the user of inventory trends. For example, the notification unit notifies the user of future inventory forecasts based on historical inventory data. For example, the notification unit analyzes historical inventory data and suggests improvements to inventory management. Thus, by including historical inventory data, it is possible to suggest improvements to inventory management. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input historical inventory data relevant to the content to be notified into a generation AI, which can then analyze and reflect the data in the notification content.

[0048] The forecasting unit, when making demand forecasts, refers not only to historical sales data but also to external market data. For example, the forecasting unit combines historical sales data and external market data to make demand forecasts. For example, the forecasting unit acquires external market data in real time and reflects it in the demand forecast. For example, the forecasting unit analyzes external market data to improve the accuracy of the demand forecast. Thus, the accuracy of the demand forecast is improved by referring to external market data. Some or all of the above processing in the forecasting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the forecasting unit can input historical sales data and external market data into a generation AI, and the generation AI can perform demand forecasts.

[0049] The forecasting unit considers the impact of specific events and campaigns when making demand forecasts. For example, the forecasting unit considers the impact of a specific event and makes a demand forecast. For example, the forecasting unit considers the impact of a campaign and makes a demand forecast. For example, the forecasting unit analyzes event and campaign data and reflects it in the demand forecast. This improves the accuracy of the demand forecast by considering the impact of events and campaigns. Some or all of the above processing in the forecasting unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the forecasting unit can input data for a specific event or campaign into a generative AI, and the generative AI can perform the demand forecast.

[0050] The forecasting unit considers regional consumption trends when forecasting demand. For example, the forecasting unit analyzes regional consumption trends and reflects them in the demand forecast. For example, the forecasting unit acquires regional consumption data and uses it in the demand forecast. For example, the forecasting unit makes the optimal demand forecast by considering regional consumption trends. This improves the accuracy of the demand forecast by considering regional consumption trends. Some or all of the above processing in the forecasting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the forecasting unit can input regional consumption trends into a generation AI, and the generation AI can perform the demand forecast.

[0051] The forecasting unit considers the product lifecycle when making demand forecasts. For example, the forecasting unit analyzes the product lifecycle and reflects it in the demand forecast. For example, the forecasting unit acquires product lifecycle data and uses it in the demand forecast. For example, the forecasting unit makes the optimal demand forecast by considering the product lifecycle. This improves the accuracy of the demand forecast by considering the product lifecycle. Some or all of the above processes in the forecasting unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the forecasting unit can input the product lifecycle into a generative AI, and the generative AI can perform the demand forecast.

[0052] The creation unit considers the size and shape of the products when creating shelf layouts. For example, for large products, the creation unit creates a shelf layout that ensures ample space. For example, for small products, the creation unit creates a shelf layout that allows for efficient placement. For example, for products with unusual shapes, the creation unit proposes a special shelf layout. This allows for efficient shelf layouts by considering the size and shape of the products. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input the size and shape of the products into a generation AI, which can then create the shelf layout.

[0053] The creation unit considers the sales history of products when creating shelf layouts. For example, if a product has a good sales history, the creation unit proposes a shelf layout that places it in a prominent location. For example, if a product has a poor sales history, the creation unit proposes a shelf layout with a changed placement. For example, the creation unit analyzes the sales history and proposes the optimal shelf layout. This makes efficient shelf layout possible by considering the sales history of products. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input the sales history of products into a generation AI, and the generation AI can create the shelf layout.

[0054] The creation unit considers the color and design of the products when creating shelf layouts. For example, for brightly colored products, the creation unit proposes a shelf layout that places them in a prominent location. For example, for products with distinctive designs, the creation unit proposes a special placement. For example, the creation unit proposes a shelf layout that takes color and design into consideration and maintains overall balance. This makes efficient shelf layout possible by considering the color and design of the products. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input the color and design of the products into a generation AI, and the generation AI can create the shelf layout.

[0055] The creation unit considers the brand and manufacturer of the products when creating shelf layouts. For example, the creation unit proposes a shelf layout that places popular brand products in prominent locations. For example, the creation unit proposes a shelf layout that groups products from a specific manufacturer together. For example, the creation unit proposes an optimal shelf layout that takes into account the characteristics of the brand and manufacturer. This makes it possible to create efficient shelf layouts by considering the brand and manufacturer of the products. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input the brand and manufacturer of the products into a generation AI, and the generation AI can create the shelf layout.

[0056] The guidance system considers the store's congestion level when guiding customers to the location of products. For example, the guidance system avoids crowded areas. For example, the guidance system monitors congestion levels in real time and guides customers along the optimal route. For example, the guidance system considers times when congestion is expected. This allows for efficient guidance by considering the store's congestion level. Some or all of the above processes in the guidance system may be performed using a generation AI, or they may be performed without a generation AI. For example, the guidance system can input the store's congestion level into a generation AI, which can then guide customers along the optimal route.

[0057] The information desk reflects the product's inventory status in real time when guiding users to the location of a product. For example, if a product has low stock, the information desk will guide users to the location of other products that are in stock. For example, the information desk will update the inventory status in real time to provide optimal guidance. For example, if a product is out of stock, the information desk will guide users to the location of an alternative product. This allows for appropriate guidance by reflecting the product's inventory status in real time. Some or all of the above processing in the information desk may be performed using a generation AI, or it may be performed without a generation AI. For example, the information desk can input the product's inventory status into the generation AI, which can then reflect it in real time.

[0058] The information unit displays a 3D map of the store when guiding customers to the location of products. The information unit, for example, displays a 3D map of the store to provide visually easy-to-understand guidance. The information unit, for example, uses 3D display to guide customers to the location of products in three dimensions. The information unit, for example, displays a 3D map of the store to guide customers so that they do not get lost. As a result, by displaying a 3D map of the store, visually easy-to-understand guidance becomes possible. Some or all of the above processing in the information unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the information unit can input a map of the store into a generation AI, and the generation AI can display it in 3D.

[0059] The information display unit shows relevant product information (e.g., price, promotion information) when guiding users to the location of a product. For example, the information display unit may show price information for a product and guide the user to it. For example, the information display unit may show promotion information for a product and guide the user to it. For example, the information display unit may show information about related products and guide the user to it. This improves user convenience by displaying relevant product information. Some or all of the above processing in the information display unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the information display unit can input relevant product information into a generation AI, which can then display it.

[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 verification unit can dynamically change the resolution of the images it acquires according to the type and importance of the product. For example, for expensive products, high-resolution images can be acquired for detailed verification. For products used daily, standard-resolution images can be acquired for efficient verification. Furthermore, for seasonal or limited-edition products, particularly high-resolution images can be acquired to thoroughly check inventory status. In this way, by changing the image resolution according to the type and importance of the product, efficient inventory verification becomes possible.

[0062] The forecasting unit can refer to external market data in addition to historical sales data when making demand forecasts. For example, it can combine historical sales data with external market data to make demand forecasts. It can also acquire external market data in real time and reflect it in demand forecasts. Furthermore, it can analyze external market data to improve the accuracy of demand forecasts. In this way, the accuracy of demand forecasts is improved by referring to external market data.

[0063] The information desk can take into account the store's congestion level when guiding customers to the location of products. For example, it can guide customers while avoiding crowded areas. It can also monitor congestion levels in real time and guide customers along the optimal route. Furthermore, it can guide customers considering times when congestion is expected. In this way, by taking into account the store's congestion level, efficient guidance becomes possible.

[0064] The verification unit can analyze inventory status by adding environmental information such as temperature and humidity to the acquired images. For example, by adding temperature information, the inventory status of products requiring temperature control can be checked. Similarly, by adding humidity information, the inventory status of products requiring humidity control can be checked. Furthermore, by comprehensively analyzing the environmental information, inventory management can be performed to maintain product quality. In this way, by adding environmental information, inventory management that maintains product quality becomes possible.

[0065] The notification unit can select different notification methods, such as voice or vibration, when sending notifications. For example, by selecting voice notification, the system can inform the user of the inventory status by voice. Alternatively, by selecting vibration notification, the system can inform the user of the inventory status by vibration. Furthermore, by selecting visual notification, the system can inform the user of the inventory status through a screen display. This allows for notifications tailored to the user's situation by offering different notification methods.

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

[0067] Step 1: The verification unit checks the inventory status. The verification unit acquires images of the shelves using, for example, a patrol device or a mobile terminal. Patrol devices include, for example, robots or drones. Mobile terminals include, for example, smartphones or tablets. The verification unit inputs the acquired images into a generating AI, which analyzes the inventory status using image recognition technology. Step 2: The notification unit notifies of stock shortages based on the inventory status confirmed by the verification unit. The notification unit sends notifications via means such as email or alerts. The notification unit can use generation AI to send stock shortage notifications. Step 3: The forecasting unit performs demand forecasting based on sales data, seasonal factors, and trend analysis. The forecasting unit uses sales data such as past sales history, sales amount, and sales volume. Seasonal factors include, for example, seasonal demand fluctuations, specific events, and holidays. Trend analysis includes, for example, trend analysis from past data and referencing external data. The forecasting unit can perform demand forecasting using generative AI. Step 4: The creation unit creates the shelf layout based on the predictions obtained by the prediction unit. The creation unit creates the shelf layout considering factors such as product placement criteria and shelf layout update frequency. The creation unit can propose the optimal shelf layout using generation AI. Step 5: The information desk guides customers to the location of products in response to their vague questions. For example, if a customer asks, "Where are the red shirts?", the generating AI will use a map of the store to locate the product and guide the customer. The information desk can use the generating AI to accurately guide customers even in response to their vague questions.

[0068] (Example of form 2) The retail industry support system according to an embodiment of the present invention is a system that utilizes generative AI to address the challenges facing the modern retail industry and strengthen its competitiveness. This retail industry support system, as an in-store operation, uses patrol devices and mobile terminals to check inventory status and stockouts and provides immediate notification. Next, as a desk operation, it automates shelf layout creation, performs demand forecasting based on sales data, seasonal factors, and trend analysis, and assists with automatic ordering. Furthermore, in response to product inquiries, it guides customers to the exact location of products, even for vague questions. For example, as an in-store operation, the retail industry support system uses patrol devices and mobile terminals to check inventory status and stockouts. In this case, the patrol device patrols the store and acquires images of the shelves. Staff use mobile terminals to take pictures of the shelves. These images are input into the generative AI, which uses image recognition technology to analyze inventory status and stockouts. For example, if there is no product on the shelf, the generative AI detects the stockout and provides immediate notification. Next, as a desk operation, the retail industry support system automates shelf layout creation. The generative AI performs demand forecasting based on sales data, seasonal factors, and trend analysis and proposes the optimal shelf layout. For example, if a particular product experiences seasonally high demand, the generating AI will suggest shelf placement to ensure that product is prominently displayed. The generating AI also assists with automated ordering, automatically reordering items when inventory levels are low. Furthermore, the retail industry support system guides customers to the exact location of products, even in response to vague inquiries. For instance, if a customer asks, "Where are the red shirts?", the generating AI will use a store map to locate the product and direct the customer. This system improves inventory management efficiency, optimizes shelf placement, enhances customer service, and strengthens the competitiveness of the retail industry. Thus, the retail industry support system can address the challenges facing the modern retail sector and enhance its competitiveness.

[0069] The retail industry support system according to this embodiment comprises a confirmation unit, a notification unit, a forecasting unit, a creation unit, and a guidance unit. The confirmation unit confirms the inventory status. The confirmation unit acquires images of shelves using, for example, a patrol device or a mobile terminal. The patrol device includes, for example, a robot or a drone. The mobile terminal includes, for example, a smartphone or a tablet. The confirmation unit inputs the acquired images into a generation AI, which analyzes the inventory status using image recognition technology. For example, if there are no products on the shelves, the generation AI detects the stockout and immediately notifies the customer. The notification unit notifies the customer of the stockout based on the inventory status confirmed by the confirmation unit. The notification unit provides notification by means such as email or an alert. The notification unit can use the generation AI to provide notifications of stockouts. The forecasting unit forecasts demand based on sales data, seasonal factors, and trend analysis. The forecasting unit uses, for example, sales data such as past sales history, sales amount, and sales quantity. Seasonal factors include, for example, seasonal demand fluctuations, specific events, and holidays. Trend analysis includes, for example, trend analysis from past data and referencing external data. The forecasting unit can perform demand forecasting using generative AI. The creation unit creates shelf layouts based on the forecasts obtained by the forecasting unit. The creation unit creates shelf layouts considering, for example, product placement criteria and shelf layout update frequency. The creation unit can propose the optimal shelf layout using generative AI. The guidance unit guides customers to the location of products in response to vague questions. For example, if a customer asks, "Where are the red shirts?", the generative AI will identify the location of the product based on a map of the store and guide the customer. The guidance unit can accurately guide customers even in response to vague questions using generative AI. As a result, the retail industry support system according to this embodiment improves the efficiency of inventory management, optimizes shelf layouts, and enhances customer service.

[0070] The verification unit checks the inventory status. The verification unit acquires images of shelves using, for example, patrol devices or mobile terminals. Patrol devices include, for example, robots or drones. These patrol devices are programmed to automatically patrol the store and periodically acquire images of shelves. Robots move along the floor, stopping in front of shelves and taking high-resolution images using cameras. Drones can fly through the air and acquire images of the tops of shelves or places that are out of reach. Mobile terminals include, for example, smartphones or tablets. Store staff use these mobile terminals to manually take images of shelves and send them to the verification unit. The verification unit inputs the acquired images into a generating AI, which analyzes the inventory status using image recognition technology. The generating AI automatically identifies the presence and quantity of products in the images and grasps the inventory status in real time. For example, if there are no products on a shelf, the generating AI detects the shortage and notifies the verification unit immediately. The generating AI reads the product labels or barcodes in the images to determine whether a particular product is out of stock. This allows the verification unit to efficiently and accurately check inventory status, enabling early detection and response to stockouts. Furthermore, the verification unit can save acquired image data and manage the history of past inventory status. This can be used to analyze inventory management trends and identify the root causes of problems.

[0071] The notification unit notifies of stock shortages based on the inventory status confirmed by the verification unit. The notification unit sends notifications via means such as email or alerts. The notification unit can use a generation AI to send stock shortage notifications. Specifically, the generation AI analyzes the inventory status data received from the verification unit and immediately generates a notification when a stock shortage occurs. The notification unit sends stock shortage information to store staff and managers via means such as email, SMS, or app push notifications. This allows store staff to respond quickly and replenish or reorder the out-of-stock items. The notification unit can also customize the content of notifications, for example, by including detailed information about the out-of-stock items, replenishment priority, and recommended replenishment methods. Furthermore, the notification unit can manage the notification history and record past notification content and response status. This allows the notification unit to support the efficiency and tracking of stock shortage responses, improving the accuracy and reliability of inventory management.

[0072] The forecasting unit performs demand forecasting based on sales data, seasonal factors, and trend analysis. The forecasting unit uses sales data such as past sales history, sales figures, and sales quantities. This data is obtained from store POS systems and sales management systems and input into the forecasting unit. Seasonal factors include, for example, seasonal demand fluctuations, specific events, and holidays. The forecasting unit considers these seasonal factors to predict demand peaks and dips. Trend analysis includes, for example, trend analysis from historical data and referencing external data. The forecasting unit can also perform demand forecasting using generative AI. Generative AI analyzes historical sales data, seasonal factors, and trend data to build models for predicting future demand. For example, based on historical sales data, generative AI can predict when and how much of a particular product will sell and suggest the optimal timing for inventory replenishment. Furthermore, generative AI can refer to external data, such as weather information, economic indicators, and consumer purchasing trends, to improve the accuracy of demand forecasting. This allows the forecasting unit to provide accurate and reliable demand forecasts, supporting optimized inventory management and maximizing sales opportunities.

[0073] The creation unit creates shelf layouts based on predictions obtained by the forecasting unit. The creation unit creates shelf layouts considering factors such as product placement criteria and shelf layout update frequency. The creation unit can propose optimal shelf layouts using a generation AI. The generation AI executes algorithms to optimize product placement and quantity based on demand forecast data provided by the forecasting unit. For example, the generation AI maximizes sales opportunities by placing high-demand products in easily visible locations and low-demand products at the bottom or back of the shelves. The generation AI can also propose shelf layout update timings considering product replenishment frequency and turnover rate. This enables the creation unit to achieve efficient and effective shelf layouts, supporting optimized inventory management and sales promotion. Furthermore, the creation unit can perform shelf layout simulations and compare the effects of different placement patterns. This allows for the selection of the optimal shelf layout and its implementation in actual store operations.

[0074] The information desk guides customers to the location of products in response to their vague questions. For example, if a customer asks, "Where are the red shirts?", the information desk uses a generative AI to identify the product's location based on a store map and guide the customer. The generative AI uses natural language processing technology to analyze the customer's question and understand its intent. For example, it extracts the keyword "red shirt" and identifies which area of ​​the store the product is located in. The generative AI refers to a store map database to search for the location of the relevant product. Based on the search results, the information desk provides specific guidance to the customer. For example, it can display a map showing the product's location via a smartphone app and guide the customer with voice guidance and vibration notifications. The information desk can also provide visual guidance to customers using in-store digital signage and information displays. This allows the information desk to respond to customer questions quickly and accurately, improving customer satisfaction and the shopping experience. Furthermore, the information desk can record the customer's question history and use it to improve future services and develop marketing strategies.

[0075] The verification unit acquires images of shelves using a patrol device or a mobile terminal. The verification unit, for example, patrols the store using a patrol device and acquires images of shelves. The patrol device includes, for example, a robot or a drone. The verification unit takes pictures of shelves using, for example, a mobile terminal. The mobile terminal includes, for example, a smartphone or a tablet. The verification unit inputs the acquired images into a generating AI, which analyzes the inventory status using image recognition technology. For example, if there are no products on a shelf, the generating AI detects the shortage and notifies the verification unit immediately. This makes it easy to check the inventory status by acquiring images of the shelves. Some or all of the above processing in the verification unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the verification unit can input the acquired images into the generating AI and analyze the inventory status using image recognition technology.

[0076] The notification unit detects shortages based on images acquired by the verification unit and immediately notifies the customer. For example, the notification unit inputs images acquired by the verification unit into a generation AI, which uses image recognition technology to detect shortages. If the notification unit detects a shortage, it immediately sends a notification. The notification unit sends notifications by means such as email or alerts. The notification unit can use the generation AI to send notifications of shortages. This enables a quick response by immediately notifying the customer of shortages. Some or all of the above-described processes in the notification unit may be performed using the generation AI or not. For example, the notification unit can input images acquired by the verification unit into a generation AI, which uses image recognition technology to detect shortages and immediately sends a notification.

[0077] The forecasting unit performs demand forecasting based on sales data, seasonal factors, and trend analysis. The forecasting unit uses sales data such as past sales history, sales figures, and sales quantities. Seasonal factors include seasonal demand fluctuations, specific events, and holidays. Trend analysis includes trend analysis from historical data and referencing external data. The forecasting unit can perform demand forecasting using generative AI. This enables appropriate inventory management through demand forecasting. Some or all of the above-described processes in the forecasting unit may be performed using generative AI, or not. For example, the forecasting unit can perform demand forecasting based on sales data, seasonal factors, and trend analysis, and then use generative AI to make the optimal forecast.

[0078] The creation unit proposes the optimal shelf layout based on the predictions obtained by the prediction unit. The creation unit creates the shelf layout considering, for example, product placement criteria and shelf layout update frequency. The creation unit can propose the optimal shelf layout using a generation AI. This makes product placement more efficient by proposing the optimal shelf layout. Some or all of the above processing in the creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the creation unit can create a shelf layout based on the predictions obtained by the prediction unit and propose the optimal shelf layout using a generation AI.

[0079] The information desk identifies and guides customers to the location of products in response to their vague questions. For example, if a customer asks, "Where are the red shirts?", the information desk uses a generative AI to identify the product's location based on a map of the store and guide the customer. The information desk can use generative AI to accurately guide customers even in response to their vague questions. This improves customer service by providing accurate guidance even to vague questions. Some or all of the above-described processes in the information desk may be performed using generative AI or not. For example, the information desk can use generative AI to identify and guide customers to the location of products in response to their vague questions.

[0080] The confirmation unit estimates the user's emotions and adjusts the frequency of inventory checks based on the estimated emotions. For example, if the user is stressed, the confirmation unit reduces the frequency of inventory checks and provides fewer notifications. For example, if the user is relaxed, the confirmation unit increases the frequency of inventory checks and provides more detailed notifications. For example, if the user is in a hurry, the confirmation unit prioritizes only important inventory checks. This reduces the user's burden by adjusting the frequency of inventory checks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may 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 confirmation unit may be performed using generative AI or not. For example, the confirmation unit can input the user's emotions into a generative AI, estimate the emotions using an emotion estimation algorithm, and adjust the frequency of inventory checks.

[0081] The verification unit dynamically changes the resolution of the images it acquires according to the type and importance of the product. For example, for expensive products, the verification unit acquires high-resolution images for detailed verification. For everyday products, the verification unit acquires standard-resolution images for efficient verification. For seasonal or limited-edition products, the verification unit acquires particularly high-resolution images to rigorously check inventory status. This allows for efficient inventory verification by changing the image resolution according to the type and importance of the product. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit can input the image resolution according to the type and importance of the product to the generation AI, which can then dynamically change it.

[0082] The verification unit improves the accuracy of inventory status by taking images from different angles. For example, the verification unit takes images from the front and side of a shelf to accurately confirm the placement of products. For example, the verification unit takes images from above to check the inventory status of products on the upper shelves. For example, the verification unit takes images from below the shelf to check the inventory status of products in hard-to-see locations. By taking images from different angles, the accuracy of inventory status is improved. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit can input images taken from different angles into a generation AI, which can then analyze the inventory status.

[0083] The verification unit estimates the user's emotions and determines the priority of inventory checks based on the estimated emotions. For example, if the user is stressed, the verification unit prioritizes checking the inventory of important items. For example, if the user is relaxed, the verification unit performs a general inventory check. For example, if the user is in a hurry, the verification unit prioritizes checking the inventory of items that are likely to be out of stock. In this way, by determining the priority of inventory checks according to the user's emotions, important inventory checks can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the verification unit may be performed using generative AI or not using generative AI. For example, the verification unit can input the user's emotions into a generative AI, estimate the emotions using an emotion estimation algorithm, and determine the priority of inventory checks.

[0084] The verification unit analyzes inventory status by adding environmental information such as temperature and humidity to the acquired images. For example, the verification unit adds temperature information to check the inventory status of products that require temperature control. For example, the verification unit adds humidity information to check the inventory status of products that require humidity control. For example, the verification unit comprehensively analyzes the environmental information to perform inventory management to maintain product quality. In this way, by adding environmental information, inventory management that maintains product quality becomes possible. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit can input environmental information such as temperature and humidity to the generation AI, and the generation AI can analyze the inventory status.

[0085] The verification unit adds a function to automatically read product barcodes or 2D codes (e.g., QR codes) from the acquired images. For example, the verification unit automatically reads barcodes in the image and obtains product inventory information. For example, the verification unit automatically reads 2D codes in the image and obtains detailed product information. For example, by reading barcodes or 2D codes, the verification unit links with the inventory management system and updates the inventory status in real time. This allows the system to update the inventory status in real time by linking with the inventory management system by reading barcodes or 2D codes. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit can input barcodes or 2D codes contained in the acquired image into the generation AI, which can then automatically read them.

[0086] The notification unit estimates the user's emotions and adjusts the urgency of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will only send high-urgency notifications. For example, if the user is relaxed, the notification unit will send detailed notifications. For example, if the user is in a hurry, the notification unit will prioritize important notifications. This reduces the user's burden by adjusting the urgency of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 notification unit may be performed using generative AI or not. For example, the notification unit can input the user's emotions into a generative AI, estimate the emotions using an emotion estimation algorithm, and adjust the urgency of notifications.

[0087] The notification unit allows the user to select different notification methods, such as voice or vibration, when issuing a notification. For example, the notification unit can select voice notification and inform the user of the inventory status by voice. For example, the notification unit can select vibration notification and inform the user of the inventory status by vibration. For example, the notification unit can select visual notification and inform the user of the inventory status by displaying it on the screen. This allows for notifications tailored to the user's situation by allowing the user to select different notification methods. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, when issuing a notification, the notification unit can input different notification methods, such as voice or vibration, into the generation AI, which can then select one.

[0088] The notification unit includes alternatives for out-of-stock items in its notifications. For example, the notification unit suggests alternative products for out-of-stock items and notifies the user. For example, the notification unit notifies the user of the expected restock date for out-of-stock items. For example, the notification unit notifies the user of the stock status at other stores as an alternative for out-of-stock items. This improves user convenience by including alternatives for out-of-stock items. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input alternatives for out-of-stock items into the notification content and the generation AI can make suggestions.

[0089] The notification unit estimates the user's emotions and adjusts the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit delays the notification. For example, if the user is relaxed, the notification unit speeds up the notification. For example, if the user is in a hurry, the notification unit sends an immediate notification. This reduces the user's burden by adjusting the timing of notifications according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the notification unit may be performed using a generative AI or not. For example, the notification unit can input the user's emotions into a generative AI, estimate the emotions using an emotion estimation algorithm, and adjust the timing of notifications.

[0090] The notification unit is designed to display notification content in multiple languages ​​when it sends a notification. For example, the notification unit can display the notification content in English and Japanese and inform the user. For example, the notification unit can display the notification content in Chinese and Korean and inform the user. For example, the notification unit can display the notification content in French and Spanish and inform the user. This allows the system to accommodate users who speak different languages ​​by displaying the notification content in multiple languages. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input the content to be notified into a generation AI, which can then display it in multiple languages.

[0091] The notification unit includes historical inventory data relevant to the notification content when it sends a notification. For example, the notification unit displays historical inventory data to inform the user of inventory trends. For example, the notification unit notifies the user of future inventory forecasts based on historical inventory data. For example, the notification unit analyzes historical inventory data and suggests improvements to inventory management. Thus, by including historical inventory data, it is possible to suggest improvements to inventory management. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input historical inventory data relevant to the content to be notified into a generation AI, which can then analyze and reflect the data in the notification content.

[0092] The forecasting unit estimates the user's emotions and adjusts the accuracy of the demand forecast based on the estimated emotions. For example, if the user is stressed, the forecasting unit increases the accuracy of the demand forecast. For example, if the user is relaxed, the forecasting unit sets the accuracy of the demand forecast to standard. For example, if the user is in a hurry, the forecasting unit decreases the accuracy of the demand forecast. By adjusting the accuracy of the demand forecast according to the user's emotions, appropriate demand forecasting becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the forecasting unit may be performed using a generative AI or not. For example, the forecasting unit can input the user's emotions into a generative AI, estimate the emotions using an emotion estimation algorithm, and adjust the accuracy of the demand forecast.

[0093] The forecasting unit, when making demand forecasts, refers not only to historical sales data but also to external market data. For example, the forecasting unit combines historical sales data and external market data to make demand forecasts. For example, the forecasting unit acquires external market data in real time and reflects it in the demand forecast. For example, the forecasting unit analyzes external market data to improve the accuracy of the demand forecast. Thus, the accuracy of the demand forecast is improved by referring to external market data. Some or all of the above processing in the forecasting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the forecasting unit can input historical sales data and external market data into a generation AI, and the generation AI can perform demand forecasts.

[0094] The forecasting unit considers the impact of specific events and campaigns when making demand forecasts. For example, the forecasting unit considers the impact of a specific event and makes a demand forecast. For example, the forecasting unit considers the impact of a campaign and makes a demand forecast. For example, the forecasting unit analyzes event and campaign data and reflects it in the demand forecast. This improves the accuracy of the demand forecast by considering the impact of events and campaigns. Some or all of the above processing in the forecasting unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the forecasting unit can input data for a specific event or campaign into a generative AI, and the generative AI can perform the demand forecast.

[0095] The forecasting unit estimates the user's emotions and adjusts the frequency of demand forecasts based on the estimated emotions. For example, if the user is stressed, the forecasting unit reduces the frequency of demand forecasts. For example, if the user is relaxed, the forecasting unit increases the frequency of demand forecasts. For example, if the user is in a hurry, the forecasting unit only forecasts important demand. This allows for appropriate demand forecasting by adjusting the frequency of demand forecasts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the forecasting unit may be performed using a generative AI or not. For example, the forecasting unit can input the user's emotions into a generative AI, estimate the emotions using an emotion estimation algorithm, and adjust the frequency of demand forecasts.

[0096] The forecasting unit considers regional consumption trends when forecasting demand. For example, the forecasting unit analyzes regional consumption trends and reflects them in the demand forecast. For example, the forecasting unit acquires regional consumption data and uses it in the demand forecast. For example, the forecasting unit makes the optimal demand forecast by considering regional consumption trends. This improves the accuracy of the demand forecast by considering regional consumption trends. Some or all of the above processing in the forecasting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the forecasting unit can input regional consumption trends into a generation AI, and the generation AI can perform the demand forecast.

[0097] The forecasting unit considers the product lifecycle when making demand forecasts. For example, the forecasting unit analyzes the product lifecycle and reflects it in the demand forecast. For example, the forecasting unit acquires product lifecycle data and uses it in the demand forecast. For example, the forecasting unit makes the optimal demand forecast by considering the product lifecycle. This improves the accuracy of the demand forecast by considering the product lifecycle. Some or all of the above processes in the forecasting unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the forecasting unit can input the product lifecycle into a generative AI, and the generative AI can perform the demand forecast.

[0098] The creation unit estimates the user's emotions and adjusts the shelf layout based on the estimated emotions. For example, if the user is stressed, the creation unit suggests a simple shelf layout. For example, if the user is relaxed, the creation unit suggests a detailed shelf layout. For example, if the user is in a hurry, the creation unit prioritizes the shelf layout of important products. This allows for efficient shelf layout by adjusting the shelf layout according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the creation unit may be performed using generative AI or not. For example, the creation unit can input the user's emotions into a generative AI, estimate the emotions using an emotion estimation algorithm, and adjust the shelf layout.

[0099] The creation unit considers the size and shape of the products when creating shelf layouts. For example, for large products, the creation unit creates a shelf layout that ensures ample space. For example, for small products, the creation unit creates a shelf layout that allows for efficient placement. For example, for products with unusual shapes, the creation unit proposes a special shelf layout. This allows for efficient shelf layouts by considering the size and shape of the products. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input the size and shape of the products into a generation AI, which can then create the shelf layout.

[0100] The creation unit considers the sales history of products when creating shelf layouts. For example, if a product has a good sales history, the creation unit proposes a shelf layout that places it in a prominent location. For example, if a product has a poor sales history, the creation unit proposes a shelf layout with a changed placement. For example, the creation unit analyzes the sales history and proposes the optimal shelf layout. This makes efficient shelf layout possible by considering the sales history of products. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input the sales history of products into a generation AI, and the generation AI can create the shelf layout.

[0101] The creation unit estimates the user's emotions and adjusts the frequency of shelf layout updates based on the estimated emotions. For example, if the user is stressed, the creation unit reduces the frequency of shelf layout updates. For example, if the user is relaxed, the creation unit increases the frequency of shelf layout updates. For example, if the user is in a hurry, the creation unit prioritizes updating the shelf layouts of important products. This allows for efficient shelf layout by adjusting the frequency of shelf layout updates according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may 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 creation unit may be performed using generative AI or not. For example, the creation unit can input the user's emotions into a generative AI, estimate the emotions using an emotion estimation algorithm, and adjust the frequency of shelf layout updates.

[0102] The creation unit considers the color and design of the products when creating shelf layouts. For example, for brightly colored products, the creation unit proposes a shelf layout that places them in a prominent location. For example, for products with distinctive designs, the creation unit proposes a special placement. For example, the creation unit proposes a shelf layout that takes color and design into consideration and maintains overall balance. This makes efficient shelf layout possible by considering the color and design of the products. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input the color and design of the products into a generation AI, and the generation AI can create the shelf layout.

[0103] The creation unit considers the brand and manufacturer of the products when creating shelf layouts. For example, the creation unit proposes a shelf layout that places popular brand products in prominent locations. For example, the creation unit proposes a shelf layout that groups products from a specific manufacturer together. For example, the creation unit proposes an optimal shelf layout that takes into account the characteristics of the brand and manufacturer. This makes it possible to create efficient shelf layouts by considering the brand and manufacturer of the products. Some or all of the above processes in the creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the creation unit can input the brand and manufacturer of the products into a generation AI, and the generation AI can create the shelf layout.

[0104] The guidance unit estimates the user's emotions and adjusts the way the guidance is presented based on the estimated emotions. For example, if the user is stressed, the guidance unit provides simple and easy-to-understand guidance. For example, if the user is relaxed, the guidance unit provides detailed guidance. For example, if the user is in a hurry, the guidance unit provides concise and rapid guidance. In this way, appropriate guidance is possible by adjusting the way the guidance is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the guidance unit may be performed using generative AI or not. For example, the guidance unit can input the user's emotions into a generative AI, estimate the emotions using an emotion estimation algorithm, and adjust the way the guidance is presented.

[0105] The guidance system considers the store's congestion level when guiding customers to the location of products. For example, the guidance system avoids crowded areas. For example, the guidance system monitors congestion levels in real time and guides customers along the optimal route. For example, the guidance system considers times when congestion is expected. This allows for efficient guidance by considering the store's congestion level. Some or all of the above processes in the guidance system may be performed using a generation AI, or they may be performed without a generation AI. For example, the guidance system can input the store's congestion level into a generation AI, which can then guide customers along the optimal route.

[0106] The information desk reflects the product's inventory status in real time when guiding users to the location of a product. For example, if a product has low stock, the information desk will guide users to the location of other products that are in stock. For example, the information desk will update the inventory status in real time to provide optimal guidance. For example, if a product is out of stock, the information desk will guide users to the location of an alternative product. This allows for appropriate guidance by reflecting the product's inventory status in real time. Some or all of the above processing in the information desk may be performed using a generation AI, or it may be performed without a generation AI. For example, the information desk can input the product's inventory status into the generation AI, which can then reflect it in real time.

[0107] The guidance unit estimates the user's emotions and determines the priority of guidance based on the estimated emotions. For example, if the user is stressed, the guidance unit prioritizes guidance on important products. For example, if the user is relaxed, the guidance unit provides general guidance. For example, if the user is in a hurry, the guidance unit prioritizes guidance on the shortest route. In this way, important guidance can be prioritized by determining the priority of guidance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the guidance unit may be performed using generative AI or not. For example, the guidance unit can input the user's emotions into a generative AI, estimate the emotions using an emotion estimation algorithm, and determine the priority of guidance.

[0108] The information unit displays a 3D map of the store when guiding customers to the location of products. The information unit, for example, displays a 3D map of the store to provide visually easy-to-understand guidance. The information unit, for example, uses 3D display to guide customers to the location of products in three dimensions. The information unit, for example, displays a 3D map of the store to guide customers so that they do not get lost. As a result, by displaying a 3D map of the store, visually easy-to-understand guidance becomes possible. Some or all of the above processing in the information unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the information unit can input a map of the store into a generation AI, and the generation AI can display it in 3D.

[0109] The information display unit shows relevant product information (e.g., price, promotion information) when guiding users to the location of a product. For example, the information display unit may show price information for a product and guide the user to it. For example, the information display unit may show promotion information for a product and guide the user to it. For example, the information display unit may show information about related products and guide the user to it. This improves user convenience by displaying relevant product information. Some or all of the above processing in the information display unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the information display unit can input relevant product information into a generation AI, which can then display it.

[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 confirmation unit can estimate the user's emotions and adjust the frequency of inventory checks based on those emotions. For example, if the user is stressed, the frequency of inventory checks can be reduced and notifications can be minimized. Conversely, if the user is relaxed, the frequency of inventory checks can be increased and more detailed notifications can be provided. Furthermore, if the user is in a hurry, only important inventory checks can be prioritized. In this way, the burden on the user can be reduced by adjusting the frequency of inventory checks according to their emotions.

[0112] The verification unit can dynamically change the resolution of the images it acquires according to the type and importance of the product. For example, for expensive products, high-resolution images can be acquired for detailed verification. For products used daily, standard-resolution images can be acquired for efficient verification. Furthermore, for seasonal or limited-edition products, particularly high-resolution images can be acquired to thoroughly check inventory status. In this way, by changing the image resolution according to the type and importance of the product, efficient inventory verification becomes possible.

[0113] The notification unit can estimate the user's emotions and adjust the urgency of notifications based on those emotions. For example, if the user is stressed, only high-urgency notifications can be sent. If the user is relaxed, detailed notifications can be sent. Furthermore, if the user is in a hurry, important notifications can be prioritized. In this way, the user's burden can be reduced by adjusting the urgency of notifications according to their emotions.

[0114] The forecasting unit can refer to external market data in addition to historical sales data when making demand forecasts. For example, it can combine historical sales data with external market data to make demand forecasts. It can also acquire external market data in real time and reflect it in demand forecasts. Furthermore, it can analyze external market data to improve the accuracy of demand forecasts. In this way, the accuracy of demand forecasts is improved by referring to external market data.

[0115] The creation unit can estimate the user's emotions and adjust the shelf layout based on those emotions. For example, if the user is stressed, a simple shelf layout can be suggested. If the user is relaxed, a more detailed shelf layout can be suggested. Furthermore, if the user is in a hurry, the shelf layout of important products can be prioritized. This allows for efficient shelf layout by adjusting the layout according to the user's emotions.

[0116] The information desk can take into account the store's congestion level when guiding customers to the location of products. For example, it can guide customers while avoiding crowded areas. It can also monitor congestion levels in real time and guide customers along the optimal route. Furthermore, it can guide customers considering times when congestion is expected. In this way, by taking into account the store's congestion level, efficient guidance becomes possible.

[0117] The guidance system can estimate the user's emotions and adjust the way guidance is presented based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand guidance. If the user is relaxed, it can provide detailed guidance. Furthermore, if the user is in a hurry, it can provide concise and rapid guidance. By adjusting the way guidance is presented according to the user's emotions, appropriate guidance becomes possible.

[0118] The verification unit can analyze inventory status by adding environmental information such as temperature and humidity to the acquired images. For example, by adding temperature information, the inventory status of products requiring temperature control can be checked. Similarly, by adding humidity information, the inventory status of products requiring humidity control can be checked. Furthermore, by comprehensively analyzing the environmental information, inventory management can be performed to maintain product quality. In this way, by adding environmental information, inventory management that maintains product quality becomes possible.

[0119] The notification unit can select different notification methods, such as voice or vibration, when sending notifications. For example, by selecting voice notification, the system can inform the user of the inventory status by voice. Alternatively, by selecting vibration notification, the system can inform the user of the inventory status by vibration. Furthermore, by selecting visual notification, the system can inform the user of the inventory status through a screen display. This allows for notifications tailored to the user's situation by offering different notification methods.

[0120] The forecasting unit can estimate the user's emotions and adjust the accuracy of the demand forecast based on those emotions. For example, if the user is stressed, the accuracy of the demand forecast can be increased. If the user is relaxed, the accuracy can be set to standard. Furthermore, if the user is in a hurry, the accuracy can be decreased. By adjusting the accuracy of the demand forecast according to the user's emotions, accurate demand forecasting becomes possible.

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

[0122] Step 1: The verification unit checks the inventory status. The verification unit acquires images of the shelves using, for example, a patrol device or a mobile terminal. Patrol devices include, for example, robots or drones. Mobile terminals include, for example, smartphones or tablets. The verification unit inputs the acquired images into a generating AI, which analyzes the inventory status using image recognition technology. Step 2: The notification unit notifies of stock shortages based on the inventory status confirmed by the verification unit. The notification unit sends notifications via means such as email or alerts. The notification unit can use generation AI to send stock shortage notifications. Step 3: The forecasting unit performs demand forecasting based on sales data, seasonal factors, and trend analysis. The forecasting unit uses sales data such as past sales history, sales amount, and sales volume. Seasonal factors include, for example, seasonal demand fluctuations, specific events, and holidays. Trend analysis includes, for example, trend analysis from past data and referencing external data. The forecasting unit can perform demand forecasting using generative AI. Step 4: The creation unit creates the shelf layout based on the predictions obtained by the prediction unit. The creation unit creates the shelf layout considering factors such as product placement criteria and shelf layout update frequency. The creation unit can propose the optimal shelf layout using generation AI. Step 5: The information desk guides customers to the location of products in response to their vague questions. For example, if a customer asks, "Where are the red shirts?", the generating AI will use a map of the store to locate the product and guide the customer. The information desk can use the generating AI to accurately guide customers even in response to their vague questions.

[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 confirmation unit, notification unit, forecasting unit, creation unit, and guidance unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the confirmation unit acquires images of shelves using the camera 42 of the smart device 14 or a mobile terminal, inputs them into the generating AI, and analyzes the inventory status. The notification unit detects stockouts based on the images acquired by the confirmation unit and immediately notifies the customer via the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing unit 12. The forecasting unit forecasts demand based on sales data, seasonal factors, and trend analysis, and makes the optimal forecast using the generating AI. The creation unit creates a shelf layout based on the forecast obtained by the forecasting unit and proposes the optimal shelf layout using the generating AI. The guidance unit identifies the location of products in response to vague customer questions and provides guidance via the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[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 confirmation unit, notification unit, forecasting unit, creation unit, and guidance unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the confirmation unit acquires images of shelves using the camera 42 of the smart glasses 214 or a mobile terminal, inputs them into a generating AI, and analyzes the inventory status. The notification unit detects stock shortages based on the images acquired by the confirmation unit and immediately notifies the customer via the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12. The forecasting unit forecasts demand based on sales data, seasonal factors, and trend analysis, and makes the optimal forecast using the generating AI. The creation unit creates a shelf layout based on the forecast obtained by the forecasting unit and proposes the optimal shelf layout using the generating AI. The guidance unit identifies the location of products in response to vague customer questions and provides guidance via the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[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 confirmation unit, notification unit, forecasting unit, creation unit, and guidance unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the confirmation unit acquires images of shelves using the camera 42 of the headset terminal 314 or a mobile terminal, inputs them into the generating AI, and analyzes the inventory status. The notification unit detects stockouts based on the images acquired by the confirmation unit and immediately notifies the customer via the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing unit 12. The forecasting unit forecasts demand based on sales data, seasonal factors, and trend analysis, and makes the optimal forecast using the generating AI. The creation unit creates a shelf layout based on the forecast obtained by the forecasting unit and proposes the optimal shelf layout using the generating AI. The guidance unit identifies the location of products in response to vague customer questions and provides guidance via the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[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 confirmation unit, notification unit, forecasting unit, creation unit, and guidance unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the confirmation unit acquires images of shelves using the camera 42 of the robot 414 or a mobile terminal, inputs them into a generating AI, and analyzes the inventory status. The notification unit detects stockouts based on the images acquired by the confirmation unit and immediately notifies the user via the control unit 46A of the robot 414 or the identification processing unit 290 of the data processing unit 12. The forecasting unit forecasts demand based on sales data, seasonal factors, and trend analysis, and makes the optimal forecast using the generating AI. The creation unit creates a shelf layout based on the forecast obtained by the forecasting unit and proposes the optimal shelf layout using the generating AI. The guidance unit identifies the location of products in response to vague customer questions and provides guidance via the control unit 46A of the robot 414 or the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[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) A confirmation unit to check the inventory status, A notification unit that notifies of stock shortages based on the inventory status confirmed by the aforementioned confirmation unit, The forecasting unit performs demand forecasting based on sales data, seasonal factors, and trend analysis, A creation unit that creates a shelf layout based on the prediction obtained by the prediction unit, It includes a guidance unit that directs customers to the location of products in response to their vague questions. A system characterized by the following features. (Note 2) The aforementioned verification unit is Images of shelves are acquired using patrol devices and mobile terminals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, Based on the image acquired by the aforementioned verification unit, missing items are detected and notification is sent immediately. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Demand forecasting is performed based on sales data, seasonal factors, and trend analysis. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, Based on the predictions obtained by the aforementioned prediction unit, the optimal shelf layout is proposed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned guide section is To identify and guide customers to the location of products in response to their vague questions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned verification unit is The system estimates the user's emotions and adjusts the frequency of inventory checks based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned verification unit is The resolution of the images to be acquired is dynamically changed according to the type and importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned verification unit is By taking images from different angles, the accuracy of inventory status can be improved. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned verification unit is The system estimates user sentiment and prioritizes inventory checks based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned verification unit is Environmental information such as temperature and humidity is added to the acquired images to analyze inventory status. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned verification unit is Add a function to automatically read product barcodes and QR codes from the images being acquired. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned notification unit, It estimates the user's emotions and adjusts the urgency of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, When sending notifications, you can choose from different notification methods such as sound and vibration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, The notification should include alternative options for out-of-stock items. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, When sending notifications, make it possible to display the notification content in multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, When sending notifications, include historical inventory data related to the notification content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, It estimates user sentiment and adjusts the accuracy of demand forecasts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When forecasting demand, we refer not only to historical sales data but also to external market data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When forecasting demand, consider the impact of specific events or campaigns. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, It estimates user sentiment and adjusts the frequency of demand forecasts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When forecasting demand, consider regional consumption trends. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When forecasting demand, consider the product lifecycle. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned creation unit, It estimates the user's emotions and adjusts the shelf layout based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned creation unit, When creating shelf layouts, take into account the size and shape of the products. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned creation unit, When creating shelf layouts, consider the sales history of the products. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned creation unit, The system estimates user sentiment and adjusts the frequency of shelf layout updates based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned creation unit, When creating shelf layouts, consider the color and design of the products. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned creation unit, When creating shelf layouts, consider the brand and manufacturer of the products. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned guide section is The system estimates the user's emotions and adjusts the way guidance is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned guide section is When directing customers to the location of products, we take into consideration the store's congestion level. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned guide section is When guiding users to the location of a product, the product's inventory status should be reflected in real time. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned guide section is The system estimates the user's emotions and determines the priority of guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned guide section is When guiding customers to the location of products, a 3D map of the store is displayed. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned guide section is When guiding customers to the location of a product, display related product information. 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. A confirmation unit to check the inventory status, A notification unit that notifies of stock shortages based on the inventory status confirmed by the aforementioned confirmation unit, The forecasting unit performs demand forecasting based on sales data, seasonal factors, and trend analysis, A creation unit that creates a shelf layout based on the prediction obtained by the prediction unit, It includes a guidance unit that directs customers to the location of products in response to their vague questions. A system characterized by the following features.

2. The aforementioned verification unit is Images of shelves are acquired using patrol devices and mobile terminals. The system according to feature 1.

3. The aforementioned notification unit, Based on the image acquired by the aforementioned verification unit, missing items are detected and notification is sent immediately. The system according to feature 1.

4. The prediction unit, Demand forecasting is performed based on sales data, seasonal factors, and trend analysis. The system according to feature 1.

5. The aforementioned creation unit, Based on the predictions obtained by the aforementioned prediction unit, the optimal shelf layout is proposed. The system according to feature 1.

6. The aforementioned guide section is To identify and guide customers to the location of products in response to their vague questions. The system according to feature 1.

7. The aforementioned verification unit is The system estimates the user's emotions and adjusts the frequency of inventory checks based on those emotions. The system according to feature 1.

8. The aforementioned verification unit is The resolution of the images to be acquired is dynamically changed according to the type and importance of the product. The system according to feature 1.

9. The aforementioned verification unit is By taking images from different angles, the accuracy of inventory status can be improved. The system according to feature 1.

10. The aforementioned verification unit is The system estimates user sentiment and prioritizes inventory checks based on the estimated sentiment. The system according to feature 1.

Images