system

Abstract

The system according to the embodiment aims to automate price setting and inventory management in stores and improve efficiency. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a display unit, and a provision unit. The collection unit collects data on clearance sales, consumption and expiration dates, inventory status, purchase prices, prices at competing stores, store visit predictions, and flyer publication status. The analysis unit uses AI to analyze the data collected by the collection unit and calculates prices. The display unit displays the prices calculated by the analysis unit on digital price tags. The provision unit provides customers with the price information calculated by the analysis unit.

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, pricing and inventory management in stores are often done manually, resulting in inefficiencies and a shortage of personnel.

[0005] The system according to the embodiment aims to automate price setting and inventory management in stores and improve efficiency. [Means for solving the problem]

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, a display unit, and a provision unit. The collection unit collects data on clearance sales, consumption and expiration dates, inventory status, purchase prices, prices at competing stores, store visit predictions, and flyer publication status. The analysis unit uses AI to analyze the data collected by the collection unit and calculates prices. The display unit displays the prices calculated by the analysis unit on digital price tags. The provision unit provides customers with the price information calculated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate price setting and inventory management in stores, thereby improving efficiency. [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 dynamic pricing system according to an embodiment of the present invention utilizes digital price tags and product barcode management in stores. This system sets minimum and maximum prices for each product, collects data such as clearance dates, expiration dates, inventory status, purchase prices, prices at competing stores, store visit forecasts (number of customers, day of the week, time, weather, etc.), and flyer listing status, and then uses AI to analyze the data to calculate optimal prices. The calculated prices are displayed on digital price tags and provided to customers. For example, products with low inventory and close expiration dates are priced lower. Conversely, prices are set higher when store visit forecasts are high and competing stores' prices are higher. In this way, prices are dynamically adjusted. Furthermore, digital flyers provide customers with the latest price information. For example, when a specific product is discounted, this information is notified to customers via the digital flyer. This makes it easier for customers to keep track of the latest prices. Furthermore, the price displayed on the digital price tag is automatically updated at checkout. This eliminates the need for price confirmation at the register or the affixing of discount stickers, improving operational efficiency. This system alleviates the labor shortage in the retail industry. This reduces the burden on employees by eliminating the need for price tags and affixing discount stickers. In addition, automating pricing eliminates the need for price adjustments. Furthermore, providing customers with the latest price information through digital flyers improves customer satisfaction. In this way, the dynamic pricing system automates pricing in stores and improves operational efficiency.

[0029] A dynamic pricing system according to an embodiment includes a collection unit, an analysis unit, a display unit, and a provision unit. The collection unit collects data on clearances, consumption and expiration dates, inventory status, purchase prices, prices at competing stores, store visit forecasts, and flyer posting status. For example, the collection unit monitors inventory status in real time using in-store sensors and cameras. The collection unit can also obtain purchase prices and prices at competing stores from an online database. The collection unit can also predict store visits based on weather forecasts and past store visit data. The analysis unit uses AI to analyze the data collected by the collection unit and calculate optimal prices. For example, the analysis unit uses a machine learning algorithm to predict prices based on past sales data and current inventory status. The analysis unit can also use deep learning to set prices taking into account prices at competing stores and store visit forecasts. The display unit displays the prices calculated by the analysis unit on a digital price tag. For example, the display unit displays prices using an electronic paper or LCD display. The display unit can also set the price update frequency. The providing unit provides the price information calculated by the analysis unit to the customer. For example, the providing unit provides the latest price information to the customer through a digital flyer. The providing unit can also provide the price information through a mobile app or a website. As a result, the dynamic pricing system according to the embodiment can automate price setting in stores and improve business efficiency.

[0030] The collection unit can analyze past data collection history and select a collection method. For example, the collection unit can identify the most efficient collection timing from the past data collection history and collect data at that timing. The collection unit can also adjust the type of data to be collected on a specific day of the week or time period based on the past data collection history. Furthermore, the collection unit can analyze the past data collection history and improve the collection method to increase the accuracy of the data. This enables efficient data collection by analyzing the past data collection history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the past data collection history into the generation AI and have the generation AI select the optimal collection method.

[0031] When collecting data, the collection unit can filter the data based on sales and seasonality of specific products. For example, the collection unit analyzes seasonal sales and prioritizes the collection of products that are in high demand during specific seasons. The collection unit can also prioritize the collection of high-selling products to optimize inventory management. Furthermore, the collection unit can filter and collect products that are in high demand during specific periods, taking seasonality into account. This enables more appropriate data collection by taking sales and seasonality into account. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input sales data into a generation AI and have the generation AI perform filtering.

[0032] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the store. For example, the collection unit can prioritize collecting data on products that are in high demand by taking into account the demographics of the area surrounding the store. The collection unit can also prioritize collecting data on products that are popular in a specific area by taking into account the location conditions of the store. Furthermore, the collection unit can prioritize collecting price information from competing stores near the store and reflect this in pricing. In this way, highly relevant data can be collected efficiently by taking geographical location information into account. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location data to a generation AI and cause the generation AI to select highly relevant data.

[0033] The collection unit can analyze social media activity and collect related data when collecting data. For example, the collection unit can prioritize collecting data on products that are trending on social media. The collection unit can also analyze social media trends and collect data on products that are likely to be in high demand. Furthermore, the collection unit can collect customer feedback on social media and use it to improve products. This analysis of social media activity improves the accuracy of demand forecasts. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media data into a generation AI and have the generation AI collect related data.

[0034] The analysis unit can adjust the level of detail of the analysis based on the importance of the product. For example, the analysis unit can perform a detailed analysis on products with high importance and provide highly accurate results. The analysis unit can also perform a simple analysis on products with low importance and provide results quickly. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the product. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the product. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the product category. For example, for fresh food, the analysis unit can apply an analysis algorithm that emphasizes expiration dates and inventory status. For daily necessities, the analysis unit can also apply an analysis algorithm that emphasizes sales trends and prices at competing stores. Furthermore, for seasonal products, the analysis unit can apply an analysis algorithm that emphasizes seasonality and store visit predictions. In this way, applying an analysis algorithm depending on the product category improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0036] The analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data and adjusts prices in real time. The analysis unit can also perform analysis according to the current situation while referring to past data. Furthermore, the analysis unit can gradually adjust the priority of analysis based on the time when the data was collected. This enables real-time price adjustments by determining the priority of analysis based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collection time data into the generation AI and have the generation AI determine the priority of analysis.

[0037] The analysis unit can improve the accuracy of the analysis by referring to related market data. The analysis unit can improve the accuracy of pricing by referring to price data of competing stores, for example. The analysis unit can also improve the accuracy of demand forecasting by referring to market trend data. Furthermore, the analysis unit can improve the accuracy of individual demand forecasting by referring to customer purchase history data. In this way, by referring to market data, the accuracy of pricing and demand forecasting is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input market data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0038] The display unit can adjust the level of detail of the display based on the sales of the product. For example, the display unit can display detailed information about a product that is selling well to appeal to customers. The display unit can also display concise information about a product that is not selling well to attract customers' attention. Furthermore, the display unit can gradually adjust the level of detail of the display according to the sales of the product. In this way, by adjusting the level of detail of the display according to the sales of the product, it is possible to provide customers with information that is useful to them. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input sales data to a generation AI and cause the generation AI to adjust the level of detail of the display.

[0039] The display unit can apply different display methods depending on the product category. For example, for fresh food, the display unit can apply a display method that emphasizes expiration dates and stock status. For daily necessities, the display unit can also apply a display method that emphasizes prices and discount information. For seasonal products, the display unit can also apply a display method that emphasizes seasonality and special promotions. By applying a display method depending on the product category, it is possible to provide a display that is easy for customers to view. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input product category data into the generation AI and cause the generation AI to apply different display methods.

[0040] The display unit can determine display priorities based on the inventory status of the products. For example, the display unit can prioritize products with low inventory and sell them out quickly. The display unit can also display products with high inventory later and prioritize other products. Furthermore, the display unit can gradually adjust the display priorities according to the inventory status of the products. This enables efficient inventory management by determining the display priorities according to the inventory status. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input inventory status data to a generation AI and have the generation AI determine the display priorities.

[0041] The display unit can adjust the display content by referring to related promotional information. For example, if a specific product is on promotion, the display unit highlights that information when it is displayed. The display unit can also adjust the display content of related products by referring to the promotional information. Furthermore, the display unit can customize the display content based on the promotional information to appeal to customers. In this way, by referring to the promotional information, it is possible to provide customers with information that is useful to them. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the promotional information into a generation AI and have the generation AI adjust the display content.

[0042] The providing unit can analyze a customer's purchase history and select optimal information. For example, the providing unit can analyze a customer's past purchase history and provide information on related products. The providing unit can also analyze a customer's purchasing patterns and provide optimal promotion information. Furthermore, the providing unit can provide individually customized information based on the customer's purchase history. In this way, by analyzing a customer's purchase history, it is possible to provide information that is useful to the customer. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input purchase history data into a generation AI and cause the generation AI to select optimal information.

[0043] The providing unit can customize information based on the customer's current areas of interest. For example, the providing unit can prioritize providing information on products in which the customer is currently interested. The providing unit can also customize and provide information on related products based on the customer's areas of interest. Furthermore, the providing unit can analyze the customer's current areas of interest and provide optimal information. In this way, by customizing information based on the customer's areas of interest, it is possible to provide information that is useful to the customer. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input area of ​​interest data to a generating AI and have the generating AI customize the information.

[0044] The providing unit can select optimal information by taking into account the customer's geographical location information. For example, the providing unit can provide promotional information for nearby stores based on the customer's current location. The providing unit can also provide optimal product information by taking into account the customer's geographical location information. Furthermore, the providing unit can also provide area-limited special offers based on the customer's location information. This makes it possible to provide useful information to customers by taking into account the customer's geographical location information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input geographical location data into a generating AI and cause the generating AI to select optimal information.

[0045] The provision unit can analyze the customer's social media activity and provide related information. For example, the provision unit can analyze the customer's social media activity and provide information on products in which the customer is interested. The provision unit can also provide information on products that are trending on social media. Furthermore, the provision unit can provide related promotional information based on the customer's social media activity. In this way, by analyzing the customer's social media activity, it is possible to provide useful information to the customer. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input social media data into a generation AI and cause the generation AI to provide related information.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The analysis unit can adjust the pricing algorithm based on the product's life cycle stage. For example, during the product's introduction stage, promotional prices can be set to attract customer interest. During the growth stage, prices can be set according to demand to maximize profits. Furthermore, during the maturity stage, prices can be taken into account by competitors to maintain price competitiveness. This makes it possible to set prices according to the product's life cycle stage, thereby optimizing sales and profits.

[0048] The collection unit collects energy consumption data from stores, and the analysis unit can set optimal prices based on the energy consumption data. For example, during times of high energy consumption, prices can be raised to cover energy costs. Also, during times of low energy consumption, prices can be lowered to attract customers. Furthermore, energy-efficient products can be promoted based on the energy consumption data. This makes it possible to set prices using energy consumption data, thereby optimizing energy costs for stores.

[0049] The provision unit can analyze a customer's purchase history and propose bundled sales of related products. For example, for a customer who has previously purchased a specific product, it can propose a bundle of products that go well with that product. It can also analyze a customer's purchasing patterns and offer bundles of frequently purchased products. It can also propose bundled sales according to seasons or events to increase customer purchasing motivation. This makes it possible to sell bundles that utilize a customer's purchase history, thereby increasing sales.

[0050] The collection unit collects data on customer movement patterns within the store, and the analysis unit can propose optimal product placement based on the movement pattern data. For example, popular products can be placed in areas where customers frequently pass through, maximizing sales. Also, based on the movement pattern data, promotional products can be placed in areas where customers tend to stay. Furthermore, the movement pattern data can be used to propose layout changes to avoid congestion. This makes it possible to utilize customer movement pattern data to optimally place products, improving customer satisfaction.

[0051] The provision unit can combine a customer's purchasing history with the current inventory status to propose optimal promotions. For example, if a customer has previously purchased a specific product, a discount can be offered if that product is in high stock. For products with low stock, a promotion can be proposed to encourage early purchase. Furthermore, based on the customer's purchasing history and inventory status, it can also propose bundle sales and set discounts. This makes it possible to conduct optimal promotions that utilize the customer's purchasing history and inventory status, thereby increasing sales.

[0052] The collection unit collects temperature and humidity data within the store, and the analysis unit can use this data to perform optimal product management. For example, when the temperature and humidity are high, the price of products for which freshness is important can be lowered to sell them off quickly. When the temperature and humidity are low, the price of products that can be stored for a long time can be adjusted. Furthermore, product placement and storage methods can be optimized based on the temperature and humidity data. This makes it possible to perform optimal product management using temperature and humidity data, thereby maintaining product quality and increasing sales.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection unit collects data on clearances, consumption and expiration dates, inventory status, purchase prices, prices at competing stores, store visit predictions, and flyer posting status. For example, the collection unit monitors inventory status in real time using sensors and cameras inside the store. The collection unit can also obtain purchase prices and prices at competing stores from online databases. Furthermore, the collection unit can predict store visits based on weather forecasts and past store visit data. Step 2: The analysis unit uses AI to analyze the data collected by the collection unit and calculate the optimal price. For example, the analysis unit uses machine learning algorithms to predict prices based on past sales data and current inventory status. The analysis unit can also use deep learning to set prices that take into account the prices of competing stores and predicted store visits. Step 3: The display unit displays the price calculated by the analysis unit on a digital price tag. For example, the display unit displays the price using electronic paper or an LCD display. The display unit can also set the price update frequency. Step 4: The provision unit provides the price information calculated by the analysis unit to the customer. For example, the provision unit provides the latest price information to the customer through a digital flyer. The provision unit can also provide the price information through a mobile app or website.

[0055] (Example 2) The dynamic pricing system according to an embodiment of the present invention utilizes digital price tags and product barcode management in stores. This system sets minimum and maximum prices for each product, collects data such as clearance dates, expiration dates, inventory status, purchase prices, prices at competing stores, store visit forecasts (number of customers, day of the week, time, weather, etc.), and flyer listing status, and then uses AI to analyze the data to calculate optimal prices. The calculated prices are displayed on digital price tags and provided to customers. For example, products with low inventory and close expiration dates are priced lower. Conversely, prices are set higher when store visit forecasts are high and competing stores' prices are higher. In this way, prices are dynamically adjusted. Furthermore, digital flyers provide customers with the latest price information. For example, when a specific product is discounted, this information is notified to customers via the digital flyer. This makes it easier for customers to keep track of the latest prices. Furthermore, the price displayed on the digital price tag is automatically updated at checkout. This eliminates the need for price confirmation at the register or the affixing of discount stickers, improving operational efficiency. This system alleviates the labor shortage in the retail industry. This reduces the burden on employees by eliminating the need for price tags and affixing discount stickers. In addition, automating pricing eliminates the need for price adjustments. Furthermore, providing customers with the latest price information through digital flyers improves customer satisfaction. In this way, the dynamic pricing system automates pricing in stores and improves operational efficiency.

[0056] A dynamic pricing system according to an embodiment includes a collection unit, an analysis unit, a display unit, and a provision unit. The collection unit collects data on clearances, consumption and expiration dates, inventory status, purchase prices, prices at competing stores, store visit forecasts, and flyer posting status. For example, the collection unit monitors inventory status in real time using in-store sensors and cameras. The collection unit can also obtain purchase prices and prices at competing stores from an online database. The collection unit can also predict store visits based on weather forecasts and past store visit data. The analysis unit uses AI to analyze the data collected by the collection unit and calculate optimal prices. For example, the analysis unit uses a machine learning algorithm to predict prices based on past sales data and current inventory status. The analysis unit can also use deep learning to set prices taking into account prices at competing stores and store visit forecasts. The display unit displays the prices calculated by the analysis unit on a digital price tag. For example, the display unit displays prices using an electronic paper or LCD display. The display unit can also set the price update frequency. The providing unit provides the price information calculated by the analysis unit to the customer. For example, the providing unit provides the latest price information to the customer through a digital flyer. The providing unit can also provide the price information through a mobile app or a website. As a result, the dynamic pricing system according to the embodiment can automate price setting in stores and improve business efficiency.

[0057] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce data collection, thereby reducing the user's burden. Furthermore, if the user is relaxed, the collection unit can collect detailed data and obtain more information. Furthermore, if the user is in a hurry, the collection unit can quickly collect only the necessary data and efficiently proceed with processing. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0058] The collection unit can analyze past data collection history and select a collection method. For example, the collection unit can identify the most efficient collection timing from the past data collection history and collect data at that timing. The collection unit can also adjust the type of data to be collected on a specific day of the week or time period based on the past data collection history. Furthermore, the collection unit can analyze the past data collection history and improve the collection method to increase the accuracy of the data. This enables efficient data collection by analyzing the past data collection history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the past data collection history into the generation AI and have the generation AI select the optimal collection method.

[0059] When collecting data, the collection unit can filter the data based on sales and seasonality of specific products. For example, the collection unit analyzes seasonal sales and prioritizes the collection of products that are in high demand during specific seasons. The collection unit can also prioritize the collection of high-selling products to optimize inventory management. Furthermore, the collection unit can filter and collect products that are in high demand during specific periods, taking seasonality into account. This enables more appropriate data collection by taking sales and seasonality into account. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input sales data into a generation AI and have the generation AI perform filtering.

[0060] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting only important data to reduce the user's burden. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data to enrich the information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly to efficiently proceed with processing. This enables efficient data collection by determining the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0061] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the store. For example, the collection unit can prioritize collecting data on products that are in high demand by taking into account the demographics of the area surrounding the store. The collection unit can also prioritize collecting data on products that are popular in a specific area by taking into account the location conditions of the store. Furthermore, the collection unit can prioritize collecting price information from competing stores near the store and reflect this in pricing. In this way, highly relevant data can be collected efficiently by taking geographical location information into account. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location data to a generation AI and cause the generation AI to select highly relevant data.

[0062] The collection unit can analyze social media activity and collect related data when collecting data. For example, the collection unit can prioritize collecting data on products that are trending on social media. The collection unit can also analyze social media trends and collect data on products that are likely to be in high demand. Furthermore, the collection unit can collect customer feedback on social media and use it to improve products. This analysis of social media activity improves the accuracy of demand forecasts. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media data into a generation AI and have the generation AI collect related data.

[0063] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is stressed, the analysis unit can use a simple analysis algorithm to quickly provide results. Alternatively, if the user is relaxed, the analysis unit can use a detailed analysis algorithm to provide highly accurate results. Furthermore, if the user is in a hurry, the analysis unit can adjust the analysis algorithm to quickly provide results. This allows for more appropriate analysis results to be provided by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0064] The analysis unit can adjust the level of detail of the analysis based on the importance of the product. For example, the analysis unit can perform a detailed analysis on products with high importance and provide highly accurate results. The analysis unit can also perform a simple analysis on products with low importance and provide results quickly. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the product. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the product. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0065] The analysis unit can apply different analysis algorithms depending on the product category. For example, for fresh food, the analysis unit can apply an analysis algorithm that emphasizes expiration dates and inventory status. For daily necessities, the analysis unit can also apply an analysis algorithm that emphasizes sales trends and prices at competing stores. Furthermore, for seasonal products, the analysis unit can apply an analysis algorithm that emphasizes seasonality and store visit predictions. In this way, applying an analysis algorithm depending on the product category improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0066] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. By adjusting the display method of the analysis results according to the user's emotions, a display that is easy for the user to view can be achieved. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0067] The analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data and adjusts prices in real time. The analysis unit can also perform analysis according to the current situation while referring to past data. Furthermore, the analysis unit can gradually adjust the priority of analysis based on the time when the data was collected. This enables real-time price adjustments by determining the priority of analysis based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input collection time data into the generation AI and have the generation AI determine the priority of analysis.

[0068] The analysis unit can improve the accuracy of the analysis by referring to related market data. The analysis unit can improve the accuracy of pricing by referring to price data of competing stores, for example. The analysis unit can also improve the accuracy of demand forecasting by referring to market trend data. Furthermore, the analysis unit can improve the accuracy of individual demand forecasting by referring to customer purchase history data. In this way, by referring to market data, the accuracy of pricing and demand forecasting is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input market data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0069] The display unit can estimate the user's emotions and adjust the display content based on the estimated user emotions. For example, when the user is stressed, the display unit provides simple, highly visible display content. Furthermore, when the user is relaxed, the display unit can provide display content including detailed information. Furthermore, when the user is in a hurry, the display unit can provide display content that focuses on the main points. This allows the display content to be adjusted according to the user's emotions, making it easier for the user to view. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0070] The display unit can adjust the level of detail of the display based on the sales of the product. For example, the display unit can display detailed information about a product that is selling well to appeal to customers. The display unit can also display concise information about a product that is not selling well to attract customers' attention. Furthermore, the display unit can gradually adjust the level of detail of the display according to the sales of the product. In this way, by adjusting the level of detail of the display according to the sales of the product, it is possible to provide customers with information that is useful to them. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input sales data to a generation AI and cause the generation AI to adjust the level of detail of the display.

[0071] The display unit can apply different display methods depending on the product category. For example, for fresh food, the display unit can apply a display method that emphasizes expiration dates and stock status. For daily necessities, the display unit can also apply a display method that emphasizes prices and discount information. For seasonal products, the display unit can also apply a display method that emphasizes seasonality and special promotions. By applying a display method depending on the product category, it is possible to provide a display that is easy for customers to view. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input product category data into the generation AI and cause the generation AI to apply different display methods.

[0072] The display unit can estimate the user's emotions and adjust the display order based on the estimated user emotions. For example, when the user is stressed, the display unit prioritizes displaying important information to reduce the user's burden. Furthermore, when the user is relaxed, the display unit can display detailed information in an orderly manner to deepen the user's understanding. Furthermore, when the user is in a hurry, the display unit can prioritize displaying key points to efficiently proceed with processing. This allows the display order to be adjusted according to the user's emotions, resulting in a display that is easy for the user to view. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0073] The display unit can determine display priorities based on the inventory status of the products. For example, the display unit can prioritize products with low inventory and sell them out quickly. The display unit can also display products with high inventory later and prioritize other products. Furthermore, the display unit can gradually adjust the display priorities according to the inventory status of the products. This enables efficient inventory management by determining the display priorities according to the inventory status. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input inventory status data to a generation AI and have the generation AI determine the display priorities.

[0074] The display unit can adjust the display content by referring to related promotional information. For example, if a specific product is on promotion, the display unit highlights that information when it is displayed. The display unit can also adjust the display content of related products by referring to the promotional information. Furthermore, the display unit can customize the display content based on the promotional information to appeal to customers. In this way, by referring to the promotional information, it is possible to provide customers with information that is useful to them. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the promotional information into a generation AI and have the generation AI adjust the display content.

[0075] The providing unit can estimate the user's emotions and adjust the content of the information to be provided based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can provide simple, highly visible information. Furthermore, when the user is relaxed, the providing unit can provide detailed information to deepen the user's understanding. Furthermore, when the user is in a hurry, the providing unit can provide information that focuses on the main points and efficiently proceed with processing. By adjusting the content of the information to be provided according to the user's emotions, useful information can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0076] The providing unit can analyze a customer's purchase history and select optimal information. For example, the providing unit can analyze a customer's past purchase history and provide information on related products. The providing unit can also analyze a customer's purchasing patterns and provide optimal promotion information. Furthermore, the providing unit can provide individually customized information based on the customer's purchase history. In this way, by analyzing a customer's purchase history, it is possible to provide information that is useful to the customer. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input purchase history data into a generation AI and cause the generation AI to select optimal information.

[0077] The providing unit can customize information based on the customer's current areas of interest. For example, the providing unit can prioritize providing information on products in which the customer is currently interested. The providing unit can also customize and provide information on related products based on the customer's areas of interest. Furthermore, the providing unit can analyze the customer's current areas of interest and provide optimal information. In this way, by customizing information based on the customer's areas of interest, it is possible to provide information that is useful to the customer. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input area of ​​interest data to a generating AI and have the generating AI customize the information.

[0078] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can prioritize providing important information to reduce the user's burden. Furthermore, when the user is relaxed, the providing unit can provide detailed information in an orderly manner to deepen the user's understanding. Furthermore, when the user is in a hurry, the providing unit can prioritize providing information that covers the main points to efficiently proceed with processing. This allows information to be prioritized according to the user's emotions, thereby providing useful information to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0079] The providing unit can select optimal information by taking into account the customer's geographical location information. For example, the providing unit can provide promotional information for nearby stores based on the customer's current location. The providing unit can also provide optimal product information by taking into account the customer's geographical location information. Furthermore, the providing unit can also provide area-limited special offers based on the customer's location information. This makes it possible to provide useful information to customers by taking into account the customer's geographical location information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input geographical location data into a generating AI and cause the generating AI to select optimal information.

[0080] The provision unit can analyze the customer's social media activity and provide related information. For example, the provision unit can analyze the customer's social media activity and provide information on products in which the customer is interested. The provision unit can also provide information on products that are trending on social media. Furthermore, the provision unit can provide related promotional information based on the customer's social media activity. In this way, by analyzing the customer's social media activity, it is possible to provide useful information to the customer. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input social media data into a generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, display unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit monitors inventory status using the camera 42 or sensors of the smart device 14, and acquires purchase prices and prices of competing stores from an online database using the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI and calculates the optimal price. The display unit, realized, for example, by the display 40A of the smart device 14, displays the calculated price on a digital price tag. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides customers with the latest price information via digital flyers or a mobile app. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, display unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit monitors inventory status using the camera 42 or sensors of the smart glasses 214, and obtains purchase prices and prices of competing stores from an online database using the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI and calculates the optimal price. The display unit, realized, for example, by the display of the smart glasses 214, displays the calculated price on a digital price tag. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides customers with the latest price information via digital flyers or a mobile app. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, display unit, and provision unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit monitors inventory status using the camera 42 or sensors of the headset terminal 314, and obtains purchase prices and prices of competing stores from an online database using the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI and calculates the optimal price. The display unit, realized, for example, by the display 343 of the headset terminal 314, displays the calculated price on a digital price tag. The provision unit, realized, for example, by the control unit 46A of the headset terminal 314, provides customers with the latest price information via digital flyers or a mobile app. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, display unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit monitors inventory status using the camera 42 and sensors of the robot 414, and obtains purchase prices and prices of competing stores from an online database using the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI and calculates the optimal price. The display unit, realized, for example, by the display of the robot 414, displays the calculated price on a digital price tag. The provision unit, realized, for example, by the control unit 46A of the robot 414, provides customers with the latest price information via digital flyers or a mobile app.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The analysis unit can adjust the pricing algorithm based on the product's life cycle stage. For example, during the product's introduction stage, promotional prices can be set to attract customer interest. During the growth stage, prices can be set according to demand to maximize profits. Furthermore, during the maturity stage, prices can be taken into account by competitors to maintain price competitiveness. This makes it possible to set prices according to the product's life cycle stage, thereby optimizing sales and profits.

[0083] The provision unit can estimate the user's emotions and adjust the timing of promotions based on the estimated user emotions. For example, if the user is feeling stressed, promotions for products that help the user relax can be provided preferentially. Also, if the user is relaxed, promotions for new products or high-priced products can be provided. Furthermore, if the user is in a hurry, promotions for products that can be purchased in a short time can be provided. This makes it possible to adjust the timing of promotions according to the user's emotions, improving customer satisfaction.

[0084] The collection unit collects energy consumption data from stores, and the analysis unit can set optimal prices based on the energy consumption data. For example, during times of high energy consumption, prices can be raised to cover energy costs. Also, during times of low energy consumption, prices can be lowered to attract customers. Furthermore, energy-efficient products can be promoted based on the energy consumption data. This makes it possible to set prices using energy consumption data, thereby optimizing energy costs for stores.

[0085] The analysis unit can estimate the user's emotions and adjust the price fluctuation range based on the estimated user emotions. For example, if the user is feeling stressed, the price fluctuation range can be reduced to provide a stable price. Alternatively, if the user is relaxed, the price fluctuation range can be increased to provide dynamic pricing. Furthermore, if the user is in a hurry, the price fluctuation range can be adjusted to provide a price that allows for quick purchase. This makes it possible to adjust the price fluctuation range according to the user's emotions, improving customer satisfaction.

[0086] The provision unit can analyze a customer's purchase history and propose bundled sales of related products. For example, for a customer who has previously purchased a specific product, it can propose a bundle of products that go well with that product. It can also analyze a customer's purchasing patterns and offer bundles of frequently purchased products. It can also propose bundled sales according to seasons or events to increase customer purchasing motivation. This makes it possible to sell bundles that utilize a customer's purchase history, thereby increasing sales.

[0087] The collection unit collects data on customer movement patterns within the store, and the analysis unit can propose optimal product placement based on the movement pattern data. For example, popular products can be placed in areas where customers frequently pass through, maximizing sales. Also, based on the movement pattern data, promotional products can be placed in areas where customers tend to stay. Furthermore, the movement pattern data can be used to propose layout changes to avoid congestion. This makes it possible to utilize customer movement pattern data to optimally place products, improving customer satisfaction.

[0088] The analysis unit can estimate the user's emotions and adjust the price display method based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible price display can be provided. If the user is relaxed, detailed price information can be displayed. Furthermore, if the user is in a hurry, a price display that focuses on the main points can be provided. This makes it possible to adjust the price display method according to the user's emotions, improving customer satisfaction.

[0089] The provision unit can combine a customer's purchasing history with the current inventory status to propose optimal promotions. For example, if a customer has previously purchased a specific product, a discount can be offered if that product is in high stock. For products with low stock, a promotion can be proposed to encourage early purchase. Furthermore, based on the customer's purchasing history and inventory status, it can also propose bundle sales and set discounts. This makes it possible to conduct optimal promotions that utilize the customer's purchasing history and inventory status, thereby increasing sales.

[0090] The collection unit collects temperature and humidity data within the store, and the analysis unit can use this data to perform optimal product management. For example, when the temperature and humidity are high, the price of products for which freshness is important can be lowered to sell them off quickly. When the temperature and humidity are low, the price of products that can be stored for a long time can be adjusted. Furthermore, product placement and storage methods can be optimized based on the temperature and humidity data. This makes it possible to perform optimal product management using temperature and humidity data, thereby maintaining product quality and increasing sales.

[0091] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user emotions. For example, if the user is feeling stressed, a concise, highly visible format can be provided. If the user is relaxed, a format including detailed information can be provided. Furthermore, if the user is in a hurry, a format that focuses on the main points can be provided. This makes it possible to adjust the information format according to the user's emotions, thereby improving customer satisfaction.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection unit collects data on clearances, consumption and expiration dates, inventory status, purchase prices, prices at competing stores, store visit predictions, and flyer posting status. For example, the collection unit monitors inventory status in real time using sensors and cameras inside the store. The collection unit can also obtain purchase prices and prices at competing stores from online databases. Furthermore, the collection unit can predict store visits based on weather forecasts and past store visit data. Step 2: The analysis unit uses AI to analyze the data collected by the collection unit and calculate the optimal price. For example, the analysis unit uses machine learning algorithms to predict prices based on past sales data and current inventory status. The analysis unit can also use deep learning to set prices that take into account the prices of competing stores and predicted store visits. Step 3: The display unit displays the price calculated by the analysis unit on a digital price tag. For example, the display unit displays the price using electronic paper or an LCD display. The display unit can also set the price update frequency. Step 4: The provision unit provides the price information calculated by the analysis unit to the customer. For example, the provision unit provides the latest price information to the customer through a digital flyer. The provision unit can also provide the price information through a mobile app or website.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0122] 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.

[0123] 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.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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. A collection department collects data on sales, consumption and expiration dates, stock status, purchase prices, prices of competing stores, store visit forecasts, and flyer publication status. an analysis unit that analyzes the data collected by the collection unit using AI and calculates a price; a display unit that displays the price calculated by the analysis unit on a digital price tag; a providing unit that provides the price information calculated by the analysis unit to a customer; Equipped with A system characterized by:

2. The collecting unit Estimate user emotions and adjust data collection timing based on the estimated user emotions The system of claim 1 .

3. The collecting unit Analyze past data collection history and select collection methods The system of claim 1 .

4. The collecting unit Filter data collection based on sales of specific products or seasonality The system of claim 1 .

5. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions The system of claim 1 .

6. The collecting unit When collecting data, prioritize the collection of relevant data by taking into account the geographic location of the store. The system of claim 1 .

7. The collecting unit During data collection, analyze social media activity and collect relevant data The system of claim 1 .

8. The analysis unit Inferring user emotions and adjusting analysis algorithms based on the estimated user emotions The system of claim 1 .

9. The analysis unit Adjust the analysis granularity based on the importance of the product The system of claim 1 .

10. The analysis unit Apply different analysis algorithms depending on the product category The system of claim 1 .

Images