System

Abstract

An object of a system according to an embodiment is to efficiently perform coupon distribution and cashback in a restaurant.SOLUTION: A system includes a menu photographing unit, a AI chat bot unit, a mini game generation unit, a cashback unit, and a questionnaire analyzing unit. The menu shooting unit shoots a menu. The AI chat bot unit analyzes the menu image acquired by the menu photographing unit. The mini-game generation unit generates a mini-game in which a coupon is won based on the menu image analyzed by the AI chat bot unit. The cashback unit distributes a cashback coupon to a user who wins the mini game generated by the mini game generation unit, and performs cashback after payment. The questionnaire analysis part analyzes the questionnaire data collected by the cash back part and transmits a report to the store side.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 technology has the problem that the process of coupon distribution and cashback at restaurants is complicated and inefficient for both users and restaurants.

[0005] The system according to the embodiment aims to efficiently distribute coupons and provide cashback at restaurants. [Means for solving the problem]

[0006] The system according to the embodiment includes a menu capture unit, an AI chatbot unit, a mini-game generation unit, a cashback unit, and a survey analysis unit. The menu capture unit captures a menu. The AI ​​chatbot unit analyzes the menu image acquired by the menu capture unit. The mini-game generation unit generates a mini-game in which coupons can be won based on the menu image analyzed by the AI ​​chatbot unit. The cashback unit distributes cashback coupons to users who win the mini-game generated by the mini-game generation unit, and provides the cashback after payment. The survey analysis unit analyzes the survey data collected by the cashback unit and sends a report to the store. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently distribute coupons and provide cashback at restaurants. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The cashback coupon begging app according to an embodiment of the present invention is a system that allows users to request cashback coupons from restaurants that accept PayPay, even those that do not distribute coupons. This system allows users to enter a restaurant and take a photo of a menu item featuring their desired item, then send it to an AI chatbot to play a mini-game where they can win a coupon. Winning users receive a cashback coupon exclusively for PayPay payments. By ordering the desired item, paying with PayPay, and answering questions about the food and customer service, they can receive a cashback equivalent to the coupon amount. Users can also increase their cashback by watching commercials, answering surveys, or installing the restaurant's official app while waiting for their food. The collected survey data is automatically analyzed by a generation AI and a report is sent to the restaurant. This allows the cashback coupon begging app to improve user convenience and provide useful information to restaurants. For example, users can enjoy a meal at a discount using the coupon, and restaurants can improve their services based on user feedback.

[0029] A cashback coupon request app according to an embodiment includes a menu capture unit, an AI chatbot unit, a mini-game generation unit, a cashback unit, and a survey analysis unit. The menu capture unit acquires a menu image captured by a user. For example, the user may take a photo of a menu with a smartphone and upload the image to the app. The menu capture unit can also automatically adjust the image resolution and format and convert the image into a format that is easier to analyze. The AI ​​chatbot unit analyzes the menu image acquired by the menu capture unit. For example, the AI ​​chatbot unit may analyze the contents of the menu using an image recognition algorithm to identify the product the user wants to request. The AI ​​chatbot unit can also collect additional information through dialogue with the user. The mini-game generation unit generates a mini-game in which a coupon can be won based on the menu image analyzed by the AI ​​chatbot unit. For example, the mini-game generation unit may generate a slot machine-style mini-game or a simple quiz-style mini-game. The mini-game generation unit can also dynamically adjust the difficulty and rewards of the game. The cashback unit distributes cashback coupons to users who win a mini-game generated by the mini-game generation unit, and provides the cashback after payment. For example, the cashback unit distributes a cashback coupon exclusively for PayPay payments to users, and after the user uses the coupon to make a payment, the cashback unit provides a cashback amount equivalent to the coupon. The cashback unit can also increase the cashback amount by having the user answer questions about food and customer service. The survey analysis unit analyzes the survey data collected by the cashback unit and transmits a report to the store. For example, the survey analysis unit automatically analyzes the survey data using a generation AI and generates a report including suggestions for new product development and store improvement. The survey analysis unit can also customize the report for each store, detailing the strengths and areas for improvement of a specific store. As a result, the cashback coupon requesting app according to the embodiment can improve user convenience and provide useful information to stores.For example, users can use coupons to enjoy meals at a discount, and restaurants can improve their services based on user feedback.

[0030] In addition to analyzing menu images, the menu capture unit can also analyze the content of the user's verbal description using voice input to obtain more detailed information. For example, after a user takes a photo of a menu, the menu capture unit asks via voice input, "What does this dish taste like?" The AI ​​chatbot unit analyzes the image and voice to provide a detailed description of the dish. The menu capture unit can also analyze the content of the user's verbal description and combine it with the menu image to obtain more detailed information. For example, if a user asks, "Is this dish spicy?" the AI ​​chatbot unit analyzes the image and voice to provide information about the spiciness of the dish. This allows the content of the user's verbal description to be analyzed and more detailed information to be obtained.

[0031] The menu photographing unit can suggest optimal coupons based on the analysis results of the menu image, taking into consideration the user's past order history and preferences. For example, when a user photographs a menu, the menu photographing unit's AI chatbot unit references the user's past order history and suggests coupons for dishes that the user likes. The menu photographing unit can also suggest optimal coupons taking into consideration the user's preferences. For example, it can suggest coupons for similar dishes based on data on dishes the user has ordered in the past. This makes it possible to suggest optimal coupons taking into consideration the user's past order history and preferences.

[0032] The menu photographing unit can use AR technology to display detailed menu information and 3D models of dishes, attracting the user's interest. For example, when a user photographs a menu, the menu photographing unit uses AR technology to display a 3D model of the dish, providing detailed information. The menu photographing unit can also use AR technology to display detailed menu information. For example, it can display information about the ingredients, calories, allergy information, etc. of the dish. In this way, the menu photographing unit can use AR technology to display detailed menu information and 3D models of the dish, attracting the user's interest.

[0033] After photographing a menu, the menu photographing unit can display reviews and ratings from other users to help with selection. For example, when a user photographs a menu, the menu photographing unit can display reviews and ratings from other users to help with selection. The menu photographing unit can also display reviews and ratings to support the user's selection. For example, it can display ratings and comments on dishes to help with selection. This allows the user to display reviews and ratings from other users to help with selection.

[0034] The mini-game generation unit can adjust the difficulty of the mini-game based on the user's past game play data to provide an appropriate challenge. The mini-game generation unit, for example, analyzes the user's past game play data and adjusts the difficulty of the mini-game. For example, the mini-game generation unit sets the difficulty of a new game based on the difficulty of games that the user has previously succeeded in. The mini-game generation unit can also provide an appropriate challenge based on the user's game play data. For example, the mini-game generation unit adjusts the difficulty of a new game based on data of games that the user has previously attempted. In this way, the mini-game generation unit can adjust the difficulty of the mini-game based on the user's past game play data to provide an appropriate challenge.

[0035] The mini-game generation unit can provide different types of coupons depending on the result of the mini-game. The mini-game generation unit provides different types of coupons, such as discount coupons and free topping coupons, depending on the result of the mini-game. For example, a discount coupon is distributed when a player wins a slot machine-style game. The mini-game generation unit can also provide different types of coupons depending on the result of the mini-game. For example, a free topping coupon is distributed when a player answers correctly in a quiz-style game. In this way, different types of coupons can be provided depending on the result of the mini-game.

[0036] The mini-game generation unit can increase the variety of mini-games to allow the user to select from. The mini-game generation unit can, for example, increase the variety of mini-games to allow the user to select from. For example, puzzle games and action games can be added. The mini-game generation unit can also increase the variety of mini-games to allow the user to select from. For example, quiz games and slot machine-style games can be added. This increases the variety of mini-games to allow the user to select from.

[0037] The mini-game generation unit can add a function to share the results of a mini-game on social media, thereby encouraging competition with other users. The mini-game generation unit can add a function to share the results of a mini-game on social media, for example, to encourage competition with other users. For example, the game score or a coupon won can be shared. The mini-game generation unit can also encourage competition with other users by having users share on social media. For example, rankings and scoreboards can be displayed to encourage competition between users. This allows the results of a mini-game to be shared on social media, encouraging competition with other users.

[0038] The cashback unit can propose an optimal cashback rate at the time of payment based on the user's past payment history. For example, the cashback unit analyzes the user's past payment history at the time of payment and proposes an optimal cashback rate. For example, a high cashback rate is offered to users who use the service frequently. The cashback unit can also propose an optimal cashback rate based on the user's payment history. For example, the cashback rate is adjusted taking into account the amount and frequency of past payments. This makes it possible to propose an optimal cashback rate based on the user's past payment history.

[0039] The cashback unit can add personalized questions based on the user's past answer history to the post-payment survey. The cashback unit, for example, adds personalized questions based on the user's past answer history to the post-payment survey. For example, a user who has previously shown interest in the "taste of food" can be asked, "How did the food taste this time?" The cashback unit can also add personalized questions based on the user's answer history. For example, a specific question is added taking into account the content of past answers. This makes it possible to add personalized questions based on the user's past answer history.

[0040] The cashback unit can provide further benefits to users by coordinating with other cashback campaigns or point programs at the time of payment. For example, the cashback unit can provide additional cashback to users by coordinating with other cashback campaigns at the time of payment. For example, additional cashback can be provided when a specific credit card is used. The cashback unit can also provide points to users by coordinating with point programs. For example, points can be awarded according to the payment amount, and can be used as a discount on the next use. This allows for coordinating with other cashback campaigns or point programs to provide further benefits to users.

[0041] The cashback unit can add a function that allows a user to compare their cashback results with other users after payment. The cashback unit can add a function that allows a user to compare their cashback results with other users after payment, for example, by comparing the amount of cashback or frequency of use. The cashback unit can also promote competition by allowing a user to compare their results with other users. For example, rankings or scoreboards can be displayed to promote competition between users. This allows a function that allows a user to compare their cashback results with other users after payment.

[0042] The cashback unit can personalize the conditions for increasing the cashback amount based on the user's past behavioral history. For example, the cashback unit personalizes the conditions for increasing the cashback amount based on the user's past behavioral history. For example, it provides special increase conditions for users who use the service frequently. The cashback unit can also personalize the increase conditions based on the user's behavioral history. For example, it adjusts the increase conditions taking into account the frequency of use and payment amounts in the past. This allows the conditions for increasing the cashback amount to be personalized based on the user's past behavioral history.

[0043] The cashback unit can dynamically change the conditions for increasing the cashback amount based on the user's current location information and the time of day. The cashback unit, for example, dynamically changes the conditions for increasing the cashback amount based on the user's current location information. For example, special increase conditions are provided to users in specific areas. The cashback unit can also dynamically change the increase conditions based on the time of day. For example, the increase conditions are adjusted depending on peak hours and off-peak hours. This allows the conditions for increasing the cashback amount to be dynamically changed based on the user's current location information and the time of day.

[0044] The cashback unit can promote social interaction by adding a condition for increasing the cashback amount that the user invites a friend. The cashback unit can promote social interaction by adding a condition for increasing the cashback amount that the user invites a friend. For example, inviting a friend increases the cashback amount. The cashback unit can also promote social interaction by having the user invite a friend. For example, using the service with a friend provides a special increase condition. This can promote social interaction by adding a condition for increasing the cashback amount that the user invites a friend.

[0045] The cashback unit can alleviate congestion at the store by adding a condition for increasing the cashback amount that the user visits during a specific time period to the conditions for increasing the cashback amount. The cashback unit can, for example, alleviate congestion at the store by adding a condition for increasing the cashback amount that the user visits during a specific time period. For example, the cashback amount is increased if the user visits during a time other than lunchtime or dinnertime. The cashback unit can also alleviate congestion at the store by having the user visit during a specific time period. For example, a special increase condition is provided if the user visits outside of peak hours. This allows the condition for increasing the cashback amount to be added, and congestion at the store can be alleviated.

[0046] The survey analysis unit can provide more detailed insights by combining the user's past response history and behavioral history with the analysis of survey data. The survey analysis unit can provide more detailed insights by, for example, combining the user's past response history with the analysis of survey data. For example, it can identify trends by comparing past responses with current responses. The survey analysis unit can also provide more detailed insights by combining the user's behavioral history. For example, it can analyze past behavioral patterns and identify changes in the user's preferences and behavior. This makes it possible to provide more detailed insights by combining the user's past response history and behavioral history with the analysis of survey data.

[0047] The survey analysis unit can provide the results of the analysis of the survey data as a report customized for each store. For example, the survey analysis unit provides the results of the analysis of the survey data as a report customized for each store. For example, the strengths and areas for improvement of a specific store are described in detail. Furthermore, by providing a report customized for each store, the survey analysis unit can also make proposals according to the needs of the store. For example, specific improvement measures are proposed based on sales data and customer feedback of a specific store. In this way, the results of the analysis of the survey data can be provided as a report customized for each store.

[0048] The survey analysis unit can integrate the results of the survey data analysis with other data sources to provide a more comprehensive report. For example, the survey analysis unit can integrate the results of the survey data analysis with social media feedback to provide a comprehensive report. For example, user word-of-mouth and ratings can be included. The survey analysis unit can also integrate with other data sources to provide a more comprehensive report. For example, customer data and sales data can be integrated to provide detailed analysis results. This allows the results of the survey data analysis to be integrated with other data sources to provide a more comprehensive report.

[0049] The survey analysis unit can compare the results of the analysis of the survey data with different industries or regions and provide them as benchmarks. The survey analysis unit, for example, compares the results of the analysis of the survey data with different industries and provides them as benchmarks. For example, it compares the data with data from other companies in the same industry to identify strengths and weaknesses. The survey analysis unit can also compare data by region and provide them as benchmarks. For example, it compares the data with data from a specific region to analyze the characteristics of each region. This allows the results of the analysis of the survey data to be compared with different industries or regions and provided as benchmarks.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The menu capture unit acquires menu images captured by the user. For example, the user can capture a menu with their smartphone and upload the image to the app. The menu capture unit can also automatically adjust the image resolution and format and convert it into a format that is easy to analyze. The AI ​​chatbot unit analyzes the menu images captured by the menu capture unit. For example, the AI ​​chatbot unit can analyze the contents of the menu using an image recognition algorithm to identify the item the user wants to order. The AI ​​chatbot unit can also collect additional information through dialogue with the user. The mini-game generation unit generates a mini-game in which coupons can be won based on the menu image analyzed by the AI ​​chatbot unit. For example, the mini-game generation unit can generate a slot machine-style mini-game or a simple quiz-style mini-game. The mini-game generation unit can also dynamically adjust the game difficulty and rewards. The cashback unit distributes cashback coupons to users who win a mini-game generated by the mini-game generation unit and provides the cashback after payment. For example, the cashback unit distributes a cashback coupon exclusively for PayPay payments to users, and provides the cashback amount of the coupon after the user uses the coupon to make a payment. The cashback unit can also increase the cashback amount by having the user answer questions about the food and customer service. The survey analysis unit analyzes the survey data collected by the cashback unit and sends a report to the store. For example, the survey analysis unit can automatically analyze the survey data using a generation AI and generate a report including suggestions for new product development and store improvement. The survey analysis unit can also customize the report for each store and provide detailed descriptions of the strengths and areas for improvement of a specific store. This allows the cashback coupon request app according to the embodiment to improve user convenience and provide useful information to stores. For example, users can use coupons to enjoy meals at a discount, and stores can improve their services based on user feedback.

[0052] In addition to analyzing menu images, the menu capture unit can also analyze the user's verbal description using voice input to obtain more detailed information. For example, after a user takes a photo of a menu, they can ask via voice input, "What does this dish taste like?" The AI ​​chatbot unit analyzes the image and voice to provide a detailed description of the dish. The menu capture unit can also analyze the user's verbal description and combine it with the menu image to obtain more detailed information. For example, if a user asks, "Is this dish spicy?" the AI ​​chatbot unit analyzes the image and voice to provide information about the spiciness of the dish. This allows the user's verbal description to be analyzed and more detailed information to be obtained.

[0053] The menu photography unit can suggest optimal coupons based on the analysis results of the menu image, taking into consideration the user's past order history and preferences. For example, when a user takes a photo of a menu, the AI ​​chatbot unit references the user's past order history and suggests coupons for dishes that the user likes. The menu photography unit can also suggest optimal coupons, taking into consideration the user's preferences. For example, it can suggest coupons for similar dishes based on data on dishes the user has ordered in the past. This makes it possible to suggest optimal coupons, taking into consideration the user's past order history and preferences.

[0054] The menu photographing unit can use AR technology to display detailed menu information and 3D models of dishes, attracting the user's interest. For example, when a user photographs a menu, AR technology is used to display a 3D model of the dish, providing detailed information. The menu photographing unit can also use AR technology to display detailed menu information. For example, it can display information about the ingredients, calories, allergy information, etc. of the dish. In this way, AR technology can be used to display detailed menu information and 3D models of the dishes, attracting the user's interest.

[0055] After photographing a menu, the menu photographing unit can display reviews and ratings from other users to help with selection. For example, when a user photographs a menu, reviews and ratings from other users are displayed to help with selection. The menu photographing unit can also display reviews and ratings to support the user's selection. For example, ratings and comments on dishes can be displayed to help with selection. This allows the user to display reviews and ratings from other users to help with selection.

[0056] The mini-game generation unit can adjust the difficulty of the mini-game based on the user's past game play data to provide an appropriate challenge. For example, the unit analyzes the user's past game play data and adjusts the difficulty of the mini-game. For example, the unit sets the difficulty of a new game based on the difficulty of games that the user has previously successfully played. The mini-game generation unit can also provide an appropriate challenge based on the user's game play data. For example, the unit adjusts the difficulty of a new game based on data of games that the user has previously played. In this way, the mini-game difficulty can be adjusted based on the user's past game play data to provide an appropriate challenge.

[0057] The mini-game generation unit can provide different types of coupons depending on the result of the mini-game. For example, different types of coupons, such as discount coupons and free topping coupons, can be provided depending on the result of the mini-game. For example, a discount coupon can be distributed if a player wins a slot machine-style game. The mini-game generation unit can also provide different types of coupons depending on the result of the mini-game. For example, a free topping coupon can be distributed if a player answers correctly in a quiz-style game. This makes it possible to provide different types of coupons depending on the result of the mini-game.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The menu capture unit acquires a menu image taken by the user. For example, the user takes a photo of a menu with their smartphone and uploads the image to the app. The menu capture unit can also automatically adjust the resolution and format of the image and convert it into a format that is easy to analyze. Step 2: The AI ​​chatbot analyzes the menu image captured by the menu capture unit. For example, the AI ​​chatbot analyzes the menu contents using an image recognition algorithm to identify the item the user wants to order. The AI ​​chatbot can also collect additional information through dialogue with the user. Step 3: The mini-game generation unit generates a mini-game in which coupons can be won based on the menu image analyzed by the AI ​​chatbot unit. For example, the mini-game generation unit generates a slot machine-style mini-game or a simple quiz-style mini-game. The mini-game generation unit can also dynamically adjust the difficulty and rewards of the game. Step 4: The cashback unit distributes cashback coupons to users who win the mini-game generated by the mini-game generation unit, and provides the cashback after payment. For example, the cashback unit distributes a cashback coupon specifically for PayPay payments to users, and after the user uses the coupon to make a payment, the cashback unit provides the amount of the coupon as a cashback. The cashback unit can also increase the amount of the cashback by having the user answer questions about cooking and customer service. Step 5: The Survey Analysis Department analyzes the survey data collected by the Cash Back Department and sends a report to the store. For example, the Survey Analysis Department can automatically analyze the survey data using generation AI and generate a report that includes suggestions for new product development and store improvements. The Survey Analysis Department can also customize the report for each store, detailing the strengths and areas for improvement of a specific store.

[0060] (Example 2) The cashback coupon begging app according to an embodiment of the present invention is a system that allows users to request cashback coupons from restaurants that accept PayPay, even those that do not distribute coupons. This system allows users to enter a restaurant and take a photo of a menu item featuring their desired item, then send it to an AI chatbot to play a mini-game where they can win a coupon. Winning users receive a cashback coupon exclusively for PayPay payments. By ordering the desired item, paying with PayPay, and answering questions about the food and customer service, they can receive a cashback equivalent to the coupon amount. Users can also increase their cashback by watching commercials, answering surveys, or installing the restaurant's official app while waiting for their food. The collected survey data is automatically analyzed by a generation AI and a report is sent to the restaurant. This allows the cashback coupon begging app to improve user convenience and provide useful information to restaurants. For example, users can enjoy a meal at a discount using the coupon, and restaurants can improve their services based on user feedback.

[0061] A cashback coupon request app according to an embodiment includes a menu capture unit, an AI chatbot unit, a mini-game generation unit, a cashback unit, and a survey analysis unit. The menu capture unit acquires a menu image captured by a user. For example, the user may take a photo of a menu with a smartphone and upload the image to the app. The menu capture unit can also automatically adjust the image resolution and format and convert the image into a format that is easier to analyze. The AI ​​chatbot unit analyzes the menu image acquired by the menu capture unit. For example, the AI ​​chatbot unit may analyze the contents of the menu using an image recognition algorithm to identify the product the user wants to request. The AI ​​chatbot unit can also collect additional information through dialogue with the user. The mini-game generation unit generates a mini-game in which a coupon can be won based on the menu image analyzed by the AI ​​chatbot unit. For example, the mini-game generation unit may generate a slot machine-style mini-game or a simple quiz-style mini-game. The mini-game generation unit can also dynamically adjust the difficulty and rewards of the game. The cashback unit distributes cashback coupons to users who win a mini-game generated by the mini-game generation unit, and provides the cashback after payment. For example, the cashback unit distributes a cashback coupon exclusively for PayPay payments to users, and after the user uses the coupon to make a payment, the cashback unit provides a cashback amount equivalent to the coupon. The cashback unit can also increase the cashback amount by having the user answer questions about food and customer service. The survey analysis unit analyzes the survey data collected by the cashback unit and transmits a report to the store. For example, the survey analysis unit automatically analyzes the survey data using a generation AI and generates a report including suggestions for new product development and store improvement. The survey analysis unit can also customize the report for each store, detailing the strengths and areas for improvement of a specific store. As a result, the cashback coupon requesting app according to the embodiment can improve user convenience and provide useful information to stores.For example, users can use coupons to enjoy meals at a discount, and restaurants can improve their services based on user feedback.

[0062] In addition to analyzing menu images, the menu capture unit can also analyze the content of the user's verbal description using voice input to obtain more detailed information. For example, after a user takes a photo of a menu, the menu capture unit asks via voice input, "What does this dish taste like?" The AI ​​chatbot unit analyzes the image and voice to provide a detailed description of the dish. The menu capture unit can also analyze the content of the user's verbal description and combine it with the menu image to obtain more detailed information. For example, if a user asks, "Is this dish spicy?" the AI ​​chatbot unit analyzes the image and voice to provide information about the spiciness of the dish. This allows the content of the user's verbal description to be analyzed and more detailed information to be obtained.

[0063] The menu photographing unit can suggest optimal coupons based on the analysis results of the menu image, taking into consideration the user's past order history and preferences. For example, when a user photographs a menu, the menu photographing unit's AI chatbot unit references the user's past order history and suggests coupons for dishes that the user likes. The menu photographing unit can also suggest optimal coupons taking into consideration the user's preferences. For example, it can suggest coupons for similar dishes based on data on dishes the user has ordered in the past. This makes it possible to suggest optimal coupons taking into consideration the user's past order history and preferences.

[0064] The menu capture unit can use the emotion estimation function to analyze the emotions of the user when taking a photo of the menu and make menu suggestions that will elicit positive emotions. For example, the menu capture unit can analyze the user's facial expression when taking a photo of the menu and suggest, "This dish is particularly popular," to elicit positive emotions. The menu capture unit can also analyze the user's emotions and make menu suggestions that will elicit positive emotions. For example, if the user takes a photo of the menu with a smile, the AI ​​chatbot unit can suggest, "This dish is especially recommended." This makes it possible to analyze the user's emotions and make menu suggestions that will elicit positive emotions.

[0065] The menu photographing unit can use AR technology to display detailed menu information and 3D models of dishes, attracting the user's interest. For example, when a user photographs a menu, the menu photographing unit uses AR technology to display a 3D model of the dish, providing detailed information. The menu photographing unit can also use AR technology to display detailed menu information. For example, it can display information about the ingredients, calories, allergy information, etc. of the dish. In this way, the menu photographing unit can use AR technology to display detailed menu information and 3D models of the dish, attracting the user's interest.

[0066] After photographing a menu, the menu photographing unit can display reviews and ratings from other users to help with selection. For example, when a user photographs a menu, the menu photographing unit can display reviews and ratings from other users to help with selection. The menu photographing unit can also display reviews and ratings to support the user's selection. For example, it can display ratings and comments on dishes to help with selection. This allows the user to display reviews and ratings from other users to help with selection.

[0067] The menu capture unit can use the emotion estimation function to analyze the user's emotions in real time when taking a photo of a menu and provide interactive feedback to elicit positive emotions. For example, the menu capture unit can analyze the user's facial expressions in real time when taking a photo of a menu and provide feedback such as "This dish is particularly popular" to elicit positive emotions. The menu capture unit can also analyze the user's emotions in real time and provide interactive feedback to elicit positive emotions. For example, if the user takes a photo of the menu with a smile, the AI ​​chatbot unit can provide feedback such as "This dish is particularly recommended." This makes it possible to analyze the user's emotions in real time and provide interactive feedback to elicit positive emotions.

[0068] The mini-game generation unit can adjust the difficulty of the mini-game based on the user's past game play data to provide an appropriate challenge. The mini-game generation unit, for example, analyzes the user's past game play data and adjusts the difficulty of the mini-game. For example, the mini-game generation unit sets the difficulty of a new game based on the difficulty of games that the user has previously succeeded in. The mini-game generation unit can also provide an appropriate challenge based on the user's game play data. For example, the mini-game generation unit adjusts the difficulty of a new game based on data of games that the user has previously attempted. In this way, the mini-game generation unit can adjust the difficulty of the mini-game based on the user's past game play data to provide an appropriate challenge.

[0069] The mini-game generation unit can provide different types of coupons depending on the result of the mini-game. The mini-game generation unit provides different types of coupons, such as discount coupons and free topping coupons, depending on the result of the mini-game. For example, a discount coupon is distributed when a player wins a slot machine-style game. The mini-game generation unit can also provide different types of coupons depending on the result of the mini-game. For example, a free topping coupon is distributed when a player answers correctly in a quiz-style game. In this way, different types of coupons can be provided depending on the result of the mini-game.

[0070] The mini-game generation unit can use the emotion estimation function to analyze the user's emotions during the mini-game and design the game to elicit positive emotions. For example, the mini-game generation unit can analyze the user's facial expressions during the mini-game and adjust the game difficulty and rewards to elicit positive emotions. The mini-game generation unit can also analyze the user's emotions and design the game to elicit positive emotions. For example, if the user is playing the game with a smile, the game difficulty can be slightly increased to provide a challenge. This allows the user's emotions during the mini-game to be analyzed and the game can be designed to elicit positive emotions.

[0071] The mini-game generation unit can increase the variety of mini-games to allow the user to select from. The mini-game generation unit can, for example, increase the variety of mini-games to allow the user to select from. For example, puzzle games and action games can be added. The mini-game generation unit can also increase the variety of mini-games to allow the user to select from. For example, quiz games and slot machine-style games can be added. This increases the variety of mini-games to allow the user to select from.

[0072] The mini-game generation unit can add a function to share the results of a mini-game on social media, thereby encouraging competition with other users. The mini-game generation unit can add a function to share the results of a mini-game on social media, for example, to encourage competition with other users. For example, the game score or a coupon won can be shared. The mini-game generation unit can also encourage competition with other users by having users share on social media. For example, rankings and scoreboards can be displayed to encourage competition between users. This allows the results of a mini-game to be shared on social media, encouraging competition with other users.

[0073] The mini-game generation unit can use the emotion estimation function to analyze the user's emotions during the mini-game in real time and provide interactive feedback to elicit positive emotions. For example, the mini-game generation unit can analyze the user's facial expressions during the mini-game in real time and provide feedback such as "Good job!" to elicit positive emotions. The mini-game generation unit can also analyze the user's emotions in real time and provide interactive feedback to elicit positive emotions. For example, if the user is playing the game with a smile, the mini-game generation unit can slightly increase the difficulty of the game to provide a challenge. This makes it possible to analyze the user's emotions during the mini-game in real time and provide interactive feedback to elicit positive emotions.

[0074] The cashback unit can propose an optimal cashback rate at the time of payment based on the user's past payment history. For example, the cashback unit analyzes the user's past payment history at the time of payment and proposes an optimal cashback rate. For example, a high cashback rate is offered to users who use the service frequently. The cashback unit can also propose an optimal cashback rate based on the user's payment history. For example, the cashback rate is adjusted taking into account the amount and frequency of past payments. This makes it possible to propose an optimal cashback rate based on the user's past payment history.

[0075] The cashback unit can add personalized questions based on the user's past answer history to the post-payment survey. The cashback unit, for example, adds personalized questions based on the user's past answer history to the post-payment survey. For example, a user who has previously shown interest in the "taste of food" can be asked, "How did the food taste this time?" The cashback unit can also add personalized questions based on the user's answer history. For example, a specific question is added taking into account the content of past answers. This makes it possible to add personalized questions based on the user's past answer history.

[0076] The cashback unit can use the emotion estimation function to analyze the user's emotion when making a payment and provide interactive feedback to elicit positive emotions. For example, the cashback unit can analyze the user's facial expression when making a payment and provide feedback such as "Thank you for using our service!" to elicit positive emotions. The cashback unit can also analyze the user's emotion and provide interactive feedback to elicit positive emotions. For example, if the user makes a payment with a smile, a special message can be displayed. This makes it possible to analyze the user's emotion when making a payment and provide interactive feedback to elicit positive emotions.

[0077] The cashback unit can provide further benefits to users by coordinating with other cashback campaigns or point programs at the time of payment. For example, the cashback unit can provide additional cashback to users by coordinating with other cashback campaigns at the time of payment. For example, additional cashback can be provided when a specific credit card is used. The cashback unit can also provide points to users by coordinating with point programs. For example, points can be awarded according to the payment amount, and can be used as a discount on the next use. This allows for coordinating with other cashback campaigns or point programs to provide further benefits to users.

[0078] The cashback unit can add a function that allows a user to compare their cashback results with other users after payment. The cashback unit can add a function that allows a user to compare their cashback results with other users after payment, for example, by comparing the amount of cashback or frequency of use. The cashback unit can also promote competition by allowing a user to compare their results with other users. For example, rankings or scoreboards can be displayed to promote competition between users. This allows a function that allows a user to compare their cashback results with other users after payment.

[0079] The cashback unit can use the emotion estimation function to analyze the user's emotion at the time of payment in real time and provide interactive feedback to elicit positive emotions. For example, the cashback unit can analyze the user's facial expression at the time of payment in real time and provide feedback such as "Thank you for using our service!" to elicit positive emotions. The cashback unit can also analyze the user's emotion in real time and provide interactive feedback to elicit positive emotions. For example, if the user makes payment with a smile, a special message can be displayed. This makes it possible to analyze the user's emotion at the time of payment in real time and provide interactive feedback to elicit positive emotions.

[0080] The cashback unit can personalize the conditions for increasing the cashback amount based on the user's past behavioral history. For example, the cashback unit personalizes the conditions for increasing the cashback amount based on the user's past behavioral history. For example, it provides special increase conditions for users who use the service frequently. The cashback unit can also personalize the increase conditions based on the user's behavioral history. For example, it adjusts the increase conditions taking into account the frequency of use and payment amounts in the past. This allows the conditions for increasing the cashback amount to be personalized based on the user's past behavioral history.

[0081] The cashback unit can dynamically change the conditions for increasing the cashback amount based on the user's current location information and the time of day. The cashback unit, for example, dynamically changes the conditions for increasing the cashback amount based on the user's current location information. For example, special increase conditions are provided to users in specific areas. The cashback unit can also dynamically change the increase conditions based on the time of day. For example, the increase conditions are adjusted depending on peak hours and off-peak hours. This allows the conditions for increasing the cashback amount to be dynamically changed based on the user's current location information and the time of day.

[0082] The cashback unit can use the emotion estimation function to analyze the user's emotions when presenting the conditions for increasing the cashback amount, and provide interactive feedback to elicit positive emotions. For example, the cashback unit can analyze the user's facial expression when presenting the conditions for increasing the cashback amount, and provide feedback such as "We have prepared special conditions for increasing the cashback amount!" to elicit positive emotions. The cashback unit can also analyze the user's emotions and provide interactive feedback to elicit positive emotions. For example, if the user confirms the conditions with a smile, a special message is displayed. This makes it possible to analyze the user's emotions when presenting the conditions for increasing the cashback amount, and provide interactive feedback to elicit positive emotions.

[0083] The cashback unit can promote social interaction by adding a condition for increasing the cashback amount that the user invites a friend. The cashback unit can promote social interaction by adding a condition for increasing the cashback amount that the user invites a friend. For example, inviting a friend increases the cashback amount. The cashback unit can also promote social interaction by having the user invite a friend. For example, using the service with a friend provides a special increase condition. This can promote social interaction by adding a condition for increasing the cashback amount that the user invites a friend.

[0084] The cashback unit can alleviate congestion at the store by adding a condition for increasing the cashback amount that the user visits during a specific time period to the conditions for increasing the cashback amount. The cashback unit can, for example, alleviate congestion at the store by adding a condition for increasing the cashback amount that the user visits during a specific time period. For example, the cashback amount is increased if the user visits during a time other than lunchtime or dinnertime. The cashback unit can also alleviate congestion at the store by having the user visit during a specific time period. For example, a special increase condition is provided if the user visits outside of peak hours. This allows the condition for increasing the cashback amount to be added, and congestion at the store can be alleviated.

[0085] The cashback unit can use the emotion estimation function to analyze the user's emotions in real time when presenting the conditions for increasing the cashback amount, and provide interactive feedback to elicit positive emotions. For example, the cashback unit can analyze the user's facial expressions in real time when presenting the conditions for increasing the cashback amount, and provide feedback such as "We have prepared special conditions for increasing the cashback amount!" to elicit positive emotions. The cashback unit can also analyze the user's emotions in real time and provide interactive feedback to elicit positive emotions. For example, if the user confirms the conditions with a smile, a special message is displayed. This makes it possible to analyze the user's emotions in real time when presenting the conditions for increasing the cashback amount, and provide interactive feedback to elicit positive emotions.

[0086] The survey analysis unit can provide more detailed insights by combining the user's past response history and behavioral history with the analysis of survey data. The survey analysis unit can provide more detailed insights by, for example, combining the user's past response history with the analysis of survey data. For example, it can identify trends by comparing past responses with current responses. The survey analysis unit can also provide more detailed insights by combining the user's behavioral history. For example, it can analyze past behavioral patterns and identify changes in the user's preferences and behavior. This makes it possible to provide more detailed insights by combining the user's past response history and behavioral history with the analysis of survey data.

[0087] The survey analysis unit can provide the results of the analysis of the survey data as a report customized for each store. For example, the survey analysis unit provides the results of the analysis of the survey data as a report customized for each store. For example, the strengths and areas for improvement of a specific store are described in detail. Furthermore, by providing a report customized for each store, the survey analysis unit can also make proposals according to the needs of the store. For example, specific improvement measures are proposed based on sales data and customer feedback of a specific store. In this way, the results of the analysis of the survey data can be provided as a report customized for each store.

[0088] The survey analysis unit can use the emotion estimation function to analyze the user's emotions when answering a survey and provide interactive feedback to elicit positive emotions. For example, the survey analysis unit can analyze the user's facial expressions when answering a survey and provide feedback such as "Thank you for your cooperation!" to elicit positive emotions. The survey analysis unit can also analyze the user's emotions and provide interactive feedback to elicit positive emotions. For example, if the user answers the survey with a smile, a special message can be displayed. This makes it possible to analyze the user's emotions when answering a survey and provide interactive feedback to elicit positive emotions.

[0089] The survey analysis unit can integrate the results of the survey data analysis with other data sources to provide a more comprehensive report. For example, the survey analysis unit can integrate the results of the survey data analysis with social media feedback to provide a comprehensive report. For example, user word-of-mouth and ratings can be included. The survey analysis unit can also integrate with other data sources to provide a more comprehensive report. For example, customer data and sales data can be integrated to provide detailed analysis results. This allows the results of the survey data analysis to be integrated with other data sources to provide a more comprehensive report.

[0090] The survey analysis unit can compare the results of the analysis of the survey data with different industries or regions and provide them as benchmarks. The survey analysis unit, for example, compares the results of the analysis of the survey data with different industries and provides them as benchmarks. For example, it compares the data with data from other companies in the same industry to identify strengths and weaknesses. The survey analysis unit can also compare data by region and provide them as benchmarks. For example, it compares the data with data from a specific region to analyze the characteristics of each region. This allows the results of the analysis of the survey data to be compared with different industries or regions and provided as benchmarks.

[0091] The survey analysis unit can use the emotion estimation function to analyze the user's emotions in real time when answering a survey and provide interactive feedback to elicit positive emotions. For example, the survey analysis unit can analyze the user's facial expressions in real time when answering a survey and provide feedback such as "Thank you for your cooperation!" to elicit positive emotions. The survey analysis unit can also analyze the user's emotions in real time and provide interactive feedback to elicit positive emotions. For example, if the user answers the survey with a smile, a special message can be displayed. This makes it possible to analyze the user's emotions in real time when answering a survey and provide interactive feedback to elicit positive emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The menu capture unit acquires menu images captured by the user. For example, the user can capture a menu with their smartphone and upload the image to the app. The menu capture unit can also automatically adjust the image resolution and format and convert it into a format that is easy to analyze. The AI ​​chatbot unit analyzes the menu images captured by the menu capture unit. For example, the AI ​​chatbot unit can analyze the contents of the menu using an image recognition algorithm to identify the item the user wants to order. The AI ​​chatbot unit can also collect additional information through dialogue with the user. The mini-game generation unit generates a mini-game in which coupons can be won based on the menu image analyzed by the AI ​​chatbot unit. For example, the mini-game generation unit can generate a slot machine-style mini-game or a simple quiz-style mini-game. The mini-game generation unit can also dynamically adjust the game difficulty and rewards. The cashback unit distributes cashback coupons to users who win a mini-game generated by the mini-game generation unit and provides the cashback after payment. For example, the cashback unit distributes a cashback coupon exclusively for PayPay payments to users, and provides the cashback amount of the coupon after the user uses the coupon to make a payment. The cashback unit can also increase the cashback amount by having the user answer questions about the food and customer service. The survey analysis unit analyzes the survey data collected by the cashback unit and sends a report to the store. For example, the survey analysis unit can automatically analyze the survey data using a generation AI and generate a report including suggestions for new product development and store improvement. The survey analysis unit can also customize the report for each store and provide detailed descriptions of the strengths and areas for improvement of a specific store. This allows the cashback coupon request app according to the embodiment to improve user convenience and provide useful information to stores. For example, users can use coupons to enjoy meals at a discount, and stores can improve their services based on user feedback.

[0094] In addition to analyzing menu images, the menu capture unit can also analyze the user's verbal description using voice input to obtain more detailed information. For example, after a user takes a photo of a menu, they can ask via voice input, "What does this dish taste like?" The AI ​​chatbot unit analyzes the image and voice to provide a detailed description of the dish. The menu capture unit can also analyze the user's verbal description and combine it with the menu image to obtain more detailed information. For example, if a user asks, "Is this dish spicy?" the AI ​​chatbot unit analyzes the image and voice to provide information about the spiciness of the dish. This allows the user's verbal description to be analyzed and more detailed information to be obtained.

[0095] The menu photography unit can suggest optimal coupons based on the analysis results of the menu image, taking into consideration the user's past order history and preferences. For example, when a user takes a photo of a menu, the AI ​​chatbot unit references the user's past order history and suggests coupons for dishes that the user likes. The menu photography unit can also suggest optimal coupons, taking into consideration the user's preferences. For example, it can suggest coupons for similar dishes based on data on dishes the user has ordered in the past. This makes it possible to suggest optimal coupons, taking into consideration the user's past order history and preferences.

[0096] The menu capture unit can use the emotion estimation function to analyze the emotions of the user when taking a photo of the menu and make menu suggestions that will elicit positive emotions. For example, it can analyze the user's facial expression when taking a photo of the menu and suggest "This dish is particularly popular" to elicit positive emotions. The menu capture unit can also analyze the user's emotions and make menu suggestions that will elicit positive emotions. For example, if the user takes a photo of the menu with a smile, the AI ​​chatbot unit will suggest "This dish is especially recommended." This makes it possible to analyze the user's emotions and make menu suggestions that will elicit positive emotions.

[0097] The menu photographing unit can use AR technology to display detailed menu information and 3D models of dishes, attracting the user's interest. For example, when a user photographs a menu, AR technology is used to display a 3D model of the dish, providing detailed information. The menu photographing unit can also use AR technology to display detailed menu information. For example, it can display information about the ingredients, calories, allergy information, etc. of the dish. In this way, AR technology can be used to display detailed menu information and 3D models of the dishes, attracting the user's interest.

[0098] After photographing a menu, the menu photographing unit can display reviews and ratings from other users to help with selection. For example, when a user photographs a menu, reviews and ratings from other users are displayed to help with selection. The menu photographing unit can also display reviews and ratings to support the user's selection. For example, ratings and comments on dishes can be displayed to help with selection. This allows the user to display reviews and ratings from other users to help with selection.

[0099] The menu capture unit can use the emotion estimation function to analyze the user's emotions in real time when taking a photo of a menu and provide interactive feedback to elicit positive emotions. For example, the menu capture unit can analyze the user's facial expressions in real time when taking a photo of a menu and provide feedback such as "This dish is particularly popular" to elicit positive emotions. The menu capture unit can also analyze the user's emotions in real time and provide interactive feedback to elicit positive emotions. For example, if the user takes a photo of the menu with a smile, the AI ​​chatbot unit can provide feedback such as "This dish is particularly recommended." This makes it possible to analyze the user's emotions in real time and provide interactive feedback to elicit positive emotions.

[0100] The mini-game generation unit can adjust the difficulty of the mini-game based on the user's past game play data to provide an appropriate challenge. For example, the unit analyzes the user's past game play data and adjusts the difficulty of the mini-game. For example, the unit sets the difficulty of a new game based on the difficulty of games that the user has previously successfully played. The mini-game generation unit can also provide an appropriate challenge based on the user's game play data. For example, the unit adjusts the difficulty of a new game based on data of games that the user has previously played. In this way, the mini-game difficulty can be adjusted based on the user's past game play data to provide an appropriate challenge.

[0101] The mini-game generation unit can provide different types of coupons depending on the result of the mini-game. For example, different types of coupons, such as discount coupons and free topping coupons, can be provided depending on the result of the mini-game. For example, a discount coupon can be distributed if a player wins a slot machine-style game. The mini-game generation unit can also provide different types of coupons depending on the result of the mini-game. For example, a free topping coupon can be distributed if a player answers correctly in a quiz-style game. This makes it possible to provide different types of coupons depending on the result of the mini-game.

[0102] The mini-game generation unit can use the emotion estimation function to analyze the user's emotions during the mini-game and design the game to elicit positive emotions. For example, the unit can analyze the user's facial expressions during the mini-game and adjust the game difficulty and rewards to elicit positive emotions. The mini-game generation unit can also analyze the user's emotions and design the game to elicit positive emotions. For example, if the user is playing the game with a smile, the unit can slightly increase the game difficulty to provide a challenge. This allows the user's emotions during the mini-game to be analyzed and the game to elicit positive emotions to be designed.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The menu capture unit acquires a menu image taken by the user. For example, the user takes a photo of a menu with their smartphone and uploads the image to the app. The menu capture unit can also automatically adjust the resolution and format of the image and convert it into a format that is easy to analyze. Step 2: The AI ​​chatbot analyzes the menu image captured by the menu capture unit. For example, the AI ​​chatbot analyzes the menu contents using an image recognition algorithm to identify the item the user wants to order. The AI ​​chatbot can also collect additional information through dialogue with the user. Step 3: The mini-game generation unit generates a mini-game in which coupons can be won based on the menu image analyzed by the AI ​​chatbot unit. For example, the mini-game generation unit generates a slot machine-style mini-game or a simple quiz-style mini-game. The mini-game generation unit can also dynamically adjust the difficulty and rewards of the game. Step 4: The cashback unit distributes cashback coupons to users who win the mini-game generated by the mini-game generation unit, and provides the cashback after payment. For example, the cashback unit distributes a cashback coupon specifically for PayPay payments to users, and after the user uses the coupon to make a payment, the cashback unit provides the amount of the coupon as a cashback. The cashback unit can also increase the amount of the cashback by having the user answer questions about cooking and customer service. Step 5: The Survey Analysis Department analyzes the survey data collected by the Cash Back Department and sends a report to the store. For example, the Survey Analysis Department can automatically analyze the survey data using generation AI and generate a report that includes suggestions for new product development and store improvements. The Survey Analysis Department can also customize the report for each store, detailing the strengths and areas for improvement of a specific store.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0137] The data processing system 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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. The system comprises a menu photographing unit that photographs a menu, an AI chatbot unit that analyzes the menu image captured by the menu photographing unit, a mini-game generating unit that generates a mini-game in which coupons can be won based on the menu image analyzed by the AI ​​chatbot unit, a cashback unit that distributes cashback coupons to users who win the mini-game generated by the mini-game generating unit and provides the cashback after payment, and a survey analysis unit that analyzes survey data collected by the cashback unit and transmits a report to the store side. A system characterized by:

2. The menu photography unit uses AR technology to display detailed menu information and 3D models of dishes, attracting the user's interest. The system of claim 1 .

3. The mini-game generation unit adjusts the difficulty of the mini-game based on the user's past game play data to provide an appropriate challenge. The system of claim 1 .

4. The cashback unit proposes an optimal cashback rate based on the user's past payment history at the time of payment. The system of claim 1 .

5. The survey analysis unit combines the analysis of the survey data with the user's past response history and behavior history to provide more detailed insights. The system of claim 1 .

6. The menu photographing unit uses an emotion estimation function to analyze the emotion of the user when photographing the menu and propose a menu that will elicit positive emotions. The system of claim 1 .

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