System

Abstract

An object of a system according to an embodiment is to improve the efficiency of sales promotion, sales analysis, and inventory management in food retail chain stores, and to improve sales.SOLUTION: A system according to an embodiment includes a smart cart, a signage, a cross-modal-mounted signage distribution system, and a store visit data analysis platform. The smart cart provides product information and promotion information to the customer. The signage displays promotion information in the store. The cross-modal-mounted signage distribution system integrally controls a signage, a speaker, and an odor device. The store visit data analysis platform analyzes sales data and an inventory status.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, sales promotion, sales analysis, and inventory management at food retail chains relied heavily on experience and intuition, making efficient operations difficult.

[0005] The system according to the embodiment aims to improve the efficiency of sales promotion, sales analysis, and inventory management in food retail chain stores, thereby increasing sales. [Means for solving the problem]

[0006] The system according to the embodiment includes a smart cart, signage, a cross-modal signage distribution system, and a store visit data analysis platform. The smart cart provides product information and promotional information to customers. The signage displays promotional information in the store. The cross-modal signage distribution system comprehensively controls the signage, speakers, and scent devices. The store visit data analysis platform analyzes sales data and inventory status. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of sales promotion, sales analysis, and inventory management in food retail chain stores, thereby increasing sales. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The business support package according to an embodiment of the present invention is a system that supports sales promotion, sales analysis, and inventory management in a food retail chain. This system aims to increase sales by improving the number of purchases and the rate of store visits, and standardizes operations that rely on experience and intuition at each store. As a result, the business support package can increase the number of purchases and the rate of store visits by customers, thereby increasing sales. Furthermore, it can standardize operations at each store and realize efficient business management.

[0029] The business support package according to the embodiment includes a smart cart, signage, a cross-modal signage distribution system, and a store visit data analysis platform. The smart cart provides product information and promotional information to customers. For example, when a customer places a specific product in the cart, the smart cart displays the product's nutritional information and recipe suggestions on the display. The smart cart can also record customer purchase history and provide personalized promotions. The signage displays promotional information in the store. For example, the signage displays promotional videos for new products using a digital display. The signage also has interactive functions, allowing customers to search for product information using a touch panel. The cross-modal signage distribution system comprehensively controls the signage, speakers, and scent devices. For example, the cross-modal signage distribution system attracts customer attention by simultaneously distributing promotional videos, audio, and scents at specific times of the day. The store visit data analysis platform analyzes sales data and inventory status. For example, the store visit data analysis platform analyzes customer purchase histories to identify the time periods and days of the week when specific products sell well, and then develops promotion strategies based on that information. This allows the business support package according to the embodiment to increase the number of purchases and store visit rates of customers, thereby increasing sales. It also allows for the standardization of operations at each store, realizing efficient business management.

[0030] Equipped with generative AI, the smart cart can make individually customized recipe and product suggestions based on a customer's purchasing history and behavioral data. For example, a smart cart equipped with generative AI can suggest related recipes and new products based on data on products a customer has purchased in the past. For example, it can suggest weekly recipes based on ingredients that a customer frequently purchases. The smart cart also analyzes real-time behavioral data and displays promotions related to products that a customer has added to their cart. For example, if a customer adds pasta to their cart, it will display discount information on pasta sauce. The smart cart also makes product suggestions tailored to specific seasons and events based on the customer's purchasing history. For example, during the Christmas season, it will suggest Christmas dinner recipes and related products. This enables more personalized suggestions to be made to customers.

[0031] Smart carts can be equipped with a voice recognition function, allowing customers to search for product information by voice or indicate which products to add to their cart. Smart carts, for example, are equipped with a voice recognition function, allowing customers to search for product information by voice. For example, if they say, "Tell me the nutritional information for tomato sauce," the information will be displayed on the display. Smart carts also allow customers to indicate which products to add to their cart by voice. For example, if they say, "Add milk to my cart," the product will be added to the cart. Smart carts also use a voice recognition function to allow customers to search for promotional information by voice. For example, if they say, "Tell me what's on sale this week," the sale information will be displayed on the display. This allows customers to search for product information or add products to their cart by voice.

[0032] Smart carts are equipped with AR functionality, and when a customer scans a product through the cart, detailed product information and promotions can be displayed in AR. For example, when a customer scans a product, a smart cart is equipped with AR functionality, and detailed product information is displayed on the display in AR. For example, the product's ingredients and usage instructions are displayed in a 3D model. When a customer scans a product through the cart, a smart cart also displays promotional information in AR. For example, product discount information and related product suggestions are displayed in AR. When a customer scans a product through the smart cart, the AR function also displays product reviews and ratings on the display. For example, reviews and star ratings from other customers are displayed in AR. This allows customers to check detailed product information and promotions through AR.

[0033] Using generative AI, signage can generate and display individually customized promotional videos in real time based on customer purchase history and behavioral data. For example, signage equipped with generative AI can generate individually customized promotional videos based on a customer's purchase history. For example, it can display a promotional video for new products related to products the customer previously purchased. Signage can also analyze in-store behavioral data and display promotional videos for products that the customer may be interested in. For example, if a customer spends a long time in a particular area, it can display a video introducing products in that area. Signage can also combine customer purchase history and behavioral data to generate promotional videos tailored to specific seasons or events. For example, it can display a video tailored to a summer bargain sale. This allows for more personalized promotional videos to be provided to customers.

[0034] Signage can be equipped with a touch panel function, allowing customers to search for product information or participate in promotions by directly touching the screen. For example, signage can be equipped with a touch panel function, allowing customers to search for product information by directly touching the screen. For example, touching a specific product displays detailed information about that product. Signage can also allow customers to participate in promotions using the touch panel. For example, a quiz or game-style promotion can be displayed, and customers can answer by touching the screen. Signage can also use the touch panel function to allow customers to customize product information to suit their interests. For example, it can provide a function to search for products in a specific category or brand. This allows customers to search for product information or participate in promotions by directly touching the screen.

[0035] The signage can be equipped with a VR function, allowing customers to explore a virtual store and select products through the signage. For example, the signage can be equipped with a VR function and allow customers to explore a virtual store. For example, the signage can provide an experience of walking around the virtual store by wearing VR goggles in front of the signage. The signage can also use the VR function to allow customers to select products in the virtual store. For example, the signage can allow customers to pick up products in the virtual store and check detailed information. The signage can also use the VR function to allow customers to participate in promotional events in the virtual store. For example, the signage can provide an experience of participating in sales and campaigns held in the virtual store. This allows customers to explore a virtual store and select products.

[0036] A cross-modal signage distribution system can use generative AI to automatically generate cross-modal promotional content that appeals to the senses of sight, hearing, and smell based on customer purchase history and behavioral data. A cross-modal signage distribution system can, for example, use generative AI to automatically generate promotional content that appeals to the senses of sight, hearing, and smell based on a customer's purchase history. For example, it can display promotional content that combines images, music, and scents related to products the customer has previously purchased. A cross-modal signage distribution system can also analyze in-store behavioral data and display cross-modal promotional content for products that the customer may be interested in. For example, if a customer spends a long time in a particular area, it can display images, music, and scents introducing products in that area. A cross-modal signage distribution system can also combine customer purchase history and behavioral data to generate cross-modal promotional content tailored to specific seasons or events. For example, it can display images, music, and scents tailored to a summer bargain sale. This allows customers to receive cross-modal promotional content that appeals to the senses of sight, hearing, and smell.

[0037] A cross-modal signage distribution system can be equipped with a voice recognition function, allowing customers to customize promotional content by voice. A cross-modal signage distribution system, for example, is equipped with a voice recognition function, allowing customers to customize promotional content by voice. For example, if a customer says, "Show me the new product promotion," a promotional video will be displayed on the display. A cross-modal signage distribution system also allows customers to customize promotional content by voice. For example, if a customer says, "Tell me about this week's special sale items," special sale information will be displayed on the display. A cross-modal signage distribution system also uses a voice recognition function to allow customers to customize promotional content by voice. For example, if a customer says, "Tell me about the nutritional information for this product," nutritional information will be displayed on the display. This allows customers to customize promotional content by voice.

[0038] A cross-modal signage delivery system is equipped with an AR function, and when a customer scans a product through the signage, a promotion can be displayed in AR. A cross-modal signage delivery system is equipped with an AR function, for example, and when a customer scans a product, a promotion can be displayed on the display in AR. For example, the product's ingredients and usage instructions can be displayed in a 3D model. Furthermore, when a customer scans a product through the signage, a cross-modal signage delivery system can display promotional information in AR. For example, product discount information and related product suggestions can be displayed in AR. Furthermore, when a customer scans a product through the cross-modal signage delivery system, the AR function can be used to display product reviews and ratings on the display. For example, reviews and star ratings from other customers can be displayed in AR. This allows customers to check promotions through AR.

[0039] The store visit data analysis platform can use generative AI to automatically generate individually customized promotion strategies based on customer purchase history and store visit data. For example, the store visit data analysis platform uses generative AI to automatically generate individually customized promotion strategies based on customer purchase history. For example, it may propose promotions for new products related to products that the customer has previously purchased. The store visit data analysis platform also analyzes store visit data and proposes products that the customer may be interested in in a promotion strategy. For example, if a customer spends a long time in a specific area, it may propose a promotion introducing products in that area. The store visit data analysis platform also combines the customer's purchase history and store visit data to automatically generate promotion strategies tailored to specific seasons or events. For example, it proposes a promotion strategy tailored to a summer bargain sale. This makes it possible to provide customers with individually customized promotion strategies.

[0040] The store visit data analysis platform can add a voice recognition function to collect and analyze customer voice data and reflect it in promotion strategies. The store visit data analysis platform, for example, is equipped with a voice recognition function and collects and analyzes customer voice data to reflect it in promotion strategies. For example, when a customer says, "Tell me about this product," the promotion strategy is adjusted based on that data. The store visit data analysis platform also analyzes customer voice data to suggest products and promotions that might interest them. For example, when a customer says, "What products do you recommend?", the platform proposes a promotion strategy based on that data. The store visit data analysis platform also uses a voice recognition function to collect and analyze customer voice data in real time and reflect it in promotion strategies. For example, when a customer says, "Tell me about this week's sale items," the platform displays sale information based on that data. This makes it possible to provide promotion strategies based on customer voice data.

[0041] The store visit data analysis platform is equipped with VR functionality and can collect and analyze customer behavior data within a virtual store. The store visit data analysis platform, for example, is equipped with VR functionality and collects and analyzes customer behavior data within a virtual store. For example, it analyzes which areas of the virtual store customers spend the most time in and adjusts promotion strategies based on that data. The store visit data analysis platform also uses VR functionality to collect and analyze behavioral data when customers select products within the virtual store. For example, it analyzes which products customers pick up and check details about, and proposes promotion strategies based on that data. The store visit data analysis platform also automatically generates promotion strategies tailored to specific seasons or events based on customer behavior data within the virtual store. For example, it proposes promotion strategies tailored to summer bargain sales. This allows the collection and analysis of customer behavior data within a virtual store.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The business support package may further include a prediction unit that predicts customer purchasing behavior. The prediction unit predicts the next product a customer is likely to purchase based on past purchase data and store visit data. For example, if a customer has frequently purchased a specific brand of milk in the past, the prediction unit predicts and suggests new products and related products of that brand. The prediction unit can also predict purchasing behavior according to seasons and events. For example, the prediction unit can predict that purchases of cold drinks and ice cream will increase in the summer and focus promotions on these products. The prediction unit can also analyze customer purchasing patterns and predict purchasing behavior on specific days of the week and time periods. For example, the prediction unit can predict that family products will be purchased in large numbers on weekends and implement weekend-only promotions. This allows the business support package to predict customer purchasing behavior and develop more effective promotion strategies.

[0044] The smart cart can also be equipped with a health management function. The health management function suggests health-conscious products based on the customer's health data. For example, if a customer has diabetes, it will suggest low-sugar products. The health management function can also record the customer's dietary history and suggest recipes that take nutritional balance into consideration. For example, if a customer has not purchased many vegetables in the past, it will suggest recipes that include a lot of vegetables. The health management function can also suggest products that are suitable for after exercise based on the customer's exercise data. For example, it can suggest protein drinks or energy bars after exercise. This enables the smart cart to suggest products that support the customer's health.

[0045] The smart cart can further be equipped with an eco-friendly product suggestion function. The eco-friendly product suggestion function gives priority to suggesting environmentally friendly products. For example, it suggests products in reusable containers or organic foods. The eco-friendly product suggestion function can also display the environmental impact of products purchased by customers. For example, it can display the carbon footprint and recycling rate of the products. The eco-friendly product suggestion function can also provide customers with benefits that can be obtained by purchasing eco-friendly products. For example, customers can accumulate eco points and obtain discount coupons. This enables the smart cart to suggest environmentally friendly products.

[0046] The smart cart may further include a learning unit that learns customer purchasing behavior. The learning unit learns customer preferences and purchasing patterns based on the customer's purchase history and behavioral data. For example, if a customer frequently purchases products from a particular brand, the learning unit will prioritize suggesting new products from that brand. The learning unit can also analyze customer purchasing behavior and detect changes in purchasing patterns. For example, if a customer has recently purchased many health-conscious products, the learning unit will suggest health-conscious products. The learning unit can also predict future purchasing behavior based on the customer's purchasing behavior. For example, if a customer tends to purchase certain products in a particular season, the learning unit will suggest products that are appropriate for that season. This allows the smart cart to learn customer purchasing behavior and make more personalized product suggestions.

[0047] The smart cart can further include an analysis unit that analyzes customer purchasing behavior. The analysis unit analyzes purchasing behavior trends based on customer purchase history and behavioral data. For example, if a customer tends to purchase certain products on certain days of the week or at certain times of the day, a promotion strategy can be developed based on that information. The analysis unit can also detect changes in purchasing patterns based on customer purchasing behavior. For example, if a customer has recently been purchasing many health-conscious products, the analysis unit can suggest health-conscious products. The analysis unit can also predict future purchasing behavior based on customer purchasing behavior. For example, if a customer tends to purchase certain products during a certain season, the analysis unit can suggest products that are appropriate for that season. This allows the smart cart to analyze customer purchasing behavior and make more effective product suggestions.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The smart cart provides product and promotional information to customers. For example, when a customer adds a specific item to their cart, the display will show that item's nutritional information and recipe suggestions. The smart cart can also record the customer's purchasing history and provide personalized promotions. Step 2: Signage displays promotional information within the store. For example, a promotional video for a new product is displayed using a digital display. The signage also has interactive functions, allowing customers to search for product information using a touch panel. Step 3: A cross-modal signage distribution system controls signage, speakers, and scent devices in an integrated manner. For example, promotional videos, audio, and scents can be simultaneously distributed at specific times to attract customer attention. Step 4: The store visit data analysis platform analyzes sales data and inventory status. For example, it analyzes customer purchase history to identify the times and days of the week when specific products sell well, and uses that information to develop promotion strategies.

[0050] (Example 2) The business support package according to an embodiment of the present invention is a system that supports sales promotion, sales analysis, and inventory management in a food retail chain. This system aims to increase sales by improving the number of purchases and the rate of store visits, and standardizes operations that rely on experience and intuition at each store. As a result, the business support package can increase the number of purchases and the rate of store visits by customers, thereby increasing sales. Furthermore, it can standardize operations at each store and realize efficient business management.

[0051] The business support package according to the embodiment includes a smart cart, signage, a cross-modal signage distribution system, and a store visit data analysis platform. The smart cart provides product information and promotional information to customers. For example, when a customer places a specific product in the cart, the smart cart displays the product's nutritional information and recipe suggestions on the display. The smart cart can also record customer purchase history and provide personalized promotions. The signage displays promotional information in the store. For example, the signage displays promotional videos for new products using a digital display. The signage also has interactive functions, allowing customers to search for product information using a touch panel. The cross-modal signage distribution system comprehensively controls the signage, speakers, and scent devices. For example, the cross-modal signage distribution system attracts customer attention by simultaneously distributing promotional videos, audio, and scents at specific times of the day. The store visit data analysis platform analyzes sales data and inventory status. For example, the store visit data analysis platform analyzes customer purchase histories to identify the time periods and days of the week when specific products sell well, and then develops promotion strategies based on that information. This allows the business support package according to the embodiment to increase the number of purchases and store visit rates of customers, thereby increasing sales. It also allows for the standardization of operations at each store, realizing efficient business management.

[0052] Equipped with generative AI, the smart cart can make individually customized recipe and product suggestions based on a customer's purchasing history and behavioral data. For example, a smart cart equipped with generative AI can suggest related recipes and new products based on data on products a customer has purchased in the past. For example, it can suggest weekly recipes based on ingredients that a customer frequently purchases. The smart cart also analyzes real-time behavioral data and displays promotions related to products that a customer has added to their cart. For example, if a customer adds pasta to their cart, it will display discount information on pasta sauce. The smart cart also makes product suggestions tailored to specific seasons and events based on the customer's purchasing history. For example, during the Christmas season, it will suggest Christmas dinner recipes and related products. This enables more personalized suggestions to be made to customers.

[0053] Smart carts use sensors to analyze customers' movements and facial expressions, assess their stress and satisfaction in real time, and adjust product recommendations based on that. For example, smart carts use built-in cameras to analyze customers' facial expressions and assess their stress levels. For example, if a customer looks tired, they can suggest products that have a relaxing effect. Smart carts also analyze customers' movement data to understand their shopping progress. For example, if a customer stays in the same place for a long time, they can suggest popular products nearby. Smart carts also combine customers' facial and movement data to assess their satisfaction in real time, and if satisfaction is low, they can display special promotions. For example, if a customer looks dissatisfied, they can offer a discount coupon. This makes it possible to suggest products based on a customer's stress and satisfaction.

[0054] The Smart Cart uses its emotion estimation function to analyze the emotions customers feel when adding items to their carts and display promotions designed to elicit positive emotions in real time. For example, the Smart Cart analyzes the customer's facial expressions when adding items to their carts and displays relevant promotions if positive emotions are detected. For example, if a customer selects a product with a smile, the Smart Cart displays discount information for that product. The Smart Cart also uses its emotion estimation function to monitor the customer's emotions in real time when adding items to their carts and displays encouraging messages if negative emotions are detected. For example, if a customer has a confused expression, the Smart Cart displays a video explaining how to use the product. The Smart Cart also emits music or fragrances from the cart to elicit positive emotions based on the customer's emotional data. For example, if a customer has a relaxed expression, the Smart Cart emits a relaxing fragrance. This enables promotions that elicit positive emotions in customers.

[0055] Smart carts can be equipped with a voice recognition function, allowing customers to search for product information by voice or indicate which products to add to their cart. Smart carts, for example, are equipped with a voice recognition function, allowing customers to search for product information by voice. For example, if they say, "Tell me the nutritional information for tomato sauce," the information will be displayed on the display. Smart carts also allow customers to indicate which products to add to their cart by voice. For example, if they say, "Add milk to my cart," the product will be added to the cart. Smart carts also use a voice recognition function to allow customers to search for promotional information by voice. For example, if they say, "Tell me what's on sale this week," the sale information will be displayed on the display. This allows customers to search for product information or add products to their cart by voice.

[0056] Smart carts are equipped with AR functionality, and when a customer scans a product through the cart, detailed product information and promotions can be displayed in AR. For example, when a customer scans a product, a smart cart is equipped with AR functionality, and detailed product information is displayed on the display in AR. For example, the product's ingredients and usage instructions are displayed in a 3D model. When a customer scans a product through the cart, a smart cart also displays promotional information in AR. For example, product discount information and related product suggestions are displayed in AR. When a customer scans a product through the smart cart, the AR function also displays product reviews and ratings on the display. For example, reviews and star ratings from other customers are displayed in AR. This allows customers to check detailed product information and promotions through AR.

[0057] The smart cart uses its emotion estimation function to monitor the emotions of customers operating the cart in real time and emit music or fragrances from the cart to elicit positive emotions. For example, the smart cart uses its emotion estimation function to monitor the emotions of customers operating the cart in real time and emit music from the cart to elicit positive emotions. For example, if a customer has a relaxed expression, it plays relaxing music. The smart cart also emits fragrances from the cart to elicit positive emotions based on the customer's emotion data. For example, if a customer has a happy expression, it emits a fruity fragrance. The smart cart also uses its emotion estimation function to monitor the emotions of customers operating the cart in real time and displays an encouraging message if negative emotions are detected. For example, if a customer has a confused expression, it displays a video explaining how to use a product. This makes it possible to provide music or fragrances to elicit positive emotions in customers.

[0058] Using generative AI, signage can generate and display individually customized promotional videos in real time based on customer purchase history and behavioral data. For example, signage equipped with generative AI can generate individually customized promotional videos based on a customer's purchase history. For example, it can display a promotional video for new products related to products the customer previously purchased. Signage can also analyze in-store behavioral data and display promotional videos for products that the customer may be interested in. For example, if a customer spends a long time in a particular area, it can display a video introducing products in that area. Signage can also combine customer purchase history and behavioral data to generate promotional videos tailored to specific seasons or events. For example, it can display a video tailored to a summer bargain sale. This allows for more personalized promotional videos to be provided to customers.

[0059] Signage can analyze customers' facial expressions using a built-in camera, assess their interests in real time, and adjust the display content based on that. For example, signage can analyze customers' facial expressions using a built-in camera, assessing their interests in real time. For example, if a customer is looking at the signage with a smile, the signage will display detailed information about that product. The signage can also display products or promotions that are likely to interest the customer based on the customer's facial expression data. For example, if a customer has a surprised expression, the signage will display an image that highlights the features of that product. The signage can also adjust the display content in real time based on the results of the analysis of the customer's facial expression. For example, if a customer has a dissatisfied expression, the signage will display a different product or promotion. This makes it possible to provide display content that matches the customer's interests.

[0060] The signage can use the emotion estimation function to analyze the emotions of customers standing in front of the signage and display images and audio to elicit positive emotions. For example, the signage can use the emotion estimation function to analyze the emotions of customers standing in front of the signage in real time and display images to elicit positive emotions. For example, if a customer is smiling, the signage can display a promotional video along with cheerful music. The signage can also emit audio to elicit positive emotions from the signage based on the customer's emotional data. For example, if a customer has a relaxed expression, the signage can play relaxing music. The signage can also use the emotion estimation function to monitor the customer's emotions in real time and display an encouraging message if negative emotions are detected. For example, if a customer has a confused expression, the signage can display a video explaining how to use a product. In this way, it is possible to provide images and audio that elicit positive emotions in customers.

[0061] Signage can be equipped with a touch panel function, allowing customers to search for product information or participate in promotions by directly touching the screen. For example, signage can be equipped with a touch panel function, allowing customers to search for product information by directly touching the screen. For example, touching a specific product displays detailed information about that product. Signage can also allow customers to participate in promotions using the touch panel. For example, a quiz or game-style promotion can be displayed, and customers can answer by touching the screen. Signage can also use the touch panel function to allow customers to customize product information to suit their interests. For example, it can provide a function to search for products in a specific category or brand. This allows customers to search for product information or participate in promotions by directly touching the screen.

[0062] The signage can be equipped with a VR function, allowing customers to explore a virtual store and select products through the signage. For example, the signage can be equipped with a VR function and allow customers to explore a virtual store. For example, the signage can provide an experience of walking around the virtual store by wearing VR goggles in front of the signage. The signage can also use the VR function to allow customers to select products in the virtual store. For example, the signage can allow customers to pick up products in the virtual store and check detailed information. The signage can also use the VR function to allow customers to participate in promotional events in the virtual store. For example, the signage can provide an experience of participating in sales and campaigns held in the virtual store. This allows customers to explore a virtual store and select products.

[0063] The signage can use the emotion estimation function to monitor the emotions of customers standing in front of the signage in real time and display interactive games and quizzes to elicit positive emotions. For example, the signage can use the emotion estimation function to monitor the emotions of customers standing in front of the signage in real time and display interactive games to elicit positive emotions. For example, if the customer is smiling, a fun game can be displayed. The signage can also display quizzes to elicit positive emotions based on the customer's emotion data. For example, a quiz can be presented about a product that the customer might be interested in, and a bonus can be offered if the answer is correct. The signage can also use the emotion estimation function to monitor the customer's emotions in real time and display an encouraging message if negative emotions are detected. For example, if a customer looks confused, a simple game or quiz can be displayed to refresh the customer's mood. This makes it possible to provide interactive games and quizzes that elicit positive emotions in customers.

[0064] A cross-modal signage distribution system can use generative AI to automatically generate cross-modal promotional content that appeals to the senses of sight, hearing, and smell based on customer purchase history and behavioral data. A cross-modal signage distribution system can, for example, use generative AI to automatically generate promotional content that appeals to the senses of sight, hearing, and smell based on a customer's purchase history. For example, it can display promotional content that combines images, music, and scents related to products the customer has previously purchased. A cross-modal signage distribution system can also analyze in-store behavioral data and display cross-modal promotional content for products that the customer may be interested in. For example, if a customer spends a long time in a particular area, it can display images, music, and scents introducing products in that area. A cross-modal signage distribution system can also combine customer purchase history and behavioral data to generate cross-modal promotional content tailored to specific seasons or events. For example, it can display images, music, and scents tailored to a summer bargain sale. This allows customers to receive cross-modal promotional content that appeals to the senses of sight, hearing, and smell.

[0065] A cross-modal signage distribution system incorporates sensors to analyze customer behavior and facial expressions and adjust promotional content in real time. A cross-modal signage distribution system, for example, analyzes customer facial expressions using a built-in camera and adjusts promotional content in real time. For example, if a customer is smiling while looking at the signage, detailed information about that product is displayed. A cross-modal signage distribution system also analyzes customer behavior data and displays products and promotions that might interest the customer. For example, if a customer stays in a particular area for a long time, it displays video, music, and scents introducing products in that area. A cross-modal signage distribution system also combines customer facial and behavior data to adjust promotional content in real time. For example, if a customer looks dissatisfied, it displays a different product or promotion. This allows promotional content to be adjusted in real time according to the customer's behavior and facial expressions.

[0066] A cross-modal signage distribution system can use an emotion estimation function to analyze customer emotions in real time and deliver cross-modal promotions to elicit positive emotions. A cross-modal signage distribution system, for example, uses the emotion estimation function to analyze customer emotions in real time and deliver cross-modal promotions to elicit positive emotions. For example, if a customer is smiling, a promotional video is displayed along with cheerful music. A cross-modal signage distribution system can also emit audio from the signage to elicit positive emotions based on customer emotion data. For example, if a customer has a relaxed expression, relaxing music is played. A cross-modal signage distribution system can also monitor customer emotions in real time using the emotion estimation function and display encouraging messages if negative emotions are detected. For example, if a customer has a confused expression, a video explaining how to use a product is displayed. This makes it possible to provide cross-modal promotions that elicit positive emotions in customers.

[0067] A cross-modal signage distribution system can be equipped with a voice recognition function, allowing customers to customize promotional content by voice. A cross-modal signage distribution system, for example, is equipped with a voice recognition function, allowing customers to customize promotional content by voice. For example, if a customer says, "Show me the new product promotion," a promotional video will be displayed on the display. A cross-modal signage distribution system also allows customers to customize promotional content by voice. For example, if a customer says, "Tell me about this week's special sale items," special sale information will be displayed on the display. A cross-modal signage distribution system also uses a voice recognition function to allow customers to customize promotional content by voice. For example, if a customer says, "Tell me about the nutritional information for this product," nutritional information will be displayed on the display. This allows customers to customize promotional content by voice.

[0068] A cross-modal signage delivery system is equipped with an AR function, and when a customer scans a product through the signage, a promotion can be displayed in AR. A cross-modal signage delivery system is equipped with an AR function, for example, and when a customer scans a product, a promotion can be displayed on the display in AR. For example, the product's ingredients and usage instructions can be displayed in a 3D model. Furthermore, when a customer scans a product through the signage, a cross-modal signage delivery system can display promotional information in AR. For example, product discount information and related product suggestions can be displayed in AR. Furthermore, when a customer scans a product through the cross-modal signage delivery system, the AR function can be used to display product reviews and ratings on the display. For example, reviews and star ratings from other customers can be displayed in AR. This allows customers to check promotions through AR.

[0069] A cross-modal signage distribution system can use an emotion estimation function to monitor customer emotions in real time and deliver cross-modal entertainment content designed to elicit positive emotions. A cross-modal signage distribution system, for example, uses the emotion estimation function to monitor customer emotions in real time and deliver cross-modal entertainment content designed to elicit positive emotions. For example, if a customer is smiling, it displays entertainment videos accompanied by cheerful music. The cross-modal signage distribution system also emits audio from the signage designed to elicit positive emotions based on customer emotion data. For example, if a customer has a relaxed expression, it plays relaxing music. The cross-modal signage distribution system also uses the emotion estimation function to monitor customer emotions in real time and displays encouraging messages when negative emotions are detected. For example, if a customer has a confused expression, it displays a video explaining how to use a product. This makes it possible to provide cross-modal entertainment content designed to elicit positive emotions in customers.

[0070] The store visit data analysis platform can use generative AI to automatically generate individually customized promotion strategies based on customer purchase history and store visit data. For example, the store visit data analysis platform uses generative AI to automatically generate individually customized promotion strategies based on customer purchase history. For example, it may propose promotions for new products related to products that the customer has previously purchased. The store visit data analysis platform also analyzes store visit data and proposes products that the customer may be interested in in a promotion strategy. For example, if a customer spends a long time in a specific area, it may propose a promotion introducing products in that area. The store visit data analysis platform also combines the customer's purchase history and store visit data to automatically generate promotion strategies tailored to specific seasons or events. For example, it proposes a promotion strategy tailored to a summer bargain sale. This makes it possible to provide customers with individually customized promotion strategies.

[0071] The store visit data analysis platform incorporates sensors to analyze customer behavior and facial expressions, allowing it to collect and analyze data in real time. For example, the store visit data analysis platform uses a built-in camera to analyze customer facial expressions and collect and analyze data in real time. For example, if a customer is smiling, the platform can adjust promotion strategies based on that data. The store visit data analysis platform also analyzes customer behavior data to suggest products and promotions that the customer might be interested in. For example, if a customer spends a long time in a particular area, the platform can suggest promotions introducing products in that area. The store visit data analysis platform also combines customer facial and behavior data to collect and analyze data in real time. For example, if a customer looks dissatisfied, the platform can suggest different products or promotions. This allows data based on customer behavior and facial expressions to be collected and analyzed in real time.

[0072] The store visit data analysis platform can use the emotion estimation function to analyze customer emotion data and develop promotion strategies to elicit positive emotions. The store visit data analysis platform, for example, uses the emotion estimation function to analyze customer emotion data in real time and develop promotion strategies to elicit positive emotions. For example, if a customer is smiling, the promotion strategy is adjusted based on that data. The store visit data analysis platform also proposes promotion strategies to elicit positive emotions based on the customer emotion data. For example, if a customer has a relaxed expression, the platform proposes a promotion that plays music that has a relaxing effect. The store visit data analysis platform also uses the emotion estimation function to monitor customer emotions in real time and displays an encouraging message if negative emotions are detected. For example, if a customer has a confused expression, the platform displays a video explaining how to use a product. This makes it possible to provide promotion strategies that elicit positive emotions in customers.

[0073] The store visit data analysis platform can add a voice recognition function to collect and analyze customer voice data and reflect it in promotion strategies. The store visit data analysis platform, for example, is equipped with a voice recognition function and collects and analyzes customer voice data to reflect it in promotion strategies. For example, when a customer says, "Tell me about this product," the promotion strategy is adjusted based on that data. The store visit data analysis platform also analyzes customer voice data to suggest products and promotions that might interest them. For example, when a customer says, "What products do you recommend?", the platform proposes a promotion strategy based on that data. The store visit data analysis platform also uses a voice recognition function to collect and analyze customer voice data in real time and reflect it in promotion strategies. For example, when a customer says, "Tell me about this week's sale items," the platform displays sale information based on that data. This makes it possible to provide promotion strategies based on customer voice data.

[0074] The store visit data analysis platform is equipped with VR functionality and can collect and analyze customer behavior data within a virtual store. The store visit data analysis platform, for example, is equipped with VR functionality and collects and analyzes customer behavior data within a virtual store. For example, it analyzes which areas of the virtual store customers spend the most time in and adjusts promotion strategies based on that data. The store visit data analysis platform also uses VR functionality to collect and analyze behavioral data when customers select products within the virtual store. For example, it analyzes which products customers pick up and check details about, and proposes promotion strategies based on that data. The store visit data analysis platform also automatically generates promotion strategies tailored to specific seasons or events based on customer behavior data within the virtual store. For example, it proposes promotion strategies tailored to summer bargain sales. This allows the collection and analysis of customer behavior data within a virtual store.

[0075] The store visit data analysis platform can use the emotion estimation function to monitor customer emotion data in real time and continuously optimize promotion strategies to elicit positive emotions. The store visit data analysis platform, for example, uses the emotion estimation function to monitor customer emotion data in real time and continuously optimize promotion strategies to elicit positive emotions. For example, if a customer is smiling, the promotion strategy is adjusted based on that data. The store visit data analysis platform also suggests promotion strategies to elicit positive emotions based on the customer emotion data. For example, if a customer has a relaxed expression, the platform suggests a promotion that plays music that has a relaxing effect. The store visit data analysis platform also uses the emotion estimation function to monitor customer emotions in real time and displays an encouraging message if negative emotions are detected. For example, if a customer has a confused expression, the platform displays a video explaining how to use a product. This makes it possible to continuously optimize promotion strategies to elicit positive emotions in customers.

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

[0077] The business support package may further include a prediction unit that predicts customer purchasing behavior. The prediction unit predicts the next product a customer is likely to purchase based on past purchase data and store visit data. For example, if a customer has frequently purchased a specific brand of milk in the past, the prediction unit predicts and suggests new products and related products of that brand. The prediction unit can also predict purchasing behavior according to seasons and events. For example, the prediction unit can predict that purchases of cold drinks and ice cream will increase in the summer and focus promotions on these products. The prediction unit can also analyze customer purchasing patterns and predict purchasing behavior on specific days of the week and time periods. For example, the prediction unit can predict that family products will be purchased in large numbers on weekends and implement weekend-only promotions. This allows the business support package to predict customer purchasing behavior and develop more effective promotion strategies.

[0078] The smart cart can also be equipped with a health management function. The health management function suggests health-conscious products based on the customer's health data. For example, if a customer has diabetes, it will suggest low-sugar products. The health management function can also record the customer's dietary history and suggest recipes that take nutritional balance into consideration. For example, if a customer has not purchased many vegetables in the past, it will suggest recipes that include a lot of vegetables. The health management function can also suggest products that are suitable for after exercise based on the customer's exercise data. For example, it can suggest protein drinks or energy bars after exercise. This enables the smart cart to suggest products that support the customer's health.

[0079] The smart cart can further be equipped with an eco-friendly product suggestion function. The eco-friendly product suggestion function gives priority to suggesting environmentally friendly products. For example, it suggests products in reusable containers or organic foods. The eco-friendly product suggestion function can also display the environmental impact of products purchased by customers. For example, it can display the carbon footprint and recycling rate of the products. The eco-friendly product suggestion function can also provide customers with benefits that can be obtained by purchasing eco-friendly products. For example, customers can accumulate eco points and obtain discount coupons. This enables the smart cart to suggest environmentally friendly products.

[0080] The smart cart may further include a learning unit that learns customer purchasing behavior. The learning unit learns customer preferences and purchasing patterns based on the customer's purchase history and behavioral data. For example, if a customer frequently purchases products from a particular brand, the learning unit will prioritize suggesting new products from that brand. The learning unit can also analyze customer purchasing behavior and detect changes in purchasing patterns. For example, if a customer has recently purchased many health-conscious products, the learning unit will suggest health-conscious products. The learning unit can also predict future purchasing behavior based on the customer's purchasing behavior. For example, if a customer tends to purchase certain products in a particular season, the learning unit will suggest products that are appropriate for that season. This allows the smart cart to learn customer purchasing behavior and make more personalized product suggestions.

[0081] The smart cart can further include an analysis unit that analyzes customer purchasing behavior. The analysis unit analyzes purchasing behavior trends based on customer purchase history and behavioral data. For example, if a customer tends to purchase certain products on certain days of the week or at certain times of the day, a promotion strategy can be developed based on that information. The analysis unit can also detect changes in purchasing patterns based on customer purchasing behavior. For example, if a customer has recently been purchasing many health-conscious products, the analysis unit can suggest health-conscious products. The analysis unit can also predict future purchasing behavior based on customer purchasing behavior. For example, if a customer tends to purchase certain products during a certain season, the analysis unit can suggest products that are appropriate for that season. This allows the smart cart to analyze customer purchasing behavior and make more effective product suggestions.

[0082] The smart cart may further include an emotion estimation unit that estimates a customer's emotions and adjusts product suggestions based on the estimated emotions. The emotion estimation unit analyzes the customer's facial expressions and movements to estimate emotions in real time. For example, if a customer is smiling, it suggests products that match that emotion. The emotion estimation unit can also display promotions to elicit positive emotions based on the customer's emotional data. For example, if a customer has a relaxed expression, it suggests products that have a relaxing effect. The emotion estimation unit can also display encouraging messages if negative emotions are detected based on the customer's emotional data. For example, if a customer has a confused expression, it displays a video explaining how to use a product. This enables the smart cart to suggest products according to the customer's emotions.

[0083] The smart cart may further include an emotion estimation unit that estimates a customer's emotion and adjusts promotions based on the estimated emotion. The emotion estimation unit analyzes the customer's facial expressions and movements to estimate the emotion in real time. For example, if a customer is smiling, a promotion that matches that emotion is displayed. The emotion estimation unit can also display promotions that elicit positive emotions based on the customer's emotion data. For example, if a customer has a relaxed expression, it plays music that has a relaxing effect. The emotion estimation unit can also display an encouraging message if a negative emotion is detected based on the customer's emotion data. For example, if a customer has a confused expression, it displays a video explaining how to use a product. This enables the smart cart to provide promotions that match the customer's emotions.

[0084] The smart cart may further include an emotion estimation unit that estimates the customer's emotions and adjusts the environment in the cart based on the estimated emotions. The emotion estimation unit analyzes the customer's facial expressions and movements to estimate the customer's emotions in real time. For example, if the customer is smiling, music that matches that emotion is played. The emotion estimation unit can also emit a scent to elicit positive emotions based on the customer's emotional data. For example, if the customer has a relaxed expression, a scent with a relaxing effect is emitted. The emotion estimation unit can also display an encouraging message if negative emotions are detected based on the customer's emotional data. For example, if the customer has a confused expression, a video explaining how to use a product is displayed. This enables the smart cart to adjust the environment according to the customer's emotions.

[0085] The smart cart may further include an emotion estimation unit that estimates a customer's emotions and adjusts the display content in the cart based on the estimated emotions. The emotion estimation unit analyzes the customer's facial expressions and movements to estimate emotions in real time. For example, if a customer is smiling, product information that matches that emotion is displayed. The emotion estimation unit can also display promotions that elicit positive emotions based on the customer's emotional data. For example, if a customer has a relaxed expression, it can suggest products that have a relaxing effect. The emotion estimation unit can also display encouraging messages based on the customer's emotional data when negative emotions are detected. For example, if a customer has a confused expression, it can display a video explaining how to use a product. This enables the smart cart to adjust the display content according to the customer's emotions.

[0086] The smart cart may further include an emotion estimation unit that estimates the customer's emotions and adjusts the lighting inside the cart based on the estimated emotions. The emotion estimation unit analyzes the customer's facial expressions and movements to estimate emotions in real time. For example, if the customer is smiling, the lighting is adjusted to match that emotion. The emotion estimation unit can also set lighting to elicit positive emotions based on the customer's emotional data. For example, if the customer has a relaxed expression, the lighting is set to have a relaxing effect. The emotion estimation unit can also display an encouraging message if negative emotions are detected based on the customer's emotional data. For example, if the customer has a confused expression, a video explaining how to use a product is displayed. This enables the smart cart to adjust lighting according to the customer's emotions.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The smart cart provides product and promotional information to customers. For example, when a customer adds a specific item to their cart, the display will show that item's nutritional information and recipe suggestions. The smart cart can also record the customer's purchasing history and provide personalized promotions. Step 2: Signage displays promotional information within the store. For example, a promotional video for a new product is displayed using a digital display. The signage also has interactive functions, allowing customers to search for product information using a touch panel. Step 3: A cross-modal signage distribution system controls signage, speakers, and scent devices in an integrated manner. For example, promotional videos, audio, and scents can be simultaneously distributed at specific times to attract customer attention. Step 4: The store visit data analysis platform analyzes sales data and inventory status. For example, it analyzes customer purchase history to identify the times and days of the week when specific products sell well, and uses that information to develop promotion strategies.

[0089] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0130] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0139] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0140] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0141] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0142] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0143] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0144] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0145] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0146] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0147] 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.

[0148] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0149] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0150] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0151] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0152] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0153] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Smart Cart and Signage and A cross-modal implementation signage distribution system, Equipped with a store visit data analysis platform, The smart cart comprises: Providing customers with product and promotion information, The signage includes: Display promotional information in-store, The cross-modal implementation signage distribution system includes: The signage, speaker, and scent device are controlled in an integrated manner. The store visit data analysis platform is Analyze sales data and inventory status A system characterized by:

2. The smart cart comprises: Equipped with generative AI, Based on the customer's purchasing history and behavioral data, the system makes individually customized recipe and product suggestions.

2. The system of claim 1.

3. The smart cart comprises: Using sensors, Analyzing the customer's movements and facial expressions, Evaluate stress and satisfaction in real time, Adjust your product recommendations accordingly 2. The system of claim 1.

4. The smart cart comprises: Analyze the emotions customers feel when adding products to their cart and display promotions in real time to elicit those positive emotions 2. The system of claim 1.

5. The smart cart comprises: Add voice recognition function The customer searches for the product information by voice, Allow users to specify which products to add to their cart 2. The system of claim 1.

6. The smart cart comprises: Equipped with the AR function, When the customer scans the item through the cart, AR displays detailed information and promotions for the product 2. The system of claim 1.

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