System

Abstract

An object of a system according to an embodiment is to efficiently manage coupons issued in a plurality of services and appropriately provide the coupons to a user.SOLUTION: A system includes a coupon warehouse generation unit, a coupon issuance unit, a provision unit, a monitoring unit, and an optimization unit. The coupon warehouse generation unit generates a coupon warehouse. The coupon issuing unit issues a coupon from the coupon warehouse generated by the coupon warehouse generating unit. The providing unit provides the user with the coupon issued by the coupon issuing unit. The monitoring unit monitors a usage status of the coupon issued by the coupon issuing unit. The optimization unit optimizes the coupon issuing strategy based on the usage status of the coupon monitored by the monitoring unit.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 techniques have had the problem of making it difficult to efficiently manage coupons issued by multiple services and provide them appropriately to users.

[0005] The system according to the embodiment aims to efficiently manage coupons issued by a plurality of services and provide them appropriately to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a coupon warehouse generation unit, a coupon issuing unit, a provision unit, a monitoring unit, and an optimization unit. The coupon warehouse generation unit generates a coupon warehouse. The coupon issuing unit issues coupons from the coupon warehouse generated by the coupon warehouse generation unit. The provision unit provides the coupons issued by the coupon issuing unit to users. The monitoring unit monitors the usage status of the coupons issued by the coupon issuing unit. The optimization unit optimizes a coupon issuance strategy based on the usage status of the coupons monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage coupons issued by multiple services and provide them appropriately to users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The coupon management system according to the embodiment of the present invention analyzes the user's behavioral history and provides optimal coupons. This allows users to effectively use coupons, thereby increasing the frequency of use and revenues of SBG as a whole.

[0029] A coupon management system according to an embodiment includes a coupon warehouse generation unit, a behavior history analysis unit, and a coupon issuing unit. The coupon warehouse generation unit generates a coupon warehouse. For example, the coupon warehouse generation unit collects, organizes, and stores coupon information issued by each SBG service. The coupon warehouse generation unit also generates a coupon warehouse containing various coupons, such as discount coupons for restaurants and discount coupons for admission to entertainment facilities. The behavior history analysis unit analyzes a user's behavior history. For example, the behavior history analysis unit collects and analyzes a user's past purchase history, visit history, and search history. The behavior history analysis unit also performs analysis to prioritize providing new restaurant coupons to users who have frequently used restaurant coupons in the past. The coupon issuing unit issues optimal coupons. For example, the coupon issuing unit issues optimal coupons to users from the coupon warehouse based on the analysis results of the behavior history analysis unit. The coupon issuing unit also provides coupons to users via smartphone apps, emails, websites, etc. For example, when a user opens a smartphone app, the optimal coupon is automatically displayed. As a result, the coupon management system according to the embodiment allows users to easily find and use the most suitable coupons, thereby improving the frequency of use and profits of SBG as a whole.

[0030] The coupon warehouse generation unit can automatically update coupon expiration dates and terms of use and provide them to users at the optimal timing. For example, the coupon warehouse generation unit automatically monitors the expiration date of each coupon in the coupon warehouse and builds a system that prioritizes notifying users of coupons that are about to expire. For example, if a coupon's expiration date is within one week, a reminder is sent to the user. The coupon warehouse generation unit also automatically updates coupon terms of use and provides them at the optimal timing based on the user's behavioral history. For example, coupons that can be used on specific days of the week or during specific time periods are provided based on the user's past usage patterns. The coupon warehouse generation unit also develops a system that updates coupon expiration dates and terms of use in real time and provides them to users at the optimal timing. For example, when a user approaches a specific store, a coupon for that store is automatically provided. This allows coupon expiration dates and terms of use to be automatically updated and provided to users at the optimal timing.

[0031] The coupon warehouse generation unit generates a popularity ranking based on coupon usage history, and is able to prioritize displaying coupons that are popular with users. The coupon warehouse generation unit, for example, builds a system that collects coupon usage history in a coupon warehouse and generates a popularity ranking. For example, the most frequently used coupons in the past month are displayed in a ranking format. The coupon warehouse generation unit also develops a system that prioritizes displaying coupons that are popular with users based on the popularity ranking. For example, top-ranked coupons are displayed on the top page of the user's smartphone app. The coupon warehouse generation unit also builds a system that analyzes coupon usage history and updates the popularity ranking in real time. For example, the ranking is automatically updated each time a user uses a coupon. This allows a popularity ranking to be generated based on coupon usage history, and popular coupons to be prioritized and displayed.

[0032] The coupon warehouse generation unit adds a function that allows users to customize coupons themselves, and can generate coupons that meet individual needs. For example, the coupon warehouse generation unit adds a function to the coupon warehouse that allows users to customize coupon discount rates and expiration dates themselves. For example, users can adjust coupons to suit their budgets and planned usage. The coupon warehouse generation unit also develops a system that allows users to customize coupons based on specific conditions. For example, users can generate coupons that can be used on specific days of the week or during specific time periods. The coupon warehouse generation unit also adds a function to the coupon warehouse that allows users to customize coupons to suit their preferences. For example, users can generate coupons for their favorite stores or products. This allows users to customize coupons themselves and generate coupons that meet individual needs.

[0033] The coupon warehouse generation unit can add a function that displays which coupons other users are using in real time, allowing trends to be grasped. The coupon warehouse generation unit, for example, builds a system that displays which coupons other users are using in real time in the coupon warehouse. For example, it displays popular coupons and recently used coupons. The coupon warehouse generation unit also adds a function that displays other users' coupon usage status in real time, allowing trends to be grasped. For example, it displays coupons that are popular during specific time periods or on specific days of the week. The coupon warehouse generation unit also develops a system that displays other users' coupon usage history in real time in the coupon warehouse, allowing trends to be grasped. For example, it displays a ranking of recently used coupons. This makes it possible to display which coupons other users are using in real time, allowing trends to be grasped.

[0034] The behavioral history analysis unit can also include the content of a user's social media posts in its analysis. For example, the behavioral history analysis unit builds a system that collects the content of a user's social media posts and analyzes them together with past coupon usage history. For example, it analyzes coupon information shared by users on social media. The behavioral history analysis unit also analyzes the content of social media posts and develops a system that understands the user's interests and concerns. For example, it provides coupons related to topics that the user frequently posts about. The behavioral history analysis unit also builds a system that provides optimal coupons based on the content of a user's social media posts, analyzing them together with past coupon usage history. For example, it provides coupons related to products or services that the user mentioned on social media. By including the content of a user's social media posts in its analysis, it becomes possible to provide more accurate coupons.

[0035] The behavioral history analysis unit can predict a user's lifestyle and preferences and provide coupons based on them. The behavioral history analysis unit, for example, builds a system that predicts a user's lifestyle and preferences based on the analysis results of the user's behavioral history. For example, if the user is health-conscious, a coupon for health food is provided. The behavioral history analysis unit also analyzes the user's behavioral history and develops an algorithm that predicts the user's lifestyle and preferences. For example, a coupon is provided based on the places the user frequently visits and the services the user uses. The behavioral history analysis unit also builds a system that predicts a user's lifestyle and preferences based on the analysis results of the user's behavioral history and provides coupons based on them. For example, if the user likes outdoor activities, a coupon for outdoor equipment is provided. This makes it possible to predict a user's lifestyle and preferences and provide coupons based on them.

[0036] The behavior history analysis unit can provide common coupons to users who have similar behavior patterns compared with other users. The behavior history analysis unit, for example, builds a system that identifies users who have similar behavior patterns compared with other users based on the analysis results of user behavior history. For example, common coupons are provided to users who frequently use the same store. The behavior history analysis unit also develops a system that provides common coupons to users who have similar behavior patterns compared with other users. For example, specific coupons are provided to users who have the same hobbies or interests. The behavior history analysis unit also builds a system that provides common coupons to users who have similar behavior patterns compared with other users based on the analysis results of user behavior history. For example, a common coupon is provided to users who use a specific service during the same time period. This makes it possible to provide common coupons to users who have similar behavior patterns compared with other users.

[0037] The behavior history analysis unit can share the results of the analysis of user behavior history between different services and implement cross-promotion. The behavior history analysis unit, for example, builds a system that shares the analysis results of user behavior history between different services and implements cross-promotion. For example, a user who uses a coupon for a restaurant is provided with a coupon for an entertainment facility. The behavior history analysis unit also develops a system that shares user behavior history between different services and implements cross-promotion. For example, a user who uses a coupon for a shopping mall is provided with a coupon for a movie theater. The behavior history analysis unit also builds a system that implements cross-promotion between different services based on the analysis results of user behavior history. For example, a user who uses a coupon for a fitness gym is provided with a coupon for a health food. This allows sharing between different services and implementing cross-promotion.

[0038] The coupon issuing unit can utilize the user's location information to provide coupons for the store closest to the user's current location. The coupon issuing unit, for example, acquires the user's location information in real time and builds a system that provides coupons for the store closest to the user's current location. For example, when the user enters a specific area, coupons for stores in that area are automatically provided. The coupon issuing unit also develops a system that provides coupons for stores that the user is likely to visit based on the location information. For example, coupons for stores in areas that the user frequently visits are provided preferentially. The coupon issuing unit also utilizes the user's location information to build a system that provides coupons for the store closest to the user's current location in real time. For example, when the user approaches a specific store, the coupon for that store is automatically provided. In this way, the user's location information can be utilized to provide coupons for the store closest to the user's current location.

[0039] The coupon issuing unit can predict and provide a coupon that a user is likely to use next based on the user's past coupon usage history. The coupon issuing unit, for example, analyzes the user's past coupon usage history and builds a system that predicts the coupon that a user is likely to use next. For example, the coupon that a user will use next is predicted based on the pattern of coupons that the user has used in the past. The coupon issuing unit also develops a system that provides a coupon that a user is likely to use next based on the coupon usage history. For example, the coupon that a user will use next is provided based on the category of coupons that the user has used in the past. The coupon issuing unit also builds a system that predicts and provides a coupon that a user is likely to use next based on the user's past coupon usage history. For example, the coupon that a user will use next is predicted based on the frequency and timing of coupons that the user has used in the past. This makes it possible to predict and provide a coupon that a user is likely to use next based on the user's past coupon usage history.

[0040] The coupon issuing unit can add a function that allows users to share coupons with friends and family, thereby expanding the scope of coupon use. For example, the coupon issuing unit builds a system that adds a function that allows users to share coupons with friends and family when issuing and providing coupons. For example, the system allows users to share coupons on social media or messaging apps. The coupon issuing unit also develops a system that uses the sharing function to allow users to share coupons with friends and family, thereby expanding the scope of coupon use. For example, shared coupons are automatically added to the accounts of friends and family. The coupon issuing unit also builds a system that adds a function that allows users to share coupons with friends and family when issuing and providing coupons, thereby expanding the scope of coupon use. For example, shared coupons are notified to the smartphone apps of friends and family. This allows users to share coupons with friends and family, thereby expanding the scope of coupon use.

[0041] The coupon issuing unit can collect feedback when a user uses a coupon and reflect it in the next coupon offering. For example, the coupon issuing unit builds a system that collects feedback when a user uses a coupon when issuing and providing a coupon. For example, feedback is collected by having users answer a questionnaire after using a coupon. The coupon issuing unit also develops a system that reflects the user's feedback in the next coupon offering based on the user's feedback. For example, a coupon for which a user has given favorable feedback is offered again. The coupon issuing unit also builds a system that collects feedback when a user uses a coupon when issuing and providing a coupon and reflects the feedback in the next coupon offering. For example, the conditions of a coupon that a user has expressed dissatisfaction with are improved. In this way, feedback when a user uses a coupon can be collected and reflected in the next coupon offering.

[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 coupon management system may further include a health management unit that acquires a user's health data and provides coupons according to their health condition. For example, coupons for health foods or fitness gyms may be provided based on data acquired from the user's fitness tracker or smartwatch. The health management unit may also provide coupons according to the user's health goals. For example, if the user is trying to lose weight, coupons for low-calorie foods may be provided. The health management unit may also monitor the user's health condition and provide coupons for health checkups or medical services as needed. This allows the provision of coupons according to the user's health condition and supports health management.

[0044] The coupon management system can further include a gamification section that introduces gamification elements to increase users' purchasing motivation. For example, users can earn points each time they use a coupon, and use those points to exchange for special coupons or prizes. The gamification section can also create a system in which users can level up by using coupons and obtain higher-value coupons. For example, by using a certain number of coupons, users can advance to the next level and obtain special discounts or limited-edition coupons. The gamification section can also encourage competition among users in coupon usage, and display the results in a ranking format. This can increase users' purchasing motivation and increase the frequency of coupon usage.

[0045] The coupon management system may further include an advertisement providing unit that provides personalized advertisements based on a user's purchase history. For example, advertisements related to products or services that the user has previously purchased may be displayed. The advertisement providing unit may also customize advertisements based on the user's interests and provide more effective advertisements. For example, advertisements related to keywords frequently searched by the user may be displayed. The advertisement providing unit may also analyze the user's behavioral history and provide advertisements at the optimal timing. For example, when the user approaches a specific store, an advertisement for that store may be displayed. This allows personalized advertisements to be provided based on the user's purchase history, maximizing the effectiveness of advertising.

[0046] The coupon management system can further include a subscription unit that provides a subscription service based on a user's purchasing history. For example, a subscription service can be created that periodically provides coupons that the user frequently uses. The subscription unit can also analyze the user's purchasing history and propose an optimal subscription plan. For example, if a user frequently purchases products in a specific category, a subscription plan specialized for that category can be provided. The subscription unit can also customize the content of the subscription service based on the user's purchasing history. For example, products and services tailored to the user's preferences can be periodically provided. This makes it possible to provide a subscription service based on the user's purchasing history and promote usage.

[0047] The coupon management system may further include a recommendation unit that provides a recommendation function based on a user's purchasing history. For example, it may recommend coupons related to products or services that the user has previously purchased. The recommendation unit may also customize coupons based on the user's interests and provide more effective recommendations. For example, it may recommend coupons related to keywords that the user frequently searches for. The recommendation unit may also analyze the user's behavioral history and recommend coupons at the optimal time. For example, when the user approaches a specific store, it may recommend coupons for that store. This provides a recommendation function based on the user's purchasing history, thereby promoting the use of coupons.

[0048] The coupon management system can further include a loyalty unit that provides a loyalty program based on the user's purchasing history. For example, a user can earn points every time they purchase a certain amount of goods, and use those points to exchange for special coupons or prizes. The loyalty unit can also analyze the user's purchasing history and suggest the most suitable loyalty program. For example, if a user frequently purchases products in a specific category, a loyalty program specialized for that category can be provided. The loyalty unit can also customize the content of the loyalty program based on the user's purchasing history. For example, it can provide benefits and services tailored to the user's preferences. This makes it possible to provide a loyalty program based on the user's purchasing history and promote usage.

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

[0050] Step 1: The coupon warehouse generation unit generates a coupon warehouse. For example, the coupon warehouse generation unit collects, organizes, and stores coupon information issued by each SBG service. The coupon warehouse generation unit also generates a coupon warehouse containing various coupons, such as discount coupons for restaurants and discount coupons for admission to entertainment facilities. Step 2: The coupon issuing unit issues the most suitable coupon. For example, the coupon issuing unit issues the most suitable coupon for the user from the coupon warehouse based on the analysis results of the behavior history analysis unit. The coupon issuing unit also provides the coupon via the user's smartphone app, email, website, etc. For example, when the user opens the smartphone app, the most suitable coupon is automatically displayed. Step 3: The providing unit provides the coupons issued by the coupon issuing unit to the user, thereby enabling the user to easily find and use the most suitable coupon. Step 4: The monitoring unit monitors the usage of the coupons issued by the coupon issuing unit. This allows the monitoring unit to understand the usage of the coupons and use it for the next strategy. Step 5: The optimization unit optimizes the coupon issuance strategy based on the coupon usage status monitored by the monitoring unit. This ensures that the coupon issuance strategy is always adjusted based on the latest usage status, enabling effective coupon provision.

[0051] (Example 2) The coupon management system according to the embodiment of the present invention analyzes the user's behavioral history and provides optimal coupons. This allows users to effectively use coupons, thereby increasing the frequency of use and revenues of SBG as a whole.

[0052] A coupon management system according to an embodiment includes a coupon warehouse generation unit, a behavior history analysis unit, and a coupon issuing unit. The coupon warehouse generation unit generates a coupon warehouse. For example, the coupon warehouse generation unit collects, organizes, and stores coupon information issued by each SBG service. The coupon warehouse generation unit also generates a coupon warehouse containing various coupons, such as discount coupons for restaurants and discount coupons for admission to entertainment facilities. The behavior history analysis unit analyzes a user's behavior history. For example, the behavior history analysis unit collects and analyzes a user's past purchase history, visit history, and search history. The behavior history analysis unit also performs analysis to prioritize providing new restaurant coupons to users who have frequently used restaurant coupons in the past. The coupon issuing unit issues optimal coupons. For example, the coupon issuing unit issues optimal coupons to users from the coupon warehouse based on the analysis results of the behavior history analysis unit. The coupon issuing unit also provides coupons to users via smartphone apps, emails, websites, etc. For example, when a user opens a smartphone app, the optimal coupon is automatically displayed. As a result, the coupon management system according to the embodiment allows users to easily find and use the most suitable coupons, thereby improving the frequency of use and profits of SBG as a whole.

[0053] The coupon warehouse generation unit can automatically update coupon expiration dates and terms of use and provide them to users at the optimal timing. For example, the coupon warehouse generation unit automatically monitors the expiration date of each coupon in the coupon warehouse and builds a system that prioritizes notifying users of coupons that are about to expire. For example, if a coupon's expiration date is within one week, a reminder is sent to the user. The coupon warehouse generation unit also automatically updates coupon terms of use and provides them at the optimal timing based on the user's behavioral history. For example, coupons that can be used on specific days of the week or during specific time periods are provided based on the user's past usage patterns. The coupon warehouse generation unit also develops a system that updates coupon expiration dates and terms of use in real time and provides them to users at the optimal timing. For example, when a user approaches a specific store, a coupon for that store is automatically provided. This allows coupon expiration dates and terms of use to be automatically updated and provided to users at the optimal timing.

[0054] The coupon warehouse generation unit generates a popularity ranking based on coupon usage history, and is able to prioritize displaying coupons that are popular with users. The coupon warehouse generation unit, for example, builds a system that collects coupon usage history in a coupon warehouse and generates a popularity ranking. For example, the most frequently used coupons in the past month are displayed in a ranking format. The coupon warehouse generation unit also develops a system that prioritizes displaying coupons that are popular with users based on the popularity ranking. For example, top-ranked coupons are displayed on the top page of the user's smartphone app. The coupon warehouse generation unit also builds a system that analyzes coupon usage history and updates the popularity ranking in real time. For example, the ranking is automatically updated each time a user uses a coupon. This allows a popularity ranking to be generated based on coupon usage history, and popular coupons to be prioritized and displayed.

[0055] The coupon warehouse generation unit uses the emotion estimation function to analyze the emotions felt by users when they view coupons, and can prioritize displaying coupons that evoke positive emotions. For example, the coupon warehouse generation unit uses the emotion estimation function to analyze facial expressions and voices of users when they view coupons, and builds a system that prioritizes displaying coupons that evoke positive emotions. For example, coupons that make the user smile are displayed preferentially. The coupon warehouse generation unit also develops a system that analyzes users' emotional responses in real time and automatically selects coupons that evoke positive emotions. For example, coupons that the user is interested in are displayed preferentially. The coupon warehouse generation unit also builds a system that prioritizes displaying coupons that evoke positive emotions in users based on emotion estimation data. For example, coupons that the user has previously evoked positive emotions for are displayed again. This allows the emotion felt by users when they view coupons to be analyzed, and coupons that evoke positive emotions to be displayed preferentially.

[0056] The coupon warehouse generation unit adds a function that allows users to customize coupons themselves, and can generate coupons that meet individual needs. For example, the coupon warehouse generation unit adds a function to the coupon warehouse that allows users to customize coupon discount rates and expiration dates themselves. For example, users can adjust coupons to suit their budgets and planned usage. The coupon warehouse generation unit also develops a system that allows users to customize coupons based on specific conditions. For example, users can generate coupons that can be used on specific days of the week or during specific time periods. The coupon warehouse generation unit also adds a function to the coupon warehouse that allows users to customize coupons to suit their preferences. For example, users can generate coupons for their favorite stores or products. This allows users to customize coupons themselves and generate coupons that meet individual needs.

[0057] The coupon warehouse generation unit can add a function that displays which coupons other users are using in real time, allowing trends to be grasped. The coupon warehouse generation unit, for example, builds a system that displays which coupons other users are using in real time in the coupon warehouse. For example, it displays popular coupons and recently used coupons. The coupon warehouse generation unit also adds a function that displays other users' coupon usage status in real time, allowing trends to be grasped. For example, it displays coupons that are popular during specific time periods or on specific days of the week. The coupon warehouse generation unit also develops a system that displays other users' coupon usage history in real time in the coupon warehouse, allowing trends to be grasped. For example, it displays a ranking of recently used coupons. This makes it possible to display which coupons other users are using in real time, allowing trends to be grasped.

[0058] The coupon warehouse generation unit uses the emotion estimation function to monitor the emotions of users when selecting coupons in real time and can suggest optimal coupons. The coupon warehouse generation unit, for example, uses the emotion estimation function to monitor facial expressions and voices of users when selecting coupons in real time and build a system that suggests optimal coupons. For example, coupons that the user is interested in are preferentially suggested. The coupon warehouse generation unit also analyzes the user's emotional responses in real time and develops a system that suggests optimal coupons. For example, coupons for which the user has shown positive emotions are preferentially suggested. The coupon warehouse generation unit also builds a system that monitors the emotions of users when selecting coupons in real time and suggests optimal coupons based on the emotion estimation data. For example, coupons for which the user has shown positive emotions in the past are again suggested. In this way, the emotions of users when selecting coupons can be monitored in real time and optimal coupons can be suggested.

[0059] The behavioral history analysis unit can also include the content of a user's social media posts in its analysis. For example, the behavioral history analysis unit builds a system that collects the content of a user's social media posts and analyzes them together with past coupon usage history. For example, it analyzes coupon information shared by users on social media. The behavioral history analysis unit also analyzes the content of social media posts and develops a system that understands the user's interests and concerns. For example, it provides coupons related to topics that the user frequently posts about. The behavioral history analysis unit also builds a system that provides optimal coupons based on the content of a user's social media posts, analyzing them together with past coupon usage history. For example, it provides coupons related to products or services that the user mentioned on social media. By including the content of a user's social media posts in its analysis, it becomes possible to provide more accurate coupons.

[0060] The behavioral history analysis unit can predict a user's lifestyle and preferences and provide coupons based on them. The behavioral history analysis unit, for example, builds a system that predicts a user's lifestyle and preferences based on the analysis results of the user's behavioral history. For example, if the user is health-conscious, a coupon for health food is provided. The behavioral history analysis unit also analyzes the user's behavioral history and develops an algorithm that predicts the user's lifestyle and preferences. For example, a coupon is provided based on the places the user frequently visits and the services the user uses. The behavioral history analysis unit also builds a system that predicts a user's lifestyle and preferences based on the analysis results of the user's behavioral history and provides coupons based on them. For example, if the user likes outdoor activities, a coupon for outdoor equipment is provided. This makes it possible to predict a user's lifestyle and preferences and provide coupons based on them.

[0061] The behavioral history analysis unit uses the emotion estimation function to analyze emotional fluctuations from the user's behavioral history and can provide coupons according to the emotions. The behavioral history analysis unit, for example, uses the emotion estimation function to build a system that analyzes emotional fluctuations from the user's behavioral history. For example, a relaxation coupon is provided when the user is feeling stressed. The behavioral history analysis unit also analyzes the user's behavioral history and develops a system that monitors emotional fluctuations in real time. For example, a special coupon is provided when the user shows positive emotions. The behavioral history analysis unit also builds a system that analyzes emotional fluctuations from the user's behavioral history based on the emotion estimation data and provides coupons according to the emotions. For example, a coupon for a change of mood is provided when the user shows negative emotions. In this way, emotional fluctuations can be analyzed from the user's behavioral history and coupons according to the emotions can be provided.

[0062] The behavior history analysis unit can provide common coupons to users who have similar behavior patterns compared with other users. The behavior history analysis unit, for example, builds a system that identifies users who have similar behavior patterns compared with other users based on the analysis results of user behavior history. For example, common coupons are provided to users who frequently use the same store. The behavior history analysis unit also develops a system that provides common coupons to users who have similar behavior patterns compared with other users. For example, specific coupons are provided to users who have the same hobbies or interests. The behavior history analysis unit also builds a system that provides common coupons to users who have similar behavior patterns compared with other users based on the analysis results of user behavior history. For example, a common coupon is provided to users who use a specific service during the same time period. This makes it possible to provide common coupons to users who have similar behavior patterns compared with other users.

[0063] The behavior history analysis unit can share the results of the analysis of user behavior history between different services and implement cross-promotion. The behavior history analysis unit, for example, builds a system that shares the analysis results of user behavior history between different services and implements cross-promotion. For example, a user who uses a coupon for a restaurant is provided with a coupon for an entertainment facility. The behavior history analysis unit also develops a system that shares user behavior history between different services and implements cross-promotion. For example, a user who uses a coupon for a shopping mall is provided with a coupon for a movie theater. The behavior history analysis unit also builds a system that implements cross-promotion between different services based on the analysis results of user behavior history. For example, a user who uses a coupon for a fitness gym is provided with a coupon for a health food. This allows sharing between different services and implementing cross-promotion.

[0064] The behavioral history analysis unit uses the emotion estimation function to analyze emotional trends from the user's behavioral history and provide coupons based on the emotions. The behavioral history analysis unit, for example, uses the emotion estimation function to build a system that analyzes emotional trends from the user's behavioral history. For example, it identifies behavioral patterns that indicate a user's positive emotions and provides coupons based on those. The behavioral history analysis unit also analyzes the user's behavioral history and develops a system that monitors emotional trends in real time. For example, it identifies behavioral patterns that indicate a user's negative emotions and provides coupons based on those. The behavioral history analysis unit also builds a system that analyzes emotional trends from the user's behavioral history based on the emotion estimation data and provides coupons based on those emotions. For example, it provides a relaxation coupon if the user is feeling stressed. This makes it possible to analyze emotional trends from the user's behavioral history and provide coupons based on those emotions.

[0065] The coupon issuing unit can utilize the user's location information to provide coupons for the store closest to the user's current location. The coupon issuing unit, for example, acquires the user's location information in real time and builds a system that provides coupons for the store closest to the user's current location. For example, when the user enters a specific area, coupons for stores in that area are automatically provided. The coupon issuing unit also develops a system that provides coupons for stores that the user is likely to visit based on the location information. For example, coupons for stores in areas that the user frequently visits are provided preferentially. The coupon issuing unit also utilizes the user's location information to build a system that provides coupons for the store closest to the user's current location in real time. For example, when the user approaches a specific store, the coupon for that store is automatically provided. In this way, the user's location information can be utilized to provide coupons for the store closest to the user's current location.

[0066] The coupon issuing unit can predict and provide a coupon that a user is likely to use next based on the user's past coupon usage history. The coupon issuing unit, for example, analyzes the user's past coupon usage history and builds a system that predicts the coupon that a user is likely to use next. For example, the coupon that a user will use next is predicted based on the pattern of coupons that the user has used in the past. The coupon issuing unit also develops a system that provides a coupon that a user is likely to use next based on the coupon usage history. For example, the coupon that a user will use next is provided based on the category of coupons that the user has used in the past. The coupon issuing unit also builds a system that predicts and provides a coupon that a user is likely to use next based on the user's past coupon usage history. For example, the coupon that a user will use next is predicted based on the frequency and timing of coupons that the user has used in the past. This makes it possible to predict and provide a coupon that a user is likely to use next based on the user's past coupon usage history.

[0067] The coupon issuing unit can use the emotion estimation function to analyze the emotion a user has when receiving a coupon, and preferentially provide coupons that elicit positive emotions. For example, the coupon issuing unit uses the emotion estimation function to analyze the facial expression and voice of a user when receiving a coupon, and builds a system that preferentially provides coupons that elicit positive emotions. For example, coupons that make the user smile are preferentially provided. The coupon issuing unit also develops a system that analyzes the user's emotional reactions in real time and automatically selects coupons that elicit positive emotions. For example, coupons that the user has shown interest in are preferentially provided. The coupon issuing unit also builds a system that preferentially provides coupons that make the user feel positive emotions, based on the emotion estimation data. For example, coupons that the user has previously expressed positive emotions about are provided again. This makes it possible to analyze the emotion a user has when receiving a coupon, and preferentially provide coupons that elicit positive emotions.

[0068] The coupon issuing unit can add a function that allows users to share coupons with friends and family, thereby expanding the scope of coupon use. For example, the coupon issuing unit builds a system that adds a function that allows users to share coupons with friends and family when issuing and providing coupons. For example, the system allows users to share coupons on social media or messaging apps. The coupon issuing unit also develops a system that uses the sharing function to allow users to share coupons with friends and family, thereby expanding the scope of coupon use. For example, shared coupons are automatically added to the accounts of friends and family. The coupon issuing unit also builds a system that adds a function that allows users to share coupons with friends and family when issuing and providing coupons, thereby expanding the scope of coupon use. For example, shared coupons are notified to the smartphone apps of friends and family. This allows users to share coupons with friends and family, thereby expanding the scope of coupon use.

[0069] The coupon issuing unit can collect feedback when a user uses a coupon and reflect it in the next coupon offering. For example, the coupon issuing unit builds a system that collects feedback when a user uses a coupon when issuing and providing a coupon. For example, feedback is collected by having users answer a questionnaire after using a coupon. The coupon issuing unit also develops a system that reflects the user's feedback in the next coupon offering based on the user's feedback. For example, a coupon for which a user has given favorable feedback is offered again. The coupon issuing unit also builds a system that collects feedback when a user uses a coupon when issuing and providing a coupon and reflects the feedback in the next coupon offering. For example, the conditions of a coupon that a user has expressed dissatisfaction with are improved. In this way, feedback when a user uses a coupon can be collected and reflected in the next coupon offering.

[0070] The coupon issuing unit can use the emotion estimation function to monitor the emotion a user feels when using a coupon in real time and reflect it in providing the next coupon. For example, the coupon issuing unit uses the emotion estimation function to monitor the facial expressions and voice of a user when using a coupon in real time and build a system that reflects this in providing the next coupon. For example, the coupon issuing unit re-issues a coupon for which the user expressed positive emotion. The coupon issuing unit also develops a system that analyzes the user's emotional response in real time and reflects this in providing the next coupon. For example, the coupon issuing unit improves the conditions for a coupon for which the user expressed negative emotion. The coupon issuing unit also builds a system that monitors the emotion a user feels when using a coupon in real time and reflects this in providing the next coupon based on the emotion estimation data. For example, the coupon issuing unit re-issues a coupon for which the user expressed positive emotion in the past. This makes it possible to monitor the emotion a user feels when using a coupon in real time and reflect this in providing the next coupon.

[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 coupon management system may further include a health management unit that acquires a user's health data and provides coupons according to their health condition. For example, coupons for health foods or fitness gyms may be provided based on data acquired from the user's fitness tracker or smartwatch. The health management unit may also provide coupons according to the user's health goals. For example, if the user is trying to lose weight, coupons for low-calorie foods may be provided. The health management unit may also monitor the user's health condition and provide coupons for health checkups or medical services as needed. This allows the provision of coupons according to the user's health condition and supports health management.

[0073] The coupon management system can further include a gamification section that introduces gamification elements to increase users' purchasing motivation. For example, users can earn points each time they use a coupon, and use those points to exchange for special coupons or prizes. The gamification section can also create a system in which users can level up by using coupons and obtain higher-value coupons. For example, by using a certain number of coupons, users can advance to the next level and obtain special discounts or limited-edition coupons. The gamification section can also encourage competition among users in coupon usage, and display the results in a ranking format. This can increase users' purchasing motivation and increase the frequency of coupon usage.

[0074] The coupon management system may further include an advertisement providing unit that provides personalized advertisements based on a user's purchase history. For example, advertisements related to products or services that the user has previously purchased may be displayed. The advertisement providing unit may also customize advertisements based on the user's interests and provide more effective advertisements. For example, advertisements related to keywords frequently searched by the user may be displayed. The advertisement providing unit may also analyze the user's behavioral history and provide advertisements at the optimal timing. For example, when the user approaches a specific store, an advertisement for that store may be displayed. This allows personalized advertisements to be provided based on the user's purchase history, maximizing the effectiveness of advertising.

[0075] The coupon management system may further include an entertainment providing unit that estimates the user's emotions and provides music and videos corresponding to the emotions. For example, when the user is feeling stressed, the entertainment providing unit may provide relaxing music. The entertainment providing unit may also provide videos that further enhance positive emotions when the user shows positive emotions. For example, when the user smiles, the entertainment providing unit may display fun videos. The entertainment providing unit may also provide entertainment content personalized according to the user's emotions. For example, the entertainment providing unit may recommend movies or dramas in a genre that the user is interested in. This allows entertainment to be provided according to the user's emotions, improving the user experience.

[0076] The coupon management system may further include a feedback providing unit that estimates the user's emotions and provides feedback according to the emotions. For example, when the user expresses negative emotions, the feedback providing unit provides an encouraging message. The feedback providing unit may also provide a message praising the user's emotions when the user expresses positive emotions. For example, when a user shares a successful experience, the feedback providing unit may display a message such as "Congratulations!" The feedback providing unit may also provide personalized feedback according to the user's emotions. For example, when the user is feeling stressed, the feedback providing unit may suggest relaxation. This allows the system to provide feedback according to the user's emotions and improve the user experience.

[0077] The coupon management system can further include an expiration date adjustment unit that estimates a user's emotions and adjusts the expiration date of a coupon according to the emotion. For example, if the user is feeling stressed, the expiration date of the coupon is extended. The expiration date adjustment unit can also shorten the expiration date of a coupon when the user shows positive emotions in order to maintain that emotion. For example, if the user is excited, the expiration date adjustment unit can provide a special coupon that can be used for a short period of time. The expiration date adjustment unit can also adjust the expiration date of a coupon in real time according to the user's emotions. For example, when the user shows a specific emotion, the expiration date is set to match that emotion. This makes it possible to adjust the expiration date of a coupon according to the user's emotions and promote usage.

[0078] The coupon management system can further include a design change unit that estimates a user's emotions and changes the design of the coupon according to the emotion. For example, when a user expresses positive emotions, the design change unit can provide a coupon with a bright color or a fun design. The design change unit can also provide a coupon with a calm color or a relaxing design when a user expresses negative emotions. For example, when a user is feeling stressed, the design change unit can provide a coupon with a design that encourages relaxation. The design change unit can also change the design of the coupon in real time according to the user's emotions. For example, when a user expresses a specific emotion, the design can be set to match that emotion. This allows the design of the coupon to be changed according to the user's emotions, thereby promoting usage.

[0079] The coupon management system can further include a subscription unit that provides a subscription service based on a user's purchasing history. For example, a subscription service can be created that periodically provides coupons that the user frequently uses. The subscription unit can also analyze the user's purchasing history and propose an optimal subscription plan. For example, if a user frequently purchases products in a specific category, a subscription plan specialized for that category can be provided. The subscription unit can also customize the content of the subscription service based on the user's purchasing history. For example, products and services tailored to the user's preferences can be periodically provided. This makes it possible to provide a subscription service based on the user's purchasing history and promote usage.

[0080] The coupon management system may further include a recommendation unit that provides a recommendation function based on a user's purchasing history. For example, it may recommend coupons related to products or services that the user has previously purchased. The recommendation unit may also customize coupons based on the user's interests and provide more effective recommendations. For example, it may recommend coupons related to keywords that the user frequently searches for. The recommendation unit may also analyze the user's behavioral history and recommend coupons at the optimal time. For example, when the user approaches a specific store, it may recommend coupons for that store. This provides a recommendation function based on the user's purchasing history, thereby promoting the use of coupons.

[0081] The coupon management system can further include a loyalty unit that provides a loyalty program based on the user's purchasing history. For example, a user can earn points every time they purchase a certain amount of goods, and use those points to exchange for special coupons or prizes. The loyalty unit can also analyze the user's purchasing history and suggest the most suitable loyalty program. For example, if a user frequently purchases products in a specific category, a loyalty program specialized for that category can be provided. The loyalty unit can also customize the content of the loyalty program based on the user's purchasing history. For example, it can provide benefits and services tailored to the user's preferences. This makes it possible to provide a loyalty program based on the user's purchasing history and promote usage.

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

[0083] Step 1: The coupon warehouse generation unit generates a coupon warehouse. For example, the coupon warehouse generation unit collects, organizes, and stores coupon information issued by each SBG service. The coupon warehouse generation unit also generates a coupon warehouse containing various coupons, such as discount coupons for restaurants and discount coupons for admission to entertainment facilities. Step 2: The coupon issuing unit issues the most suitable coupon. For example, the coupon issuing unit issues the most suitable coupon for the user from the coupon warehouse based on the analysis results of the behavior history analysis unit. The coupon issuing unit also provides the coupon via the user's smartphone app, email, website, etc. For example, when the user opens the smartphone app, the most suitable coupon is automatically displayed. Step 3: The providing unit provides the coupons issued by the coupon issuing unit to the user, thereby enabling the user to easily find and use the most suitable coupon. Step 4: The monitoring unit monitors the usage of the coupons issued by the coupon issuing unit. This allows the monitoring unit to understand the usage of the coupons and use it for the next strategy. Step 5: The optimization unit optimizes the coupon issuance strategy based on the coupon usage status monitored by the monitoring unit. This ensures that the coupon issuance strategy is always adjusted based on the latest usage status, enabling effective coupon provision.

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

[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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

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

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 coupon warehouse generation unit for generating a coupon warehouse; a coupon issuing unit that issues coupons from the coupon warehouse generated by the coupon warehouse generating unit; a providing unit that provides the coupon issued by the coupon issuing unit to the user; a monitoring unit that monitors the usage status of coupons issued by the coupon issuing unit; an optimization unit that optimizes a coupon issuance strategy based on the coupon usage status monitored by the monitoring unit; A system characterized by:

2. The coupon warehouse generation unit The coupon expiration date and terms of use are automatically updated, and the coupon is provided to the user at the optimal time.

2. The system of claim 1.

3. The coupon warehouse generation unit A popularity ranking is generated based on the coupon usage history, and coupons that are popular with the user are preferentially displayed.

2. The system of claim 1.

4. The coupon warehouse generation unit Analyze the emotions felt by the user when viewing the coupons, and prioritize displaying coupons that evoke positive emotions 2. The system of claim 1.

5. The coupon warehouse generation unit Add a function that allows the user to customize the coupon themselves and generate coupons that meet individual needs.

2. The system of claim 1.

6. The coupon warehouse generation unit Add a feature that shows which coupons other users are using in real time and allows you to understand trends.

2. The system of claim 1.

7. The coupon warehouse generation unit The emotions of the user when selecting the coupon are monitored in real time, and the most suitable coupon is proposed.

2. The system of claim 1.

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