system

Abstract

The system according to this embodiment aims to effectively appeal to users by automatically applying discounts based on inventory levels and remaining business hours. [Solution] The system according to this embodiment comprises a calculation unit, a discount unit, and a promotion unit. The calculation unit calculates the inventory quantity and the remaining business hours. The discount unit automatically applies discounts based on the information calculated by the calculation unit. The promotion unit promotes the discount information applied by the discount unit to the user in conjunction with a map service.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that an appropriate discount is not sufficiently automatically performed based on the inventory quantity and the remaining business hours, and effectively appealing to the user has not been fully achieved.

[0005] The system according to the embodiment aims to automatically perform a discount based on the inventory quantity and the remaining business hours and effectively appeal to the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a calculation unit, a discount unit, and a promotion unit. The calculation unit calculates the inventory quantity and the remaining business hours. The discount unit automatically applies discounts based on the information calculated by the calculation unit. The promotion unit promotes the discount information applied by the discount unit to the user in conjunction with a map service. [Effects of the Invention]

[0007] The system according to this embodiment can automatically apply discounts based on inventory levels and remaining business hours, effectively appealing to users. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system in which AI calculates remaining inventory and closing time, automatically applies discounts, and appeals to users who want to buy at a discount using a map service. This system is designed to prevent food waste that occurs in restaurants and supermarkets. First, the system's AI calculates the store's inventory and remaining closing time. For example, if there is little time left until closing and a lot of inventory, it identifies products that are likely to remain unsold. Next, the AI ​​automatically applies discounts. For example, it sets specific discount rates such as 20% off bento boxes, 10% off prepared foods, 20% off bread, and 50% off cakes. Furthermore, it works in conjunction with a map service to appeal to users with discount information. For example, it displays a "discount information map" on the map, allowing users to check what discounted products are available at stores near their current location. This allows users to buy dinner at a discount, and stores can reduce waste. As a result, the system reduces food waste and mitigates the environmental impact. Users can purchase products at a discount, increasing their satisfaction. Stores also benefit economically by reducing waste costs. As a concrete example, if a supermarket has a large amount of inventory remaining near closing time, the AI ​​can detect this inventory and apply an appropriate discount. This information is then communicated to users via a map service, allowing them to purchase items at a discount. This reduces waste for the store and provides a more satisfying shopping experience for users. The system caters to both B2B targets such as restaurants and supermarkets, and C2B targets such as general consumers who want to eat at a discount. By predicting waste rates based on weather and recent trends and applying appropriate discounts, the AI ​​can effectively prevent food waste.

[0029] The system according to this embodiment comprises a calculation unit, a discount unit, and a promotion unit. The calculation unit calculates the inventory quantity and the remaining business hours. For example, the calculation unit obtains inventory data from the store's inventory management system and calculates the remaining time until closing. The calculation unit can use AI to identify products that are likely to remain unsold based on the inventory quantity and remaining business hours. For example, the calculation unit analyzes past sales data and predicts products that will remain unsold during a specific time period. The calculation unit can also identify products that are likely to remain unsold by considering weather and event information. The discount unit automatically applies discounts based on the information calculated by the calculation unit. For example, the discount unit sets a certain discount rate for a specific product. The discount unit can use AI to dynamically adjust the discount rate according to the sales performance and inventory status of the products. For example, the discount unit sets a high discount rate for products that are not selling well and a low discount rate for products that are selling well. The discount unit can also implement discounts limited to specific time periods. The promotion unit promotes the discount information implemented by the discount unit to users in conjunction with a map service. The appeal function can, for example, display discount information on a map, allowing users to see what discounted items are available at stores near their current location. Using AI, the appeal function can provide personalized discount information based on the user's location and purchase history. For instance, it can display relevant discount information based on items the user has previously purchased. It can also prioritize displaying discount information from stores near the user's current location. This allows the system to calculate inventory levels and remaining hours, automatically apply discounts, and appeal to users in conjunction with map services.

[0030] The calculation unit calculates inventory levels and remaining operating hours. For example, the calculation unit obtains inventory data from the store's inventory management system and calculates the remaining time until closing. Specifically, the inventory management system updates the inventory levels of each product in real time, and the calculation unit periodically obtains this data. The remaining time until closing is calculated based on the store's operating hours information, for example, by determining the time difference from the current time to the closing time. The calculation unit can use AI to identify products that are likely to remain unsold based on inventory levels and remaining operating hours. The AI ​​analyzes past sales data and predicts products that will remain unsold during specific time periods. For example, it learns from past data that certain products tend to remain unsold in the afternoon on weekdays and makes predictions based on that information. The calculation unit can also identify products that are likely to remain unsold by considering weather and event information. For example, it predicts that demand for umbrellas and raincoats will increase on rainy days, while ice cream sales will decrease. This allows the calculation unit to make more accurate predictions by considering not only inventory levels and remaining operating hours, but also external factors. Furthermore, the calculation unit can provide information to optimize store inventory management and sales strategies based on these prediction results. For example, by offering early discounts on items that are likely to remain unsold, inventory turnover can be improved.

[0031] The discount unit automatically applies discounts based on information calculated by the calculation unit. For example, the discount unit sets a certain discount rate for specific products. Specifically, based on a list of products likely to remain unsold provided by the calculation unit, the discount unit determines the discount rate for each product. The discount unit can use AI to dynamically adjust discount rates according to product sales and inventory status. For example, the AI ​​monitors product sales in real time, setting higher discount rates for slow-selling products and lower discount rates for fast-selling products. The discount unit can also implement discounts limited to specific time periods. For example, significantly discounting specific products for the hour before closing can prevent unsold inventory. Furthermore, the discount unit can conduct special discount campaigns according to seasons and events. For example, discounting specific products during Christmas or New Year's sales can increase customer purchasing intent. The discount unit links this discount information with the store's POS system to enable automatic discounts at the register. This allows the discount unit to implement discounts efficiently and effectively, improving inventory turnover.

[0032] The promotion unit promotes discount information implemented by the discount unit to users in conjunction with a map service. For example, the promotion unit displays discount information on a map, allowing users to check what discounted items are available at stores near their current location. Specifically, the promotion unit integrates store location information and discount information to generate data for display on the map service. Users can access the map service using their smartphones or computers to check discount information being offered at stores near their current location. The promotion unit can use AI to provide personalized discount information based on the user's location information and purchase history. For example, the AI ​​analyzes data on products the user has purchased in the past and stores they have visited, and prioritizes displaying relevant discount information. The promotion unit can also prioritize displaying discount information from stores near the user's current location. This allows users to shop at the nearest and most convenient store to get the best deals. Furthermore, the promotion unit can also directly notify users of discount information via push notifications or email. For example, if a particular product is significantly discounted, sending a real-time notification to the user can increase their purchasing intent. This allows the sales team to effectively communicate discount information to users and improve store sales.

[0033] The forecasting unit can predict waste rates by considering weather and recent trends. For example, it can acquire weather data and predict waste rates based on changes in weather. The forecasting unit can use AI to learn the relationship between past weather data and waste rates and predict future waste rates. For example, the forecasting unit can predict that the waste rate of fresh food will be high on hot days and take appropriate measures. The forecasting unit can also analyze trend data from social media and search engines and predict waste rates based on recent trends. For example, if a particular product is trending on social media, the forecasting unit can predict that the waste rate of that product will be low and adjust inventory management accordingly. In this way, the forecasting unit can predict waste rates by considering weather and recent trends.

[0034] The identification unit can identify stores near the user's current location. For example, the identification unit can acquire GPS data to determine the user's current location. The identification unit can use AI to identify stores within a certain range from the user's current location. For example, the identification unit can identify stores within a 1-kilometer radius of the user's current location and provide discount information for those stores. The identification unit can also determine the user's current location using an IP address. For example, the identification unit can obtain approximate location information from the user's IP address and identify nearby stores based on that location information. In this way, the identification unit can identify stores close to the user's current location.

[0035] The display unit can display discount information on a map. For example, the display unit can integrate with a map service to display discount information on a map. The display unit can use AI to display personalized discount information based on the user's location and purchase history. For example, the display unit can display relevant discount information on a map based on products the user has previously purchased. Furthermore, the display unit can prioritize displaying discount information from stores close to the user's current location. This allows the display unit to display discount information on a map.

[0036] The calculation unit can identify products that are likely to remain unsold when there is little time left until closing and a large amount of inventory. For example, the calculation unit analyzes past sales data to predict products that will remain unsold during specific time periods. The calculation unit can use AI to identify products that are likely to remain unsold based on inventory levels and remaining business hours. For example, the calculation unit can identify products that are likely to remain unsold when there is little time left until closing and a large amount of inventory, and take appropriate measures. The calculation unit can also identify products that are likely to remain unsold by considering weather and event information. As a result, the calculation unit can identify products that are likely to remain unsold when there is little time left until closing and a large amount of inventory.

[0037] The discounting unit can set specific discount rates for designated products. For example, the discounting unit can set a fixed discount rate for specific products. The discounting unit can use AI to dynamically adjust discount rates according to product sales and inventory levels. For example, the discounting unit can set higher discount rates for slow-selling products and lower discount rates for fast-selling products. The discounting unit can also implement discounts limited to specific time periods. This allows the discounting unit to set specific discount rates for designated products.

[0038] The calculation unit can analyze past inventory data and predict inventory fluctuation patterns. For example, it can analyze inventory data from the past year to predict seasonal inventory fluctuation patterns. The calculation unit can use AI to analyze past inventory data and predict inventory fluctuation patterns. For example, it can predict inventory fluctuation patterns for specific days of the week or time slots based on past sales data. It can also analyze inventory data from past campaign periods to predict inventory fluctuations for the next campaign. In this way, the calculation unit can analyze past inventory data and predict inventory fluctuation patterns.

[0039] The calculation unit can improve the accuracy of inventory calculations by considering the expiration dates of products. For example, the calculation unit can prioritize calculating the inventory of products with approaching expiration dates and take measures to sell them off quickly. The calculation unit can use AI to improve the accuracy of inventory calculations by considering the expiration dates of products. For example, the calculation unit can calculate the inventory of products with longer expiration dates later, enabling efficient inventory management. The calculation unit can also calculate the inventory of multiple products with different expiration dates simultaneously and optimize the overall inventory balance. In this way, the calculation unit can improve the accuracy of inventory calculations by considering the expiration dates of products.

[0040] The calculation unit can take into account the geographical location of stores when calculating inventory levels. For example, in urban stores where inventory turnover is high, the calculation unit uses a rapid calculation method. The calculation unit can use AI to take into account the geographical location of stores when calculating inventory levels. For example, in suburban stores where inventory turnover is low, the calculation unit uses a detailed calculation method. Furthermore, in stores in tourist areas, the calculation unit can take into account seasonal inventory fluctuations. In this way, the calculation unit can calculate inventory levels while taking into account the geographical location of stores.

[0041] The calculation unit can improve the accuracy of inventory calculations by referring to the store's past sales data. For example, the calculation unit can predict inventory fluctuations for specific days of the week or time slots based on past sales data to improve calculation accuracy. The calculation unit can use AI to improve the accuracy of inventory calculations by referring to the store's past sales data. For example, the calculation unit can refer to sales data from past campaign periods and reflect this in inventory calculations for the next campaign. The calculation unit can also analyze past sales data and perform calculations considering seasonal inventory fluctuations. In this way, the calculation unit can improve the accuracy of inventory calculations by referring to the store's past sales data.

[0042] The discounting system can set discount rates based on the popularity of the product when discounting. For example, for popular products, the discounting system can set a lower discount rate to ensure profitability. The discounting system can use AI to set discount rates based on the popularity of the product when discounting. For example, for unpopular products, the discounting system can set a higher discount rate to clear inventory quickly. Also, for new products, the discounting system can set a moderate discount rate to stimulate purchasing intent. In this way, the discounting system can set discount rates based on the popularity of the product.

[0043] The discounting function can apply different discounting algorithms to each product category when discounting. For example, in the case of fresh food, the discounting function sets a higher discount rate for products nearing their expiration date. The discounting function can use AI to apply different discounting algorithms to each product category when discounting. For example, in the case of processed food, the discounting function sets an appropriate discount rate according to the inventory level. Also, in the case of daily necessities, the discounting function can set the discount rate considering the risk of unsold inventory. In this way, the discounting function can apply different discounting algorithms to each product category.

[0044] The discounting system can set discount rates based on the product's expiration date when discounting. For example, for products nearing their expiration date, the discounting system can set a higher discount rate to sell them off quickly. The discounting system can use AI to set discount rates based on the product's expiration date when discounting. For example, for products with a long expiration date, the discounting system can set a moderate discount rate to maintain inventory. Furthermore, the discounting system can set individual discount rates for multiple products with different expiration dates. This allows the discounting system to set discount rates based on the product's expiration date.

[0045] The discounting unit can optimize discount rates by referring to the store's past discount data when discounting prices. For example, the discounting unit can optimize discount rates for specific days of the week or time slots based on past discount data. The discounting unit can use AI to optimize discount rates by referring to the store's past discount data when discounting prices. For example, the discounting unit can refer to discount data from past campaign periods to optimize discount rates for the next campaign. The discounting unit can also analyze past discount data to optimize discount rates for each season. In this way, the discounting unit can optimize discount rates by referring to the store's past discount data.

[0046] The appeals unit can analyze a user's past purchase history to select the optimal appeal method at the time of appeal. For example, the appeals unit can select an appeal method for related products based on products the user has previously purchased. The appeals unit can use AI to analyze a user's past purchase history to select the optimal appeal method at the time of appeal. For example, the appeals unit can select an appeal method for specific days of the week or time slots based on the user's past purchase history. The appeals unit can also analyze a user's past purchase history to select the most effective appeal method. In this way, the appeals unit can analyze a user's past purchase history to select the optimal appeal method.

[0047] The appeal unit can customize its appeal content by considering the user's current location information when making an appeal. For example, the appeal unit can prioritize promoting products from stores close to the user's current location. The appeal unit can use AI to customize its appeal content by considering the user's current location information when making an appeal. For example, if the appeal unit is in a specific area, it can select an appeal method for products related to that area. The appeal unit can also customize the optimal appeal method based on the user's current location. In this way, the appeal unit can customize its appeal content by considering the user's current location information.

[0048] The appeal unit can select the optimal appeal method when making an appeal, taking into account the user's device information. For example, if the user is using a smartphone, the appeal unit will provide an appeal method that is adapted to the screen size. The appeal unit can use AI to select the optimal appeal method when making an appeal, taking into account the user's device information. For example, if the user is using a tablet, the appeal unit will provide an appeal method optimized for a large screen. Furthermore, if the user is using a smartwatch, the appeal unit can provide a concise and highly visible appeal method. In this way, the appeal unit can select the optimal appeal method, taking into account the user's device information.

[0049] The appeals department can analyze users' social media activity and customize the appeal content at the time of appealing. For example, the appeals department can customize the appeal content based on products that users have shown interest in on social media. The appeals department can use AI to analyze users' social media activity and customize the appeal content at the time of appealing. For example, the appeals department can select an appeal method for products related to a specific trend based on the user's social media activity. The appeals department can also analyze users' social media activity and customize the most effective appeal content. In this way, the appeals department can analyze users' social media activity and customize the appeal content.

[0050] The forecasting unit can predict the waste rate by referring to past weather data during the forecasting process. For example, the forecasting unit can analyze weather data from the past year to predict the waste rate for each season. The forecasting unit can use AI to predict the waste rate by referring to past weather data during the forecasting process. For example, the forecasting unit can predict the waste rate under specific weather conditions based on past weather data. The forecasting unit can also refer to past weather data and predict the waste rate based on the next weather conditions. In this way, the forecasting unit can predict the waste rate by referring to past weather data.

[0051] The prediction unit can predict the waste rate by analyzing recent trend data during the prediction process. For example, the prediction unit can predict the waste rate for a specific product category based on recent trend data. The prediction unit can use AI to predict the waste rate by analyzing recent trend data during the prediction process. For example, the prediction unit analyzes recent trend data and predicts the waste rate at a specific time. The prediction unit can also refer to recent trend data and predict the waste rate based on the next trend. In this way, the prediction unit can predict the waste rate by analyzing recent trend data.

[0052] The prediction unit can predict waste rates by considering the geographical location of stores during the prediction process. For example, the prediction unit uses a rapid prediction method for stores in urban areas where waste rates are high. The prediction unit can use AI to predict waste rates by considering the geographical location of stores during the prediction process. For example, the prediction unit uses a detailed prediction method for stores in suburban areas where waste rates are low. Furthermore, the prediction unit can also make predictions by considering seasonal waste rates for stores in tourist areas. In this way, the prediction unit can predict waste rates by considering the geographical location of stores.

[0053] The prediction unit can predict the waste rate by referring to the store's past sales data during the prediction process. For example, the prediction unit can predict the waste rate for specific days of the week or time slots based on past sales data. The prediction unit can use AI to predict the waste rate by referring to the store's past sales data during the prediction process. For example, the prediction unit can refer to sales data from past campaign periods to predict the waste rate for the next campaign. The prediction unit can also analyze past sales data and make predictions considering seasonal waste rates. In this way, the prediction unit can predict the waste rate by referring to the store's past sales data.

[0054] The identification unit can identify the optimal store by analyzing the user's past visit history at a specific time. For example, the identification unit can identify the optimal store based on the stores the user has visited in the past. The identification unit can use AI to identify the optimal store by analyzing the user's past visit history at a specific time. For example, the identification unit can identify stores that the user visits on specific days of the week or at specific times based on the user's past visit history. The identification unit can also analyze the user's past visit history to identify the most efficient store. In this way, the identification unit can identify the optimal store by analyzing the user's past visit history.

[0055] The identification unit can identify the optimal store by considering the user's device information at the time of identification. For example, if the user is using a smartphone, the identification unit provides a store identification method that is adapted to the screen size. The identification unit can use AI to identify the optimal store by considering the user's device information at the time of identification. For example, if the user is using a tablet, the identification unit provides a store identification method optimized for a large screen. Furthermore, if the user is using a smartwatch, the identification unit can also provide a concise and highly visible store identification method. In this way, the identification unit can identify the optimal store by considering the user's device information.

[0056] The display unit can select the optimal display method by referring to the user's past purchase history when displaying information. For example, the display unit can select how to display related products based on products the user has previously purchased. The display unit can also use AI to select the optimal display method by referring to the user's past purchase history when displaying information. For example, the display unit can select how to display information on specific days of the week or time slots based on the user's past purchase history. Furthermore, the display unit can analyze the user's past purchase history and select the most effective display method. In this way, the display unit can select the optimal display method by referring to the user's past purchase history.

[0057] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can use AI to select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a tablet, the display unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the display unit can select the optimal display method, taking into account the user's device information.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The system can also include a history analysis unit that analyzes the user's purchase history. This unit collects data on products the user has purchased in the past and analyzes their purchasing patterns. For example, if the history analysis unit finds that a user tends to purchase certain products on specific days or times, it can use this information to predict future purchases. Furthermore, the history analysis unit can identify products that users frequently purchase and offer special discounts or promotions for those products. In addition, the history analysis unit can recommend related products based on the user's purchase history. This allows the system to leverage the user's purchase history to provide more personalized services.

[0060] The system can also include a feedback collection unit to gather user feedback. This unit collects feedback from users about their satisfaction with and experience using the products they purchased. For example, it could send a survey to users after purchase, requesting their evaluation of the product. Furthermore, the feedback collection unit can analyze the feedback provided by users to improve product quality and services. It can also use user feedback to offer special offers or discounts on future purchases. This allows the system to leverage user feedback to provide better service.

[0061] The system can also include a prediction unit that makes predictions based on the user's purchase history. For example, the prediction unit can predict the next purchase based on data of products the user has purchased in the past. The prediction unit can use AI to analyze the user's purchase history and make predictions for the next purchase. For example, if the prediction unit finds that a user tends to purchase certain products on certain days or times, it can use that information to predict the next purchase. The prediction unit can also identify products that the user frequently purchases and offer special offers for those products. In this way, the prediction unit can leverage the user's purchase history to provide more personalized services.

[0062] The system can also include a weather analysis unit that combines and analyzes user purchase history with weather data. For example, the weather analysis unit can analyze trends in products users purchase under specific weather conditions. The weather analysis unit can use AI to combine and analyze user purchase history and weather data. For instance, it could identify trends in products purchased on rainy days and offer special offers for those products. It could also analyze trends in products purchased on sunny days and provide recommendations based on that information. This allows the weather analysis unit to combine user purchase history and weather data to provide more personalized services.

[0063] The system can also include a social analytics unit that combines and analyzes user purchase history and social media data. For example, the social analytics unit can analyze products a user has shown interest in on social media in combination with their purchase history. The social analytics unit can use AI to combine and analyze user purchase history and social media data. For instance, it can identify products a user is discussing on social media and offer special offers for those products. Furthermore, the social analytics unit can recommend relevant products based on the user's social media activity. This allows the social analytics unit to provide more personalized services by combining user purchase history and social media data.

[0064] The system can also include a location analysis unit that combines and analyzes the user's purchase history and location information. For example, the location analysis unit can analyze the trends in products a user purchases in specific locations. The location analysis unit can use AI to combine and analyze the user's purchase history and location information. For instance, it can identify products a user frequently purchases at specific stores and offer special offers for those products. Furthermore, the location analysis unit can analyze the trends in products a user purchases in specific areas and provide recommendations based on that information. This allows the location analysis unit to combine the user's purchase history and location information to provide more personalized services.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The calculation unit calculates inventory levels and remaining operating hours. The calculation unit retrieves inventory data from the store's inventory management system and calculates the remaining time until closing. Furthermore, it uses AI to identify products that are likely to remain unsold based on inventory levels and remaining operating hours. For example, it analyzes past sales data to predict products that will remain unsold during specific time periods. It can also identify products that are likely to remain unsold by considering weather and event information. Step 2: The discount unit automatically applies discounts based on the information calculated by the calculation unit. The discount unit sets a certain discount rate for specific products and uses AI to dynamically adjust the discount rate according to the sales performance and inventory status of the products. For example, it sets a higher discount rate for products with poor sales and a lower discount rate for products with good sales. It can also implement discounts that are limited to specific time periods. Step 3: The promotional unit promotes discount information implemented by the discounting unit to users in conjunction with a map service. The promotional unit displays the discount information on the map, allowing users to see what discounted items are available at stores near their current location. Furthermore, it uses AI to provide personalized discount information based on the user's location and purchase history. For example, it displays relevant discount information based on products the user has purchased in the past and prioritizes displaying discount information from stores near the user's current location.

[0067] (Example of form 2) The system according to an embodiment of the present invention is a system in which AI calculates remaining inventory and closing time, automatically applies discounts, and appeals to users who want to buy at a discount using a map service. This system is designed to prevent food waste that occurs in restaurants and supermarkets. First, the system's AI calculates the store's inventory and remaining closing time. For example, if there is little time left until closing and a lot of inventory, it identifies products that are likely to remain unsold. Next, the AI ​​automatically applies discounts. For example, it sets specific discount rates such as 20% off bento boxes, 10% off prepared foods, 20% off bread, and 50% off cakes. Furthermore, it works in conjunction with a map service to appeal to users with discount information. For example, it displays a "discount information map" on the map, allowing users to check what discounted products are available at stores near their current location. This allows users to buy dinner at a discount, and stores can reduce waste. As a result, the system reduces food waste and mitigates the environmental impact. Users can purchase products at a discount, increasing their satisfaction. Stores also benefit economically by reducing waste costs. As a concrete example, if a supermarket has a large amount of inventory remaining near closing time, the AI ​​can detect this inventory and apply an appropriate discount. This information is then communicated to users via a map service, allowing them to purchase items at a discount. This reduces waste for the store and provides a more satisfying shopping experience for users. The system caters to both B2B targets such as restaurants and supermarkets, and C2B targets such as general consumers who want to eat at a discount. By predicting waste rates based on weather and recent trends and applying appropriate discounts, the AI ​​can effectively prevent food waste.

[0068] The system according to this embodiment comprises a calculation unit, a discount unit, and a promotion unit. The calculation unit calculates the inventory quantity and the remaining business hours. For example, the calculation unit obtains inventory data from the store's inventory management system and calculates the remaining time until closing. The calculation unit can use AI to identify products that are likely to remain unsold based on the inventory quantity and remaining business hours. For example, the calculation unit analyzes past sales data and predicts products that will remain unsold during a specific time period. The calculation unit can also identify products that are likely to remain unsold by considering weather and event information. The discount unit automatically applies discounts based on the information calculated by the calculation unit. For example, the discount unit sets a certain discount rate for a specific product. The discount unit can use AI to dynamically adjust the discount rate according to the sales performance and inventory status of the products. For example, the discount unit sets a high discount rate for products that are not selling well and a low discount rate for products that are selling well. The discount unit can also implement discounts limited to specific time periods. The promotion unit promotes the discount information implemented by the discount unit to users in conjunction with a map service. The appeal function can, for example, display discount information on a map, allowing users to see what discounted items are available at stores near their current location. Using AI, the appeal function can provide personalized discount information based on the user's location and purchase history. For instance, it can display relevant discount information based on items the user has previously purchased. It can also prioritize displaying discount information from stores near the user's current location. This allows the system to calculate inventory levels and remaining hours, automatically apply discounts, and appeal to users in conjunction with map services.

[0069] The calculation unit calculates inventory levels and remaining operating hours. For example, the calculation unit obtains inventory data from the store's inventory management system and calculates the remaining time until closing. Specifically, the inventory management system updates the inventory levels of each product in real time, and the calculation unit periodically obtains this data. The remaining time until closing is calculated based on the store's operating hours information, for example, by determining the time difference from the current time to the closing time. The calculation unit can use AI to identify products that are likely to remain unsold based on inventory levels and remaining operating hours. The AI ​​analyzes past sales data and predicts products that will remain unsold during specific time periods. For example, it learns from past data that certain products tend to remain unsold in the afternoon on weekdays and makes predictions based on that information. The calculation unit can also identify products that are likely to remain unsold by considering weather and event information. For example, it predicts that demand for umbrellas and raincoats will increase on rainy days, while ice cream sales will decrease. This allows the calculation unit to make more accurate predictions by considering not only inventory levels and remaining operating hours, but also external factors. Furthermore, the calculation unit can provide information to optimize store inventory management and sales strategies based on these prediction results. For example, by offering early discounts on items that are likely to remain unsold, inventory turnover can be improved.

[0070] The discount unit automatically applies discounts based on information calculated by the calculation unit. For example, the discount unit sets a certain discount rate for specific products. Specifically, based on a list of products likely to remain unsold provided by the calculation unit, the discount unit determines the discount rate for each product. The discount unit can use AI to dynamically adjust discount rates according to product sales and inventory status. For example, the AI ​​monitors product sales in real time, setting higher discount rates for slow-selling products and lower discount rates for fast-selling products. The discount unit can also implement discounts limited to specific time periods. For example, significantly discounting specific products for the hour before closing can prevent unsold inventory. Furthermore, the discount unit can conduct special discount campaigns according to seasons and events. For example, discounting specific products during Christmas or New Year's sales can increase customer purchasing intent. The discount unit links this discount information with the store's POS system to enable automatic discounts at the register. This allows the discount unit to implement discounts efficiently and effectively, improving inventory turnover.

[0071] The promotion unit promotes discount information implemented by the discount unit to users in conjunction with a map service. For example, the promotion unit displays discount information on a map, allowing users to check what discounted items are available at stores near their current location. Specifically, the promotion unit integrates store location information and discount information to generate data for display on the map service. Users can access the map service using their smartphones or computers to check discount information being offered at stores near their current location. The promotion unit can use AI to provide personalized discount information based on the user's location information and purchase history. For example, the AI ​​analyzes data on products the user has purchased in the past and stores they have visited, and prioritizes displaying relevant discount information. The promotion unit can also prioritize displaying discount information from stores near the user's current location. This allows users to shop at the nearest and most convenient store to get the best deals. Furthermore, the promotion unit can also directly notify users of discount information via push notifications or email. For example, if a particular product is significantly discounted, sending a real-time notification to the user can increase their purchasing intent. This allows the sales team to effectively communicate discount information to users and improve store sales.

[0072] The forecasting unit can predict waste rates by considering weather and recent trends. For example, it can acquire weather data and predict waste rates based on changes in weather. The forecasting unit can use AI to learn the relationship between past weather data and waste rates and predict future waste rates. For example, the forecasting unit can predict that the waste rate of fresh food will be high on hot days and take appropriate measures. The forecasting unit can also analyze trend data from social media and search engines and predict waste rates based on recent trends. For example, if a particular product is trending on social media, the forecasting unit can predict that the waste rate of that product will be low and adjust inventory management accordingly. In this way, the forecasting unit can predict waste rates by considering weather and recent trends.

[0073] The identification unit can identify stores near the user's current location. For example, the identification unit can acquire GPS data to determine the user's current location. The identification unit can use AI to identify stores within a certain range from the user's current location. For example, the identification unit can identify stores within a 1-kilometer radius of the user's current location and provide discount information for those stores. The identification unit can also determine the user's current location using an IP address. For example, the identification unit can obtain approximate location information from the user's IP address and identify nearby stores based on that location information. In this way, the identification unit can identify stores close to the user's current location.

[0074] The display unit can display discount information on a map. For example, the display unit can integrate with a map service to display discount information on a map. The display unit can use AI to display personalized discount information based on the user's location and purchase history. For example, the display unit can display relevant discount information on a map based on products the user has previously purchased. Furthermore, the display unit can prioritize displaying discount information from stores close to the user's current location. This allows the display unit to display discount information on a map.

[0075] The calculation unit can identify products that are likely to remain unsold when there is little time left until closing and a large amount of inventory. For example, the calculation unit analyzes past sales data to predict products that will remain unsold during specific time periods. The calculation unit can use AI to identify products that are likely to remain unsold based on inventory levels and remaining business hours. For example, the calculation unit can identify products that are likely to remain unsold when there is little time left until closing and a large amount of inventory, and take appropriate measures. The calculation unit can also identify products that are likely to remain unsold by considering weather and event information. As a result, the calculation unit can identify products that are likely to remain unsold when there is little time left until closing and a large amount of inventory.

[0076] The discounting unit can set specific discount rates for designated products. For example, the discounting unit can set a fixed discount rate for specific products. The discounting unit can use AI to dynamically adjust discount rates according to product sales and inventory levels. For example, the discounting unit can set higher discount rates for slow-selling products and lower discount rates for fast-selling products. The discounting unit can also implement discounts limited to specific time periods. This allows the discounting unit to set specific discount rates for designated products.

[0077] The calculation unit can estimate the user's emotions and adjust the inventory calculation method based on the estimated emotions. For example, if the user is stressed, the calculation unit can quickly calculate the inventory using a simple calculation method. The calculation unit can use AI to estimate the user's emotions and adjust the inventory calculation method based on the estimated emotions. For example, if the user is relaxed, the calculation unit can accurately calculate the inventory using a detailed calculation method. Also, if the user is in a hurry, the calculation unit can quickly estimate the inventory based on past data. This allows the calculation unit to adjust the inventory calculation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The calculation unit can analyze past inventory data and predict inventory fluctuation patterns. For example, it can analyze inventory data from the past year to predict seasonal inventory fluctuation patterns. The calculation unit can use AI to analyze past inventory data and predict inventory fluctuation patterns. For example, it can predict inventory fluctuation patterns for specific days of the week or time slots based on past sales data. It can also analyze inventory data from past campaign periods to predict inventory fluctuations for the next campaign. In this way, the calculation unit can analyze past inventory data and predict inventory fluctuation patterns.

[0079] The calculation unit can improve the accuracy of inventory calculations by considering the expiration dates of products. For example, the calculation unit can prioritize calculating the inventory of products with approaching expiration dates and take measures to sell them off quickly. The calculation unit can use AI to improve the accuracy of inventory calculations by considering the expiration dates of products. For example, the calculation unit can calculate the inventory of products with longer expiration dates later, enabling efficient inventory management. The calculation unit can also calculate the inventory of multiple products with different expiration dates simultaneously and optimize the overall inventory balance. In this way, the calculation unit can improve the accuracy of inventory calculations by considering the expiration dates of products.

[0080] The calculation unit can estimate the user's emotions and adjust the display method of inventory based on the estimated emotions. For example, if the user is nervous, the calculation unit provides a simple and highly visible display method. The calculation unit can use AI to estimate the user's emotions and adjust the display method of inventory based on the estimated emotions. For example, if the user is relaxed, the calculation unit provides a display method that includes detailed information. The calculation unit can also provide a concise display method if the user is in a hurry. In this way, the calculation unit can adjust the display method of inventory based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The calculation unit can take into account the geographical location of stores when calculating inventory levels. For example, in urban stores where inventory turnover is high, the calculation unit uses a rapid calculation method. The calculation unit can use AI to take into account the geographical location of stores when calculating inventory levels. For example, in suburban stores where inventory turnover is low, the calculation unit uses a detailed calculation method. Furthermore, in stores in tourist areas, the calculation unit can take into account seasonal inventory fluctuations. In this way, the calculation unit can calculate inventory levels while taking into account the geographical location of stores.

[0082] The calculation unit can improve the accuracy of inventory calculations by referring to the store's past sales data. For example, the calculation unit can predict inventory fluctuations for specific days of the week or time slots based on past sales data to improve calculation accuracy. The calculation unit can use AI to improve the accuracy of inventory calculations by referring to the store's past sales data. For example, the calculation unit can refer to sales data from past campaign periods and reflect this in inventory calculations for the next campaign. The calculation unit can also analyze past sales data and perform calculations considering seasonal inventory fluctuations. In this way, the calculation unit can improve the accuracy of inventory calculations by referring to the store's past sales data.

[0083] The discounting unit can estimate the user's emotions and adjust the discount rate based on those emotions. For example, if the user is stressed, the discounting unit can set a higher discount rate to stimulate their desire to buy. The discounting unit can use AI to estimate the user's emotions and adjust the discount rate based on those emotions. For example, if the user is relaxed, the discounting unit can set a moderate discount rate to maintain their desire to buy. Also, if the user is in a hurry, the discounting unit can quickly provide discount information to increase their desire to buy. In this way, the discounting unit can adjust the discount rate based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The discounting system can set discount rates based on the popularity of the product when discounting. For example, for popular products, the discounting system can set a lower discount rate to ensure profitability. The discounting system can use AI to set discount rates based on the popularity of the product when discounting. For example, for unpopular products, the discounting system can set a higher discount rate to clear inventory quickly. Also, for new products, the discounting system can set a moderate discount rate to stimulate purchasing intent. In this way, the discounting system can set discount rates based on the popularity of the product.

[0085] The discounting function can apply different discounting algorithms to each product category when discounting. For example, in the case of fresh food, the discounting function sets a higher discount rate for products nearing their expiration date. The discounting function can use AI to apply different discounting algorithms to each product category when discounting. For example, in the case of processed food, the discounting function sets an appropriate discount rate according to the inventory level. Also, in the case of daily necessities, the discounting function can set the discount rate considering the risk of unsold inventory. In this way, the discounting function can apply different discounting algorithms to each product category.

[0086] The discount section can estimate the user's emotions and adjust how discount information is displayed based on those emotions. For example, if the user is nervous, the discount section provides a simple and highly visible display method. The discount section can use AI to estimate the user's emotions and adjust how discount information is displayed based on those emotions. For example, if the user is relaxed, the discount section provides a display method that includes detailed information. Also, if the user is in a hurry, the discount section can provide a display method that gets straight to the point. In this way, the discount section can adjust how discount information is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The discounting system can set discount rates based on the product's expiration date when discounting. For example, for products nearing their expiration date, the discounting system can set a higher discount rate to sell them off quickly. The discounting system can use AI to set discount rates based on the product's expiration date when discounting. For example, for products with a long expiration date, the discounting system can set a moderate discount rate to maintain inventory. Furthermore, the discounting system can set individual discount rates for multiple products with different expiration dates. This allows the discounting system to set discount rates based on the product's expiration date.

[0088] The discounting unit can optimize discount rates by referring to the store's past discount data when discounting prices. For example, the discounting unit can optimize discount rates for specific days of the week or time slots based on past discount data. The discounting unit can use AI to optimize discount rates by referring to the store's past discount data when discounting prices. For example, the discounting unit can refer to discount data from past campaign periods to optimize discount rates for the next campaign. The discounting unit can also analyze past discount data to optimize discount rates for each season. In this way, the discounting unit can optimize discount rates by referring to the store's past discount data.

[0089] The appeal unit can estimate the user's emotions and adjust its appeal based on those emotions. For example, if the user is stressed, the appeal unit can provide a simple and highly visible appeal. The appeal unit can use AI to estimate the user's emotions and adjust its appeal based on those emotions. For example, if the user is relaxed, the appeal unit can provide an appeal that includes detailed information. Alternatively, if the user is in a hurry, the appeal unit can provide a concise appeal. This allows the appeal unit to adjust its appeal based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The appeals unit can analyze a user's past purchase history to select the optimal appeal method at the time of appeal. For example, the appeals unit can select an appeal method for related products based on products the user has previously purchased. The appeals unit can use AI to analyze a user's past purchase history to select the optimal appeal method at the time of appeal. For example, the appeals unit can select an appeal method for specific days of the week or time slots based on the user's past purchase history. The appeals unit can also analyze a user's past purchase history to select the most effective appeal method. In this way, the appeals unit can analyze a user's past purchase history to select the optimal appeal method.

[0091] The appeal unit can customize its appeal content by considering the user's current location information when making an appeal. For example, the appeal unit can prioritize promoting products from stores close to the user's current location. The appeal unit can use AI to customize its appeal content by considering the user's current location information when making an appeal. For example, if the appeal unit is in a specific area, it can select an appeal method for products related to that area. The appeal unit can also customize the optimal appeal method based on the user's current location. In this way, the appeal unit can customize its appeal content by considering the user's current location information.

[0092] The appeal unit can estimate the user's emotions and adjust the display order of appeal information based on the estimated emotions. For example, if the user is nervous, the appeal unit provides a simple and highly visible display order. The appeal unit can use AI to estimate the user's emotions and adjust the display order of appeal information based on the estimated emotions. For example, if the user is relaxed, the appeal unit provides a display order that includes detailed information. Also, if the user is in a hurry, the appeal unit can provide a display order that gets straight to the point. In this way, the appeal unit can adjust the display order of appeal information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The appeal unit can select the optimal appeal method when making an appeal, taking into account the user's device information. For example, if the user is using a smartphone, the appeal unit will provide an appeal method that is adapted to the screen size. The appeal unit can use AI to select the optimal appeal method when making an appeal, taking into account the user's device information. For example, if the user is using a tablet, the appeal unit will provide an appeal method optimized for a large screen. Furthermore, if the user is using a smartwatch, the appeal unit can provide a concise and highly visible appeal method. In this way, the appeal unit can select the optimal appeal method, taking into account the user's device information.

[0094] The appeals department can analyze users' social media activity and customize the appeal content at the time of appealing. For example, the appeals department can customize the appeal content based on products that users have shown interest in on social media. The appeals department can use AI to analyze users' social media activity and customize the appeal content at the time of appealing. For example, the appeals department can select an appeal method for products related to a specific trend based on the user's social media activity. The appeals department can also analyze users' social media activity and customize the most effective appeal content. In this way, the appeals department can analyze users' social media activity and customize the appeal content.

[0095] The prediction unit can estimate the user's emotions and adjust its discard rate prediction method based on the estimated emotions. For example, if the user is stressed, the prediction unit can quickly predict the discard rate using a simple prediction method. The prediction unit can use AI to estimate the user's emotions and adjust its discard rate prediction method based on the estimated emotions. For example, if the user is relaxed, the prediction unit can accurately predict the discard rate using a detailed prediction method. The prediction unit can also quickly estimate the discard rate based on past data if the user is in a hurry. This allows the prediction unit to adjust its discard rate prediction method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The forecasting unit can predict the waste rate by referring to past weather data during the forecasting process. For example, the forecasting unit can analyze weather data from the past year to predict the waste rate for each season. The forecasting unit can use AI to predict the waste rate by referring to past weather data during the forecasting process. For example, the forecasting unit can predict the waste rate under specific weather conditions based on past weather data. The forecasting unit can also refer to past weather data and predict the waste rate based on the next weather conditions. In this way, the forecasting unit can predict the waste rate by referring to past weather data.

[0097] The prediction unit can predict the waste rate by analyzing recent trend data during the prediction process. For example, the prediction unit can predict the waste rate for a specific product category based on recent trend data. The prediction unit can use AI to predict the waste rate by analyzing recent trend data during the prediction process. For example, the prediction unit analyzes recent trend data and predicts the waste rate at a specific time. The prediction unit can also refer to recent trend data and predict the waste rate based on the next trend. In this way, the prediction unit can predict the waste rate by analyzing recent trend data.

[0098] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is nervous, the prediction unit provides a simple and highly visible display method. The prediction unit can use AI to estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is relaxed, the prediction unit provides a display method that includes detailed information. The prediction unit can also provide a concise display method if the user is in a hurry. In this way, the prediction unit can adjust the display method of the prediction results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The prediction unit can predict waste rates by considering the geographical location of stores during the prediction process. For example, the prediction unit uses a rapid prediction method for stores in urban areas where waste rates are high. The prediction unit can use AI to predict waste rates by considering the geographical location of stores during the prediction process. For example, the prediction unit uses a detailed prediction method for stores in suburban areas where waste rates are low. Furthermore, the prediction unit can also make predictions by considering seasonal waste rates for stores in tourist areas. In this way, the prediction unit can predict waste rates by considering the geographical location of stores.

[0100] The prediction unit can predict the waste rate by referring to the store's past sales data during the prediction process. For example, the prediction unit can predict the waste rate for specific days of the week or time slots based on past sales data. The prediction unit can use AI to predict the waste rate by referring to the store's past sales data during the prediction process. For example, the prediction unit can refer to sales data from past campaign periods to predict the waste rate for the next campaign. The prediction unit can also analyze past sales data and make predictions considering seasonal waste rates. In this way, the prediction unit can predict the waste rate by referring to the store's past sales data.

[0101] The identification unit can estimate the user's emotions and adjust its store identification method based on the estimated emotions. For example, if the user is stressed, the identification unit can quickly identify a store using a simple identification method. The identification unit can use AI to estimate the user's emotions and adjust its store identification method based on the estimated emotions. For example, if the user is relaxed, the identification unit can accurately identify a store using a detailed identification method. Also, if the user is in a hurry, the identification unit can quickly identify a store based on past data. This allows the identification unit to adjust its store identification method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The identification unit can identify the optimal store by analyzing the user's past visit history at a specific time. For example, the identification unit can identify the optimal store based on the stores the user has visited in the past. The identification unit can use AI to identify the optimal store by analyzing the user's past visit history at a specific time. For example, the identification unit can identify stores that the user visits on specific days of the week or at specific times based on the user's past visit history. The identification unit can also analyze the user's past visit history to identify the most efficient store. In this way, the identification unit can identify the optimal store by analyzing the user's past visit history.

[0103] The identification unit can estimate the user's emotions and adjust the display method of the identified store based on the estimated user emotions. For example, if the user is nervous, the identification unit provides a simple and highly visible display method. The identification unit can use AI to estimate the user's emotions and adjust the display method of the identified store based on the estimated user emotions. For example, if the user is relaxed, the identification unit provides a display method that includes detailed information. Also, if the user is in a hurry, the identification unit can provide a concise display method. In this way, the identification unit can adjust the display method of the identified store based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The identification unit can identify the optimal store by considering the user's device information at the time of identification. For example, if the user is using a smartphone, the identification unit provides a store identification method that is adapted to the screen size. The identification unit can use AI to identify the optimal store by considering the user's device information at the time of identification. For example, if the user is using a tablet, the identification unit provides a store identification method optimized for a large screen. Furthermore, if the user is using a smartwatch, the identification unit can also provide a concise and highly visible store identification method. In this way, the identification unit can identify the optimal store by considering the user's device information.

[0105] The display unit can estimate the user's emotions and adjust the way discount information is displayed based on those emotions. For example, if the user is nervous, the display unit provides a simple and highly visible display method. The display unit can use AI to estimate the user's emotions and adjust the way discount information is displayed based on those emotions. For example, if the user is relaxed, the display unit provides a display method that includes detailed information. The display unit can also provide a concise display method if the user is in a hurry. In this way, the display unit can adjust the way discount information is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The display unit can select the optimal display method by referring to the user's past purchase history when displaying information. For example, the display unit can select how to display related products based on products the user has previously purchased. The display unit can also use AI to select the optimal display method by referring to the user's past purchase history when displaying information. For example, the display unit can select how to display information on specific days of the week or time slots based on the user's past purchase history. Furthermore, the display unit can analyze the user's past purchase history and select the most effective display method. In this way, the display unit can select the optimal display method by referring to the user's past purchase history.

[0107] The display unit can estimate the user's emotions and prioritize the displayed information based on those emotions. For example, if the user is nervous, the display unit will prioritize displaying simple, highly visible information. The display unit can use AI to estimate the user's emotions and prioritize the displayed information based on those emotions. For example, if the user is relaxed, the display unit will prioritize displaying detailed information. Also, if the user is in a hurry, the display unit can prioritize displaying concise information. In this way, the display unit can prioritize the displayed information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can use AI to select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a tablet, the display unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the display unit can select the optimal display method, taking into account the user's device information.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The system can also include a history analysis unit that analyzes the user's purchase history. This unit collects data on products the user has purchased in the past and analyzes their purchasing patterns. For example, if the history analysis unit finds that a user tends to purchase certain products on specific days or times, it can use this information to predict future purchases. Furthermore, the history analysis unit can identify products that users frequently purchase and offer special discounts or promotions for those products. In addition, the history analysis unit can recommend related products based on the user's purchase history. This allows the system to leverage the user's purchase history to provide more personalized services.

[0111] The system can also include a feedback collection unit to gather user feedback. This unit collects feedback from users about their satisfaction with and experience using the products they purchased. For example, it could send a survey to users after purchase, requesting their evaluation of the product. Furthermore, the feedback collection unit can analyze the feedback provided by users to improve product quality and services. It can also use user feedback to offer special offers or discounts on future purchases. This allows the system to leverage user feedback to provide better service.

[0112] The system can also include a recommendation unit that estimates the user's emotions and makes recommendations based on those emotions. For example, if the user is feeling stressed, the recommendation unit might recommend products or services that help them relax. The recommendation unit can use AI to estimate the user's emotions and make recommendations based on those emotions. For example, if the user is relaxed, the recommendation unit might suggest new products or services. Also, if the user is in a hurry, the recommendation unit can recommend products or services that can be purchased quickly. In this way, the recommendation unit can make optimal recommendations based on the user's emotions.

[0113] The system can also include an analytics unit that combines and analyzes the user's purchase history and emotions. For example, the analytics unit can analyze the types of products a user purchases when they are in a particular emotional state. The analytics unit can use AI to combine and analyze the user's purchase history and emotional data. For instance, it could identify products a user purchases when they are stressed and offer special offers for those products. It could also analyze the types of products a user purchases when they are relaxed and use that information to make recommendations. This allows the analytics unit to combine the user's purchase history and emotions to provide more personalized services.

[0114] The system can also include a monitoring unit that monitors the user's emotions in real time. This monitoring unit can estimate emotions from, for example, the user's facial expressions and voice. The monitoring unit can use AI to monitor the user's emotions in real time. For example, if the monitoring unit detects that the user is stressed, it can provide this information to other elements to take appropriate action. Furthermore, if the user is relaxed, the monitoring unit can use this information to make recommendations. This allows the monitoring unit to monitor the user's emotions in real time and improve the overall system performance.

[0115] The system can also include a prediction unit that makes predictions based on the user's purchase history. For example, the prediction unit can predict the next purchase based on data of products the user has purchased in the past. The prediction unit can use AI to analyze the user's purchase history and make predictions for the next purchase. For example, if the prediction unit finds that a user tends to purchase certain products on certain days or times, it can use that information to predict the next purchase. The prediction unit can also identify products that the user frequently purchases and offer special offers for those products. In this way, the prediction unit can leverage the user's purchase history to provide more personalized services.

[0116] The system can also include a weather analysis unit that combines and analyzes user purchase history with weather data. For example, the weather analysis unit can analyze trends in products users purchase under specific weather conditions. The weather analysis unit can use AI to combine and analyze user purchase history and weather data. For instance, it could identify trends in products purchased on rainy days and offer special offers for those products. It could also analyze trends in products purchased on sunny days and provide recommendations based on that information. This allows the weather analysis unit to combine user purchase history and weather data to provide more personalized services.

[0117] The system can also include a social analytics unit that combines and analyzes user purchase history and social media data. For example, the social analytics unit can analyze products a user has shown interest in on social media in combination with their purchase history. The social analytics unit can use AI to combine and analyze user purchase history and social media data. For instance, it can identify products a user is discussing on social media and offer special offers for those products. Furthermore, the social analytics unit can recommend relevant products based on the user's social media activity. This allows the social analytics unit to provide more personalized services by combining user purchase history and social media data.

[0118] The system can also include a location analysis unit that combines and analyzes the user's purchase history and location information. For example, the location analysis unit can analyze the trends in products a user purchases in specific locations. The location analysis unit can use AI to combine and analyze the user's purchase history and location information. For instance, it can identify products a user frequently purchases at specific stores and offer special offers for those products. Furthermore, the location analysis unit can analyze the trends in products a user purchases in specific areas and provide recommendations based on that information. This allows the location analysis unit to combine the user's purchase history and location information to provide more personalized services.

[0119] The system may also include an ad display unit that estimates the user's emotions and displays ads based on those emotions. For example, if the user is feeling stressed, the ad display unit might display ads for products or services that help them relax. The ad display unit can use AI to estimate the user's emotions and display ads based on those emotions. For example, if the user is relaxed, the ad display unit might display ads for new products or services. Also, if the user is in a hurry, the ad display unit might display ads for products or services that can be purchased quickly. This allows the ad display unit to display the most appropriate ads based on the user's emotions.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The calculation unit calculates inventory levels and remaining operating hours. The calculation unit retrieves inventory data from the store's inventory management system and calculates the remaining time until closing. Furthermore, it uses AI to identify products that are likely to remain unsold based on inventory levels and remaining operating hours. For example, it analyzes past sales data to predict products that will remain unsold during specific time periods. It can also identify products that are likely to remain unsold by considering weather and event information. Step 2: The discount unit automatically applies discounts based on the information calculated by the calculation unit. The discount unit sets a certain discount rate for specific products and uses AI to dynamically adjust the discount rate according to the sales performance and inventory status of the products. For example, it sets a higher discount rate for products with poor sales and a lower discount rate for products with good sales. It can also implement discounts that are limited to specific time periods. Step 3: The promotional unit promotes discount information implemented by the discounting unit to users in conjunction with a map service. The promotional unit displays the discount information on the map, allowing users to see what discounted items are available at stores near their current location. Furthermore, it uses AI to provide personalized discount information based on the user's location and purchase history. For example, it displays relevant discount information based on products the user has purchased in the past and prioritizes displaying discount information from stores near the user's current location.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the calculation unit, discount unit, appeal unit, prediction unit, identification unit, and display unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the calculation unit is implemented by the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing device 12. The discount unit is implemented by the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing device 12. The appeal unit is implemented by the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing device 12. The prediction unit is implemented by the identification processing unit 290 of the data processing device 12. The identification unit is implemented by the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing device 12. The display unit is implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the calculation unit, discount unit, appeal unit, prediction unit, identification unit, and display unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the calculation unit is implemented by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing device 12. The discount unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing device 12. The appeal unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing device 12. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing device 12. The identification unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing device 12. The display unit is implemented, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the calculation unit, discount unit, appeal unit, prediction unit, identification unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the calculation unit is implemented by the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing device 12. The discount unit is implemented by the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing device 12. The appeal unit is implemented by the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing device 12. The prediction unit is implemented by the identification processing unit 290 of the data processing device 12. The identification unit is implemented by the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing device 12. The display unit is implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the calculation unit, discount unit, appeal unit, prediction unit, identification unit, and display unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the calculation unit is implemented by the control unit 46A of the robot 414 or the identification processing unit 290 of the data processing device 12. The discount unit is implemented, for example, by the control unit 46A of the robot 414 or the identification processing unit 290 of the data processing device 12. The appeal unit is implemented, for example, by the control unit 46A of the robot 414 or the identification processing unit 290 of the data processing device 12. The prediction unit is implemented, for example, by the identification processing unit 290 of the data processing device 12. The identification unit is implemented, for example, by the control unit 46A of the robot 414 or the identification processing unit 290 of the data processing device 12. The display unit is implemented, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A calculation unit that calculates the inventory count and remaining operating hours, A discount unit that automatically applies a discount based on the information calculated by the calculation unit, A promotional unit that promotes discount information implemented by the aforementioned discount unit to users in conjunction with a map service, Equipped with A system characterized by the following features. (Note 2) It includes a prediction unit that forecasts the waste rate, taking into account weather and recent trends. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a unit that identifies the nearest store based on the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a display unit that shows discount information on a map. The system described in Appendix 1, characterized by the features described herein. (Note 5) The calculation unit, Identify products that are likely to remain unsold when there is little time left before closing and a large amount of inventory. The system described in Appendix 1, characterized by the features described herein. (Note 6) The discounted portion is, Set a specific discount rate for the identified product. The system described in Appendix 1, characterized by the features described herein. (Note 7) The calculation unit, The system estimates user sentiment and adjusts the inventory calculation method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The calculation unit, By analyzing past inventory data, we predict patterns of inventory fluctuations. The system described in Appendix 1, characterized by the features described herein. (Note 9) The calculation unit, When calculating inventory levels, we improve calculation accuracy by taking into account the expiration dates of the products. The system described in Appendix 1, characterized by the features described herein. (Note 10) The calculation unit, The system estimates the user's emotions and adjusts how inventory levels are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The calculation unit, When calculating inventory levels, the store's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The calculation unit, When calculating inventory levels, we improve calculation accuracy by referencing past sales data from the store. The system described in Appendix 1, characterized by the features described herein. (Note 13) The discounted portion is, The system estimates the user's emotions and adjusts the discount rate based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The discounted portion is, When offering discounts, the discount rate is set based on the popularity of the product. The system described in Appendix 1, characterized by the features described herein. (Note 15) The discounted portion is, When discounts are applied, different discount algorithms are used for each product category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The discounted portion is, The system estimates the user's emotions and adjusts how discount information is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The discounted portion is, When offering discounts, the discount rate is set based on the product's expiration date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The discounted portion is, When offering discounts, the store optimizes the discount rate by referring to past discount data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned appeal section is, It estimates the user's emotions and adjusts the appeal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned appeal section is, When making an appeal, we analyze the user's past purchase history to select the most effective appeal method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned appeal section is, When making an appeal, the content of the appeal will be customized based on the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned appeal section is, It estimates the user's emotions and adjusts the display order of appealing information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned appeal section is, When making an appeal, the most suitable appeal method is selected by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned appeal section is, When creating an appeal, analyze the user's social media activity to customize the appeal content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, We estimate user sentiment and adjust the discard rate prediction method based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 26) The prediction unit, When making predictions, historical weather data is used to predict the waste rate. The system described in Appendix 2, characterized by the features described herein. (Note 27) The prediction unit, When making predictions, recent trend data is analyzed to predict the discard rate. The system described in Appendix 2, characterized by the features described herein. (Note 28) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The prediction unit, When making predictions, the geographical location of the stores is taken into consideration when predicting the waste rate. The system described in Appendix 2, characterized by the features described herein. (Note 30) The prediction unit, When making predictions, the store's historical sales data is used to predict the waste rate. The system described in Appendix 2, characterized by the features described herein. (Note 31) The specified part is, We estimate user sentiment and adjust the store selection method based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 32) The specified part is, At specific times, the system analyzes the user's past visit history to identify the most suitable store. The system described in Appendix 3, characterized by the features described herein. (Note 33) The specified part is, We estimate the user's emotions and adjust how identified stores are displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The specified part is, When a user is identified, the system will take their device information into consideration to determine the most suitable store. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned display unit is The system estimates the user's emotions and adjusts how discount information is displayed based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned display unit is When displaying information, the system selects the optimal display method by referring to the user's past purchase history. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned display unit is It estimates the user's emotions and determines the priority of displayed information based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A calculation unit that calculates the inventory count and remaining operating hours, A discount unit that automatically applies a discount based on the information calculated by the calculation unit, A promotional unit that promotes discount information implemented by the aforementioned discount unit to users in conjunction with a map service, Equipped with A system characterized by the following features.

2. It includes a prediction unit that forecasts the waste rate, taking into account weather and recent trends. The system according to feature 1.

3. It includes a unit that identifies the nearest store based on the user's current location. The system according to feature 1.

4. It features a display unit that shows discount information on a map. The system according to feature 1.

5. The calculation unit, Identify products that are likely to remain unsold when there is little time left before closing and a large amount of inventory. The system according to feature 1.

6. The discounted portion is, Set a specific discount rate for the identified product. The system according to feature 1.

7. The calculation unit, The system estimates user sentiment and adjusts the inventory calculation method based on the estimated user sentiment. The system according to feature 1.

8. The calculation unit, By analyzing past inventory data, we predict patterns of inventory fluctuations. The system according to feature 1.

9. The calculation unit, When calculating inventory levels, we improve calculation accuracy by taking into account the expiration dates of the products. The system according to feature 1.

Images