System

Abstract

An object of a system according to an embodiment is to reduce discarding of food products whose expiration dates are approaching and to efficiently sell the food products.SOLUTION: A system according to an embodiment includes a food registration unit, a discount setting unit, a notification unit, and a sales status update unit. The food registration unit registers a food whose expiration date is approaching. The discount setting unit sets a discount rate of the food registered by the food registration unit. The notification unit notifies the user of the discount rate set by the discount setting unit. The selling-status updating unit updates the selling status of the food product.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to sufficiently reduce the waste of food that is approaching its expiration date and sell it efficiently.

[0005] The system according to the embodiment aims to reduce the waste of food products approaching their expiration date and to sell them efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a food registration unit, a discount setting unit, a notification unit, and a trading status update unit. The food registration unit registers food items that are approaching their expiration date. The discount setting unit sets a discount rate for the food items registered by the food registration unit. The notification unit notifies the user of the discount rate set by the discount setting unit. The trading status update unit updates the trading status of the food items. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the waste of food products that are approaching their expiration date and can sell them efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The AI ​​application for preventing product loss due to expiration dates according to an embodiment of the present invention is a system that automatically registers food products approaching their expiration date, discounts them, notifies users, and updates the sales status. As a result, the AI ​​application for preventing product loss due to expiration dates reduces food waste and enables users to shop cost-effectively.

[0029] The AI ​​app for preventing product loss due to expiration dates according to the embodiment includes a food registration unit, a discount setting unit, a notification unit, and a buying / selling status update unit. The food registration unit registers food items that are approaching their expiration date. For example, a store enters food items that are approaching their expiration date into the app, and the AI ​​automatically registers them. The food registration unit can also register food items using barcode scanning. For example, a barcode scanner can be used to read food information and register it in the app. The food registration unit can also manually enter food information. For example, a store staff member can manually enter the expiration date and price of the food item and register it in the app. The discount setting unit sets a discount rate for the food items registered by the food registration unit. For example, the AI ​​automatically sets the discount rate based on the expiration date. The discount setting unit can also adjust the discount rate according to the store's inventory status. For example, the discount rate can be set higher when inventory is high and lower when inventory is low. The discount setting unit can also change the discount rate during specific time periods. For example, the discount rate may be set higher just before closing time and lower immediately after opening time. The notification unit notifies the user of the discount rate set by the discount setting unit. For example, the discount information is sent to the user using a push notification. The notification unit can also send the discount information to the user using email. For example, the discount information is sent to the user's email address. The notification unit can also display the discount information to the user using a notification function within the app. For example, the discount information is displayed in the app's notification center. The sales status update unit updates the sales status of food products. For example, if a product is sold out, that information is immediately reflected in the app. The sales status update unit can also increase the discount rate if a discounted product is not sold within a certain period of time. For example, the discount rate for a product that is not sold within a certain period of time is increased from 20% to 50%. The sales status update unit can also update the inventory status of products in real time. For example, if inventory decreases, that information is immediately reflected in the app. As a result, the AI ​​app for preventing product waste due to expiration dates in accordance with the embodiment can efficiently register food products that are approaching their expiration date, set discounts, send notifications, and update the buying and selling status.For example, stores can manage inventory without hassle, and users can purchase bargain items without missing out.

[0030] The discount setting unit can dynamically adjust the discount rate based not only on the expiration date but also on at least one external factor selected from the season, weather, and local events. For example, the discount setting unit uses AI to predict seasonal demand and adjusts the discount rate for food products approaching their expiration date. For example, the discount rate for ice cream and beverages, which are in high demand in the summer, is set low, while the discount rate for products, which are in low demand in the winter, is set high. The discount setting unit can also adjust the discount rate taking into account changes in weather. For example, the discount rate for umbrellas and raincoats is set high on rainy days, and the discount rate for outdoor gear is set low on sunny days. Furthermore, the discount setting unit can adjust the discount rate in accordance with local events. For example, related products are discounted during local festivals and sporting events. This allows the discount rate to be dynamically adjusted taking into account external factors.

[0031] The discount setting unit can detect the quality of food using a sensor and set a discount rate based on that quality. For example, the discount setting unit can use a camera sensor that detects changes in appearance to evaluate the appearance quality of food and set a discount rate. For example, the discount setting unit can detect changes in the color or shape of vegetables or fruits and increase the discount rate if the quality has deteriorated. The discount setting unit can also use a gas sensor that detects changes in smell to evaluate the odor quality of food and set a discount rate. For example, the discount setting unit can detect changes in the smell of meat or fish and increase the discount rate if the quality has deteriorated. Furthermore, the discount setting unit can use a temperature sensor to evaluate the storage condition of food and set a discount rate. For example, the discount setting unit can detect changes in the temperature inside a refrigerator and increase the discount rate if the storage condition has deteriorated. In this way, the discount rate can be set based on the quality of food.

[0032] The food registration unit can include not only food items approaching their expiration date, but also beverages and daily necessities that are approaching their best-before date. For example, the food registration unit registers beverages that are approaching their expiration date in the app and discounts them. For example, AI automatically determines the discount rate, such as 20% off for beverages with an expiration date within one week and 50% off for beverages with an expiration date within three days. The food registration unit can also register daily necessities that are approaching their expiration date in the app and discount them. For example, AI automatically determines the discount rate, such as 20% off for daily necessities with an expiration date within one week and 50% off for daily necessities with an expiration date within three days. Furthermore, the food registration unit can manage food items approaching their expiration date and beverages and daily necessities that are approaching their best-before date all in one place. For example, the app can centrally manage all of this information and efficiently provide discount information. This allows beverages and daily necessities that are approaching their expiration date to be included in the target items.

[0033] The discount setting unit can collect discount information from other stores in real time and automatically set competitive prices. The discount setting unit, for example, collects discount information from other stores in real time and automatically sets competitive prices. For example, if the same product is discounted at a nearby store, the discount rate is adjusted based on that information. The discount setting unit can also collect discount information from online stores and set competitive prices. For example, the discount rate of the store is adjusted based on the discount information from the online store. Furthermore, the discount setting unit can adjust the discount rate of a specific product category based on the discount information from other stores. For example, if a product in the same category is discounted at another store, the discount rate is adjusted based on that information. This makes it possible to automatically set competitive prices based on the discount information from other stores.

[0034] The notification unit can use the user's location information to prioritize notifying the user of discount information at the nearest store. For example, the notification unit acquires the user's location information and prioritizes notifying the user of discount information at the nearest store. For example, the user receives discount information at the store closest to the user's current location in real time. The notification unit can also notify the user of discount information in a specific area based on the user's location information. For example, if the user is in a specific area, the notification unit notifies the user of discount information at stores in that area. Furthermore, the notification unit can also notify a user who is on the move of discount information at the nearest store based on the user's location information. For example, the user receives discount information at the nearest store while on the move. This allows discount information at the nearest store to be prioritized based on the user's location information.

[0035] The notification unit analyzes not only the user's purchase history but also the content of posts on SNS and search history, enabling more accurate product recommendations. The notification unit, for example, analyzes the user's purchase history and recommends products based on past purchases and preferences. For example, it prioritizes notifications of products that the user has previously purchased. The notification unit can also analyze the content of posts on SNS and recommend products based on the user's interests and concerns. For example, it notifies the user of products that the user has mentioned on SNS and related products. Furthermore, the notification unit can analyze the user's search history and recommend searched products and related products. For example, it notifies the user of related products based on the products the user has searched for. This enables more accurate product recommendations based on the purchase history, content of posts on SNS, and search history.

[0036] The notification unit can convey the appeal of a product not only by text but also by using video and audio messages when sending notifications. For example, the notification unit uses video to convey the appeal of a product when sending notifications. For example, a short video introducing how to use the product and its features is attached to the notification. The notification unit can also convey the appeal of a product using audio messages. For example, an audio message explaining the features and advantages of the product is attached to the notification. Furthermore, the notification unit can convey the appeal of a product by combining text, video, and audio messages. For example, detailed information about the product is provided in text, how to use the product is shown in video, and the advantages are explained in an audio message. This makes it possible to convey the appeal of a product not only by text but also by using video and audio messages.

[0037] The notification unit can add a social function that allows a user to share discount information with other users within the app. The notification unit, for example, adds a social function within the app to allow a user to share discount information with other users. For example, the notification unit can provide a function that allows a user to share a bargain product that the user has found with friends and family. The notification unit can also add a social function that allows a user to communicate with other users within the app. For example, the notification unit can provide a function that allows a user to post comments and reviews about discount information. The notification unit can also add a group function that allows a user to share discount information with other users within the app. For example, a user can create a specific group and share discount information within that group. This allows a social function that allows a user to share discount information with other users within the app to be added.

[0038] When updating the sales status, the sales status update unit can reflect not only the product inventory status but also the store's congestion status in real time. For example, the sales status update unit uses AI to monitor the store's congestion status in real time and delays updating the sales status when it is crowded. For example, it delays the reflection of sold-out information during times when the store is crowded. The sales status update unit can also adjust the product inventory status based on the store's congestion status. For example, it displays more inventory when it is crowded and less inventory when it is not crowded. Furthermore, the sales status update unit can adjust the product discount rate based on the store's congestion status. For example, it lowers the discount rate when it is crowded and raises the discount rate when it is not crowded. This allows the sales status to be reflected in real time, taking into account the product inventory status and the store's congestion status.

[0039] The buying and selling status update unit can learn the user's purchasing patterns and prioritize displaying products that are likely to sell during a specific time period. The buying and selling status update unit, for example, analyzes the user's purchasing patterns and prioritizes displaying products that are likely to sell during a specific time period. For example, bento boxes and sandwiches are prioritized for display at lunchtime. The buying and selling status update unit can also prioritize displaying products that are likely to sell during a specific day of the week based on the user's purchasing patterns. For example, snacks and drinks are prioritized for display on weekends. Furthermore, the buying and selling status update unit can also prioritize displaying products that are likely to sell during a specific season based on the user's purchasing patterns. For example, ice cream and cold drinks are prioritized for display in the summer. In this way, the buying and selling status update unit can learn the user's purchasing patterns and prioritize displaying products that are likely to sell during a specific time period.

[0040] The buying and selling status update unit can link the updated information on the buying and selling status with inventory information from other retail stores, allowing the user to purchase at the nearest store. The buying and selling status update unit, for example, collects inventory information from other retail stores in real time, allowing the user to purchase at the nearest store. For example, it displays inventory information from the store closest to the user's current location. The buying and selling status update unit can also suggest an alternative store when a specific product is sold out based on inventory information from other retail stores. For example, when a specific product is sold out, it displays inventory information from nearby stores. Furthermore, the buying and selling status update unit can also display the inventory status in a specific region based on inventory information from other retail stores. For example, when the user is in a specific region, it displays inventory information from stores in that region. This links with inventory information from other retail stores, allowing the user to purchase at the nearest store.

[0041] The buying and selling status update unit can link the updated information about the buying and selling status with the user's calendar app and automatically add the planned purchase to the schedule. For example, the buying and selling status update unit can link the updated information about the buying and selling status with the user's calendar app and automatically add the planned purchase to the schedule. For example, if a specific product is discounted, the information is added to the calendar. The buying and selling status update unit can also link with the user's calendar app to add a planned purchase to coincide with a specific event. For example, a specific product is added to the planned purchase to coincide with the user's birthday or anniversary. The buying and selling status update unit can also link with the user's calendar app to add a planned purchase for a specific time period. For example, if the user has a habit of shopping during a specific time period, the planned purchase is added for that time period. In this way, the updated information about the buying and selling status can be linked with the user's calendar app and the planned purchase can be automatically added to the schedule.

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

[0043] The food registration unit can include not only food items approaching their expiration date, but also beverages and daily necessities that are approaching their best-before date. For example, the food registration unit registers beverages that are approaching their expiration date in the app and discounts them. For example, AI automatically determines the discount rate, such as 20% off for beverages with an expiration date within one week and 50% off for beverages with an expiration date within three days. The food registration unit can also register daily necessities that are approaching their expiration date in the app and discount them. For example, AI automatically determines the discount rate, such as 20% off for daily necessities with an expiration date within one week and 50% off for daily necessities with an expiration date within three days. Furthermore, the food registration unit can manage food items approaching their expiration date and beverages and daily necessities that are approaching their best-before date all in one place. For example, the app can centrally manage all of this information and efficiently provide discount information. This allows beverages and daily necessities that are approaching their expiration date to be included in the target items.

[0044] The discount setting unit can collect discount information from other stores in real time and automatically set competitive prices. The discount setting unit, for example, collects discount information from other stores in real time and automatically sets competitive prices. For example, if the same product is discounted at a nearby store, the discount rate is adjusted based on that information. The discount setting unit can also collect discount information from online stores and set competitive prices. For example, the discount rate of the store is adjusted based on the discount information from the online store. Furthermore, the discount setting unit can adjust the discount rate of a specific product category based on the discount information from other stores. For example, if a product in the same category is discounted at another store, the discount rate is adjusted based on that information. This makes it possible to automatically set competitive prices based on the discount information from other stores.

[0045] The notification unit can use the user's location information to prioritize notifying the user of discount information at the nearest store. For example, the notification unit acquires the user's location information and prioritizes notifying the user of discount information at the nearest store. For example, the user receives discount information at the store closest to the user's current location in real time. The notification unit can also notify the user of discount information in a specific area based on the user's location information. For example, if the user is in a specific area, the notification unit notifies the user of discount information at stores in that area. Furthermore, the notification unit can also notify a user who is on the move of discount information at the nearest store based on the user's location information. For example, the user receives discount information at the nearest store while on the move. This allows discount information at the nearest store to be prioritized based on the user's location information.

[0046] The notification unit analyzes not only the user's purchase history but also the content of posts on SNS and search history, enabling more accurate product recommendations. The notification unit, for example, analyzes the user's purchase history and recommends products based on past purchases and preferences. For example, it prioritizes notifications of products that the user has previously purchased. The notification unit can also analyze the content of posts on SNS and recommend products based on the user's interests and concerns. For example, it notifies the user of products that the user has mentioned on SNS and related products. Furthermore, the notification unit can analyze the user's search history and recommend searched products and related products. For example, it notifies the user of related products based on the products the user has searched for. This enables more accurate product recommendations based on the purchase history, content of posts on SNS, and search history.

[0047] The notification unit can convey the appeal of a product not only by text but also by using video and audio messages when sending notifications. For example, the notification unit uses video to convey the appeal of a product when sending notifications. For example, a short video introducing how to use the product and its features is attached to the notification. The notification unit can also convey the appeal of a product using audio messages. For example, an audio message explaining the features and advantages of the product is attached to the notification. Furthermore, the notification unit can convey the appeal of a product by combining text, video, and audio messages. For example, detailed information about the product is provided in text, how to use the product is shown in video, and the advantages are explained in an audio message. This makes it possible to convey the appeal of a product not only by text but also by using video and audio messages.

[0048] The notification unit can add a social function that allows a user to share discount information with other users within the app. The notification unit, for example, adds a social function within the app to allow a user to share discount information with other users. For example, the notification unit can provide a function that allows a user to share a bargain product that the user has found with friends and family. The notification unit can also add a social function that allows a user to communicate with other users within the app. For example, the notification unit can provide a function that allows a user to post comments and reviews about discount information. The notification unit can also add a group function that allows a user to share discount information with other users within the app. For example, a user can create a specific group and share discount information within that group. This allows a social function that allows a user to share discount information with other users within the app to be added.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The food registration unit registers food items that are approaching their expiration date. For example, a store can enter food items that are approaching their expiration date into the app, and the AI ​​will automatically register them. Alternatively, a barcode scanner can be used to read food information and register it in the app. Store staff can also manually enter the expiration date and price of food items and register them in the app. Step 2: The discount setting unit sets the discount rate for the food registered by the food registration unit. For example, the AI ​​automatically sets the discount rate based on the expiration date. It can also adjust the discount rate according to the store's inventory status. It can also change the discount rate during specific time periods. Step 3: The notification unit notifies the user of the discount rate set by the discount setting unit. For example, the discount information is sent to the user using a push notification or email. The discount information can also be displayed to the user using a notification function within the app. Step 4: The sales status updater updates the sales status of food items. For example, if a certain product is sold out, that information is immediately reflected in the app. Also, if a discounted product does not sell within a certain period of time, the discount rate can be increased further. Furthermore, product inventory status can be updated in real time.

[0051] (Example 2) The AI ​​application for preventing product loss due to expiration dates according to an embodiment of the present invention is a system that automatically registers food products approaching their expiration date, discounts them, notifies users, and updates the sales status. As a result, the AI ​​application for preventing product loss due to expiration dates reduces food waste and enables users to shop cost-effectively.

[0052] The AI ​​app for preventing product loss due to expiration dates according to the embodiment includes a food registration unit, a discount setting unit, a notification unit, and a buying / selling status update unit. The food registration unit registers food items that are approaching their expiration date. For example, a store enters food items that are approaching their expiration date into the app, and the AI ​​automatically registers them. The food registration unit can also register food items using barcode scanning. For example, a barcode scanner can be used to read food information and register it in the app. The food registration unit can also manually enter food information. For example, a store staff member can manually enter the expiration date and price of the food item and register it in the app. The discount setting unit sets a discount rate for the food items registered by the food registration unit. For example, the AI ​​automatically sets the discount rate based on the expiration date. The discount setting unit can also adjust the discount rate according to the store's inventory status. For example, the discount rate can be set higher when inventory is high and lower when inventory is low. The discount setting unit can also change the discount rate during specific time periods. For example, the discount rate may be set higher just before closing time and lower immediately after opening time. The notification unit notifies the user of the discount rate set by the discount setting unit. For example, the discount information is sent to the user using a push notification. The notification unit can also send the discount information to the user using email. For example, the discount information is sent to the user's email address. The notification unit can also display the discount information to the user using a notification function within the app. For example, the discount information is displayed in the app's notification center. The sales status update unit updates the sales status of food products. For example, if a product is sold out, that information is immediately reflected in the app. The sales status update unit can also increase the discount rate if a discounted product is not sold within a certain period of time. For example, the discount rate for a product that is not sold within a certain period of time is increased from 20% to 50%. The sales status update unit can also update the inventory status of products in real time. For example, if inventory decreases, that information is immediately reflected in the app. As a result, the AI ​​app for preventing product waste due to expiration dates in accordance with the embodiment can efficiently register food products that are approaching their expiration date, set discounts, send notifications, and update the buying and selling status.For example, stores can manage inventory without hassle, and users can purchase bargain items without missing out.

[0053] The discount setting unit can dynamically adjust the discount rate based not only on the expiration date but also on at least one external factor selected from the season, weather, and local events. For example, the discount setting unit uses AI to predict seasonal demand and adjusts the discount rate for food products approaching their expiration date. For example, the discount rate for ice cream and beverages, which are in high demand in the summer, is set low, while the discount rate for products, which are in low demand in the winter, is set high. The discount setting unit can also adjust the discount rate taking into account changes in weather. For example, the discount rate for umbrellas and raincoats is set high on rainy days, and the discount rate for outdoor gear is set low on sunny days. Furthermore, the discount setting unit can adjust the discount rate in accordance with local events. For example, related products are discounted during local festivals and sporting events. This allows the discount rate to be dynamically adjusted taking into account external factors.

[0054] The discount setting unit can detect the quality of food using a sensor and set a discount rate based on that quality. For example, the discount setting unit can use a camera sensor that detects changes in appearance to evaluate the appearance quality of food and set a discount rate. For example, the discount setting unit can detect changes in the color or shape of vegetables or fruits and increase the discount rate if the quality has deteriorated. The discount setting unit can also use a gas sensor that detects changes in smell to evaluate the odor quality of food and set a discount rate. For example, the discount setting unit can detect changes in the smell of meat or fish and increase the discount rate if the quality has deteriorated. Furthermore, the discount setting unit can use a temperature sensor to evaluate the storage condition of food and set a discount rate. For example, the discount setting unit can detect changes in the temperature inside a refrigerator and increase the discount rate if the storage condition has deteriorated. In this way, the discount rate can be set based on the quality of food.

[0055] The notification unit can provide a special discount to a user who is in a particular emotional state based on the user's emotional data. For example, the notification unit analyzes the user's emotional data and provides a special discount to a user who is feeling stressed. For example, the notification unit can provide a discount on food and drinks that are useful for relieving stress. The notification unit can also provide a special discount when the user is feeling happy. For example, when the user is feeling happy, the notification unit can provide a discount on a favorite product. The notification unit can also provide a special discount when the user is feeling sad. For example, when the user is feeling sad, the notification unit can provide a discount on a product that will lift the user's spirits. In this way, a special discount can be provided according to the user's emotional state.

[0056] The food registration unit can include not only food items approaching their expiration date, but also beverages and daily necessities that are approaching their best-before date. For example, the food registration unit registers beverages that are approaching their expiration date in the app and discounts them. For example, AI automatically determines the discount rate, such as 20% off for beverages with an expiration date within one week and 50% off for beverages with an expiration date within three days. The food registration unit can also register daily necessities that are approaching their expiration date in the app and discount them. For example, AI automatically determines the discount rate, such as 20% off for daily necessities with an expiration date within one week and 50% off for daily necessities with an expiration date within three days. Furthermore, the food registration unit can manage food items approaching their expiration date and beverages and daily necessities that are approaching their best-before date all in one place. For example, the app can centrally manage all of this information and efficiently provide discount information. This allows beverages and daily necessities that are approaching their expiration date to be included in the target items.

[0057] The discount setting unit can collect discount information from other stores in real time and automatically set competitive prices. The discount setting unit, for example, collects discount information from other stores in real time and automatically sets competitive prices. For example, if the same product is discounted at a nearby store, the discount rate is adjusted based on that information. The discount setting unit can also collect discount information from online stores and set competitive prices. For example, the discount rate of the store is adjusted based on the discount information from the online store. Furthermore, the discount setting unit can adjust the discount rate of a specific product category based on the discount information from other stores. For example, if a product in the same category is discounted at another store, the discount rate is adjusted based on that information. This makes it possible to automatically set competitive prices based on the discount information from other stores.

[0058] The notification unit can prioritize discounts on specific food categories when the user is in a specific emotional state based on the user's emotional data. For example, the notification unit analyzes the user's emotional data and prioritizes discounts on comfort foods when the user is in a specific emotional state. For example, when the user is feeling stressed, the notification unit discounts chocolate and ice cream. The notification unit can also prioritize discounts on specific food categories when the user is feeling happy. For example, when the user is feeling happy, the notification unit discounts their favorite snacks and drinks. The notification unit can also prioritize discounts on specific food categories when the user is feeling sad. For example, when the user is feeling sad, the notification unit discounts foods and drinks that lift the user's spirits. In this way, it is possible to prioritize discounts on specific food categories when the user is in a specific emotional state.

[0059] The notification unit can use the user's location information to prioritize notifying the user of discount information at the nearest store. For example, the notification unit acquires the user's location information and prioritizes notifying the user of discount information at the nearest store. For example, the user receives discount information at the store closest to the user's current location in real time. The notification unit can also notify the user of discount information in a specific area based on the user's location information. For example, if the user is in a specific area, the notification unit notifies the user of discount information at stores in that area. Furthermore, the notification unit can also notify a user who is on the move of discount information at the nearest store based on the user's location information. For example, the user receives discount information at the nearest store while on the move. This allows discount information at the nearest store to be prioritized based on the user's location information.

[0060] The notification unit analyzes not only the user's purchase history but also the content of posts on SNS and search history, enabling more accurate product recommendations. The notification unit, for example, analyzes the user's purchase history and recommends products based on past purchases and preferences. For example, it prioritizes notifications of products that the user has previously purchased. The notification unit can also analyze the content of posts on SNS and recommend products based on the user's interests and concerns. For example, it notifies the user of products that the user has mentioned on SNS and related products. Furthermore, the notification unit can analyze the user's search history and recommend searched products and related products. For example, it notifies the user of related products based on the products the user has searched for. This enables more accurate product recommendations based on the purchase history, content of posts on SNS, and search history.

[0061] The notification unit can use the emotion estimation function to notify the user of products that have a relaxing effect when the user is feeling stressed. The notification unit, for example, analyzes the user's emotion data and notifies the user of products that have a relaxing effect when the user is feeling stressed. For example, when the user is feeling stressed, the notification unit notifies the user of discount information on herbal tea or aroma candles. The notification unit can also notify the user of products that have a relaxing effect when the user is seeking relaxation. For example, when the user is seeking relaxation, the notification unit notifies the user of discount information on relaxation goods or massage oils. Furthermore, the notification unit can also notify the user of products that have a relaxing effect when the user is feeling tired. For example, when the user is feeling tired, the notification unit notifies the user of discount information on refreshing drinks or bath salts. In this way, the notification unit can notify the user of products that have a relaxing effect when the user is feeling stressed.

[0062] The notification unit can convey the appeal of a product not only by text but also by using video and audio messages when sending notifications. For example, the notification unit uses video to convey the appeal of a product when sending notifications. For example, a short video introducing how to use the product and its features is attached to the notification. The notification unit can also convey the appeal of a product using audio messages. For example, an audio message explaining the features and advantages of the product is attached to the notification. Furthermore, the notification unit can convey the appeal of a product by combining text, video, and audio messages. For example, detailed information about the product is provided in text, how to use the product is shown in video, and the advantages are explained in an audio message. This makes it possible to convey the appeal of a product not only by text but also by using video and audio messages.

[0063] The notification unit can add a social function that allows a user to share discount information with other users within the app. The notification unit, for example, adds a social function within the app to allow a user to share discount information with other users. For example, the notification unit can provide a function that allows a user to share a bargain product that the user has found with friends and family. The notification unit can also add a social function that allows a user to communicate with other users within the app. For example, the notification unit can provide a function that allows a user to post comments and reviews about discount information. The notification unit can also add a group function that allows a user to share discount information with other users within the app. For example, a user can create a specific group and share discount information within that group. This allows a social function that allows a user to share discount information with other users within the app to be added.

[0064] The notification unit can use the emotion estimation function to customize the tone and content of notifications to match the user's emotion when the user is in a specific emotional state. The notification unit, for example, analyzes the user's emotional data and customizes the tone and content of notifications when the user is in a specific emotional state. For example, when the user is feeling stressed, the notification unit notifies the user of products that have a relaxing effect. The notification unit can also brighten the tone and make the content of notifications positive when the user is feeling happy. For example, when the user is feeling happy, the notification unit notifies the user of special offers and promotional information. Furthermore, when the user is feeling sad, the notification unit can soften the tone and make the content encouraging. For example, when the user is feeling sad, the notification unit notifies the user of products that will lift their spirits. In this way, the tone and content of notifications can be customized according to the user's emotional state.

[0065] When updating the sales status, the sales status update unit can reflect not only the product inventory status but also the store's congestion status in real time. For example, the sales status update unit uses AI to monitor the store's congestion status in real time and delays updating the sales status when it is crowded. For example, it delays the reflection of sold-out information during times when the store is crowded. The sales status update unit can also adjust the product inventory status based on the store's congestion status. For example, it displays more inventory when it is crowded and less inventory when it is not crowded. Furthermore, the sales status update unit can adjust the product discount rate based on the store's congestion status. For example, it lowers the discount rate when it is crowded and raises the discount rate when it is not crowded. This allows the sales status to be reflected in real time, taking into account the product inventory status and the store's congestion status.

[0066] The buying and selling status update unit can learn the user's purchasing patterns and prioritize displaying products that are likely to sell during a specific time period. The buying and selling status update unit, for example, analyzes the user's purchasing patterns and prioritizes displaying products that are likely to sell during a specific time period. For example, bento boxes and sandwiches are prioritized for display at lunchtime. The buying and selling status update unit can also prioritize displaying products that are likely to sell during a specific day of the week based on the user's purchasing patterns. For example, snacks and drinks are prioritized for display on weekends. Furthermore, the buying and selling status update unit can also prioritize displaying products that are likely to sell during a specific season based on the user's purchasing patterns. For example, ice cream and cold drinks are prioritized for display in the summer. In this way, the buying and selling status update unit can learn the user's purchasing patterns and prioritize displaying products that are likely to sell during a specific time period.

[0067] The buying and selling status update unit can use the emotion estimation function to suggest a substitute for a sold-out product according to the user's emotional state. The buying and selling status update unit, for example, analyzes the user's emotional data and suggests a substitute for the sold-out product. For example, if the user is feeling stressed, the buying and selling status update unit suggests a substitute that has a relaxing effect. The buying and selling status update unit can also suggest a substitute for a sold-out product if the user is feeling happy. For example, if the user is feeling happy, the buying and selling status update unit suggests other products in the same category. Furthermore, the buying and selling status update unit can also suggest a substitute for a sold-out product if the user is feeling sad. For example, if the user is feeling sad, the buying and selling status update unit suggests a substitute that will lift the user's spirits. In this way, a substitute for a sold-out product can be suggested according to the user's emotional state.

[0068] The buying and selling status update unit can link the updated information on the buying and selling status with inventory information from other retail stores, allowing the user to purchase at the nearest store. The buying and selling status update unit, for example, collects inventory information from other retail stores in real time, allowing the user to purchase at the nearest store. For example, it displays inventory information from the store closest to the user's current location. The buying and selling status update unit can also suggest an alternative store when a specific product is sold out based on inventory information from other retail stores. For example, when a specific product is sold out, it displays inventory information from nearby stores. Furthermore, the buying and selling status update unit can also display the inventory status in a specific region based on inventory information from other retail stores. For example, when the user is in a specific region, it displays inventory information from stores in that region. This links with inventory information from other retail stores, allowing the user to purchase at the nearest store.

[0069] The buying and selling status update unit can link the updated information about the buying and selling status with the user's calendar app and automatically add the planned purchase to the schedule. For example, the buying and selling status update unit can link the updated information about the buying and selling status with the user's calendar app and automatically add the planned purchase to the schedule. For example, if a specific product is discounted, the information is added to the calendar. The buying and selling status update unit can also link with the user's calendar app to add a planned purchase to coincide with a specific event. For example, a specific product is added to the planned purchase to coincide with the user's birthday or anniversary. The buying and selling status update unit can also link with the user's calendar app to add a planned purchase for a specific time period. For example, if the user has a habit of shopping during a specific time period, the planned purchase is added for that time period. In this way, the updated information about the buying and selling status can be linked with the user's calendar app and the planned purchase can be automatically added to the schedule.

[0070] The buying and selling status update unit can use the emotion estimation function to prioritize notifying the user that a sold-out item is back in stock when the user is in a specific emotional state. The buying and selling status update unit, for example, analyzes the user's emotional data and prioritizes notifying the user that a sold-out item is back in stock when the user is in a specific emotional state. For example, when the user is feeling stressed, the unit notifies the user that a relaxing item is back in stock. The buying and selling status update unit can also prioritize notifying the user that a sold-out item is back in stock when the user is feeling happy. For example, when the user is feeling happy, the unit notifies the user that a product in the same category is back in stock. Furthermore, when the user is feeling sad, the buying and selling status update unit can also prioritize notifying the user that a sold-out item is back in stock. For example, when the user is feeling sad, the unit notifies the user that a product that will lift their spirits is back in stock. In this way, the buying and selling status update unit can prioritize notifying the user that a sold-out item is back in stock when the user is in a specific emotional state.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The notification unit can provide a special discount to a user who is in a particular emotional state based on the user's emotional data. For example, the notification unit analyzes the user's emotional data and provides a special discount to a user who is feeling stressed. For example, the notification unit can provide a discount on food and drinks that are useful for relieving stress. The notification unit can also provide a special discount when the user is feeling happy. For example, when the user is feeling happy, the notification unit can provide a discount on a favorite product. The notification unit can also provide a special discount when the user is feeling sad. For example, when the user is feeling sad, the notification unit can provide a discount on a product that will lift the user's spirits. In this way, a special discount can be provided according to the user's emotional state.

[0073] The food registration unit can include not only food items approaching their expiration date, but also beverages and daily necessities that are approaching their best-before date. For example, the food registration unit registers beverages that are approaching their expiration date in the app and discounts them. For example, AI automatically determines the discount rate, such as 20% off for beverages with an expiration date within one week and 50% off for beverages with an expiration date within three days. The food registration unit can also register daily necessities that are approaching their expiration date in the app and discount them. For example, AI automatically determines the discount rate, such as 20% off for daily necessities with an expiration date within one week and 50% off for daily necessities with an expiration date within three days. Furthermore, the food registration unit can manage food items approaching their expiration date and beverages and daily necessities that are approaching their best-before date all in one place. For example, the app can centrally manage all of this information and efficiently provide discount information. This allows beverages and daily necessities that are approaching their expiration date to be included in the target items.

[0074] The discount setting unit can collect discount information from other stores in real time and automatically set competitive prices. The discount setting unit, for example, collects discount information from other stores in real time and automatically sets competitive prices. For example, if the same product is discounted at a nearby store, the discount rate is adjusted based on that information. The discount setting unit can also collect discount information from online stores and set competitive prices. For example, the discount rate of the store is adjusted based on the discount information from the online store. Furthermore, the discount setting unit can adjust the discount rate of a specific product category based on the discount information from other stores. For example, if a product in the same category is discounted at another store, the discount rate is adjusted based on that information. This makes it possible to automatically set competitive prices based on the discount information from other stores.

[0075] The notification unit can prioritize discounts on specific food categories when the user is in a specific emotional state based on the user's emotional data. For example, the notification unit analyzes the user's emotional data and prioritizes discounts on comfort foods when the user is in a specific emotional state. For example, when the user is feeling stressed, the notification unit discounts chocolate and ice cream. The notification unit can also prioritize discounts on specific food categories when the user is feeling happy. For example, when the user is feeling happy, the notification unit discounts their favorite snacks and drinks. The notification unit can also prioritize discounts on specific food categories when the user is feeling sad. For example, when the user is feeling sad, the notification unit discounts foods and drinks that lift the user's spirits. In this way, it is possible to prioritize discounts on specific food categories when the user is in a specific emotional state.

[0076] The notification unit can use the user's location information to prioritize notifying the user of discount information at the nearest store. For example, the notification unit acquires the user's location information and prioritizes notifying the user of discount information at the nearest store. For example, the user receives discount information at the store closest to the user's current location in real time. The notification unit can also notify the user of discount information in a specific area based on the user's location information. For example, if the user is in a specific area, the notification unit notifies the user of discount information at stores in that area. Furthermore, the notification unit can also notify a user who is on the move of discount information at the nearest store based on the user's location information. For example, the user receives discount information at the nearest store while on the move. This allows discount information at the nearest store to be prioritized based on the user's location information.

[0077] The notification unit analyzes not only the user's purchase history but also the content of posts on SNS and search history, enabling more accurate product recommendations. The notification unit, for example, analyzes the user's purchase history and recommends products based on past purchases and preferences. For example, it prioritizes notifications of products that the user has previously purchased. The notification unit can also analyze the content of posts on SNS and recommend products based on the user's interests and concerns. For example, it notifies the user of products that the user has mentioned on SNS and related products. Furthermore, the notification unit can analyze the user's search history and recommend searched products and related products. For example, it notifies the user of related products based on the products the user has searched for. This enables more accurate product recommendations based on the purchase history, content of posts on SNS, and search history.

[0078] The notification unit can use the emotion estimation function to notify the user of products that have a relaxing effect when the user is feeling stressed. The notification unit, for example, analyzes the user's emotion data and notifies the user of products that have a relaxing effect when the user is feeling stressed. For example, when the user is feeling stressed, the notification unit notifies the user of discount information on herbal tea or aroma candles. The notification unit can also notify the user of products that have a relaxing effect when the user is seeking relaxation. For example, when the user is seeking relaxation, the notification unit notifies the user of discount information on relaxation goods or massage oils. Furthermore, the notification unit can also notify the user of products that have a relaxing effect when the user is feeling tired. For example, when the user is feeling tired, the notification unit notifies the user of discount information on refreshing drinks or bath salts. In this way, the notification unit can notify the user of products that have a relaxing effect when the user is feeling stressed.

[0079] The notification unit can convey the appeal of a product not only by text but also by using video and audio messages when sending notifications. For example, the notification unit uses video to convey the appeal of a product when sending notifications. For example, a short video introducing how to use the product and its features is attached to the notification. The notification unit can also convey the appeal of a product using audio messages. For example, an audio message explaining the features and advantages of the product is attached to the notification. Furthermore, the notification unit can convey the appeal of a product by combining text, video, and audio messages. For example, detailed information about the product is provided in text, how to use the product is shown in video, and the advantages are explained in an audio message. This makes it possible to convey the appeal of a product not only by text but also by using video and audio messages.

[0080] The notification unit can add a social function that allows a user to share discount information with other users within the app. The notification unit, for example, adds a social function within the app to allow a user to share discount information with other users. For example, the notification unit can provide a function that allows a user to share a bargain product that the user has found with friends and family. The notification unit can also add a social function that allows a user to communicate with other users within the app. For example, the notification unit can provide a function that allows a user to post comments and reviews about discount information. The notification unit can also add a group function that allows a user to share discount information with other users within the app. For example, a user can create a specific group and share discount information within that group. This allows a social function that allows a user to share discount information with other users within the app to be added.

[0081] The notification unit can use the emotion estimation function to customize the tone and content of notifications to match the user's emotion when the user is in a specific emotional state. The notification unit, for example, analyzes the user's emotional data and customizes the tone and content of notifications when the user is in a specific emotional state. For example, when the user is feeling stressed, the notification unit notifies the user of products that have a relaxing effect. The notification unit can also brighten the tone and make the content of notifications positive when the user is feeling happy. For example, when the user is feeling happy, the notification unit notifies the user of special offers and promotional information. Furthermore, when the user is feeling sad, the notification unit can soften the tone and make the content encouraging. For example, when the user is feeling sad, the notification unit notifies the user of products that will lift their spirits. In this way, the tone and content of notifications can be customized according to the user's emotional state.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The food registration unit registers food items that are approaching their expiration date. For example, a store can enter food items that are approaching their expiration date into the app, and the AI ​​will automatically register them. Alternatively, a barcode scanner can be used to read food information and register it in the app. Store staff can also manually enter the expiration date and price of food items and register them in the app. Step 2: The discount setting unit sets the discount rate for the food registered by the food registration unit. For example, the AI ​​automatically sets the discount rate based on the expiration date. It can also adjust the discount rate according to the store's inventory status. It can also change the discount rate during specific time periods. Step 3: The notification unit notifies the user of the discount rate set by the discount setting unit. For example, the discount information is sent to the user using a push notification or email. The discount information can also be displayed to the user using a notification function within the app. Step 4: The sales status updater updates the sales status of food items. For example, if a certain product is sold out, that information is immediately reflected in the app. Also, if a discounted product does not sell within a certain period of time, the discount rate can be increased further. Furthermore, product inventory status can be updated in real time.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a food registration unit that registers food that is approaching its expiration date; a discount setting unit that sets a discount rate for the food product registered by the food product registration unit; a notification unit that notifies a user of the discount rate set by the discount setting unit; a trading status update unit that updates the trading status of the food product. A system characterized by:

2. The discount setting unit Dynamically adjust discount rates based on not only expiration dates but also external factors such as seasons, weather, and local events.

2. The system of claim 1.

3. The food registration unit This includes not only food products that are nearing their expiration date, but also beverages and daily necessities that are nearing their best-before date.

2. The system of claim 1.

4. The notification unit By using the location information of the user, the discount information at the nearest store is preferentially notified.

2. The system of claim 1.

5. The trading status update unit When updating sales status, not only the product's inventory status but also the store's congestion status are taken into account and reflected in real time.

2. The system of claim 1.

6. The notification unit Providing special discounts to users in a particular emotional state based on the emotional data of the users. The system of claim 1 .

7. The notification unit Notifying the user of products that have a relaxing effect when the user is feeling stressed 2. The system of claim 1.

8. The trading status update unit Suggesting an alternative to the sold-out product depending on the emotional state of the user 2. The system of claim 1.

Images