System

Abstract

A system according to an embodiment aims to provide motivation for digital dependents to participate in real life activities and to balance digital and real life.SOLUTION: A system according to an embodiment includes a quest setting unit, a reward providing unit, and a payment cooperation unit. The quest setting unit sets a real quest using the generated AI. The reward providing unit provides a digital reward to a user who has cleared the quest set by the quest setting unit. The payment federation unit adds the digital reward provided by the reward providing unit to the user's account through the electronic payment service.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced challenges such as a lack of motivation for digital addicts to participate in real-world activities, making it difficult to balance the digital and real world.

[0005] The system according to the embodiment aims to provide motivation for digital addicts to participate in real-life activities and to balance the digital and real world. [Means for solving the problem]

[0006] The system according to the embodiment includes a quest setting unit, a reward providing unit, and a payment linking unit. The quest setting unit sets a real-world quest using a generation AI. The reward providing unit provides a digital reward to a user who completes the quest set by the quest setting unit. The payment linking unit adds the digital reward provided by the reward providing unit to the user's account via an electronic payment service. [Effects of the Invention]

[0007] The system according to the embodiment provides motivation for digital addicts to participate in real-life activities, allowing them to balance the digital and real world. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The reward system according to an embodiment of the present invention is a system that combines generation AI and electronic payment services. This system provides digitally addicted individuals with digital rewards through real-world activities. This allows digitally addicted individuals to obtain digital rewards through real-world activities, thereby restoring the balance between the digital world and reality.

[0029] A reward system according to an embodiment includes a quest setting unit, a reward providing unit, and a payment linking unit. The quest setting unit sets real-world quests using a generation AI. For example, the generation AI sets quests for a user, such as participating in a local event, visiting a specific location, or volunteering. The generation AI can also customize quests based on the user's interests. The reward providing unit provides digital rewards to users who complete quests set by the quest setting unit. For example, digital rewards may include special digital art, limited music tracks, or rare game items. The reward providing unit analyzes the user's progress in completing the quest and provides appropriate rewards. The payment linking unit adds the digital rewards provided by the reward providing unit to the user's account through an electronic payment service. For example, when a user completes a quest, the digital reward is automatically added to the user's account through the electronic payment service. In this way, the reward system allows digital addicts to obtain digital rewards through real-world activities, thereby restoring a balance between the digital world and reality.

[0030] The quest setting unit can analyze a user's past behavioral history and automatically generate quests optimized for each individual user. For example, the quest setting unit uses a generation AI to analyze a user's past behavioral history and automatically generate optimal quests based on the user's interests and concerns. For example, the quest setting unit suggests new quests that the user can enjoy based on data on events previously attended and places visited. The quest setting unit also analyzes the user's behavioral history and identifies specific patterns and trends to generate quests that are challenging yet achievable for the user. For example, the quest setting unit sets quests that include an appropriate amount of exercise based on past exercise data. The quest setting unit also uses a generation AI to analyze the user's behavioral history in real time and suggest quests that are optimal for the current situation. For example, the quest setting unit suggests nearby events and activities based on the user's current location and time of day. This allows the system to provide optimal quests based on the user's interests and concerns.

[0031] The quest setting unit can adjust the difficulty of the quest based on the user's real-time health data. The quest setting unit acquires health data, such as the user's step count and heart rate, in real time and adjusts the difficulty of the quest based on that data. For example, if the user is in good physical condition, the quest setting unit suggests a quest with a high level of difficulty. The quest setting unit also generates quests based on the user's physical strength and health condition based on the health data. For example, if the heart rate is high, the quest setting unit suggests a quest that includes light exercise, and if the heart rate is stable, the quest setting unit sets a more challenging quest. The quest setting unit also analyzes the user's health data and compares it with past data to provide quests of an appropriate level of difficulty. For example, the quest setting unit sets a quest that the user can easily complete based on their past exercise history. This makes it possible to provide quests appropriate to the user's health condition.

[0032] The quest setting unit can set a joint mission with the user's friends or family to promote social interaction. For example, the quest setting unit uses a generation AI to set a joint mission with the user's friends or family to promote social interaction. For example, the quest setting unit suggests events or activities that can be participated in together with friends as quests. The quest setting unit also analyzes the user's social network data to generate quests that can be enjoyed together with friends or family. For example, it sets local events or volunteer activities that the whole family can participate in. The quest setting unit also suggests quests that allow the user to deepen bonds with friends or family through joint missions. For example, it sets challenges that can be completed in cooperation with friends or outdoor activities that can be enjoyed as a family. This can promote social interaction among users.

[0033] The quest setting unit can incorporate learning elements related to local history or culture into the content of the quest to provide educational value. For example, the quest setting unit uses a generation AI to incorporate information about local history or culture into the quest and set a quest that the user can enjoy while learning. For example, the quest setting unit can suggest a quest to visit historical landmarks in the area. The quest setting unit can also add educational elements to the content of the quest to provide the user with an opportunity to learn about local culture and traditions. For example, the quest setting unit can suggest a quest to visit local museums and cultural facilities. The quest setting unit can also incorporate quizzes and missions related to local history or culture into the quest to allow the user to learn while having fun. For example, the quest setting unit can suggest a system where the user can proceed to the next quest by solving a quiz about local history. This can provide educational value to the user.

[0034] The reward providing unit can analyze a user's hobbies or interests and provide a customized digital reward based on the results. For example, the reward providing unit uses a generation AI to analyze a user's hobbies and interests and provide a customized digital reward based on the results. For example, the reward providing unit may provide limited edition digital art by a user's favorite artist as a reward. The reward providing unit also generates individually customized digital rewards based on the user's past behavioral data and interests. For example, the reward providing unit may provide digital content related to a theme that interests the user. The reward providing unit also uses a generation AI to analyze a user's hobbies and interests in real time and suggest optimal digital rewards based on the results. For example, the reward providing unit may provide digital items related to a field that the user has recently become interested in. This makes it possible to provide optimal digital rewards based on the user's hobbies and interests.

[0035] The reward providing unit can optimize the timing of reward provision based on the user's activity pattern. For example, the reward providing unit analyzes the user's activity pattern and builds a system that provides digital rewards at the optimal timing. For example, rewards are provided during the user's most active time period. The reward providing unit also adjusts the timing of reward provision based on the user's activity data using a generation AI. For example, rewards are provided immediately after the user completes a quest to increase motivation. The reward providing unit also monitors the user's activity pattern in real time and provides digital rewards at the optimal timing. For example, rewards are provided after the user completes their day's activities to increase a sense of accomplishment. This makes it possible to provide rewards at the optimal timing based on the user's activity pattern.

[0036] The reward providing unit can add a trade function to the digital reward, allowing users to exchange rewards with other users. The reward providing unit, for example, adds a trade function to the digital reward and builds a system in which users can exchange rewards with other users. For example, users can exchange digital items with each other, thereby revitalizing the community. The reward providing unit also forms a new community by users exchanging rewards through the trade function. For example, it provides a place where users who own specific digital items can interact with each other. The reward providing unit also promotes communication when users exchange rewards by using the trade function. For example, it adds a function to send messages during the exchange, deepening interaction between users. This allows users to exchange rewards with other users.

[0037] The reward providing unit can provide an option for a user to donate a portion of the reward to a charity of the user's choice. For example, the reward providing unit builds a system that provides an option for a user to donate a portion of the digital reward to a charity of the user's choice. For example, the reward providing unit allows the user to select a donation destination when receiving a reward. The reward providing unit also encourages social contribution by allowing the user to donate a portion of the reward to a charity. For example, the reward providing unit displays a donation history so that the user can check which organizations the user has donated to. The reward providing unit also provides the user with an opportunity to participate in social contribution activities through the option to donate a portion of the reward. For example, the reward providing unit displays a thank-you message from the organization to which the user has donated, allowing the user to realize the user's contribution. This allows the user to donate a portion of the reward to a charity.

[0038] The payment linking unit can provide a function that automatically saves a portion of a reward when a user receives it. The payment linking unit provides a function that automatically saves a portion of a reward when a user receives it, for example, through an electronic payment service. For example, 10% of the reward can be automatically transferred to a savings account. The payment linking unit also provides a function that allows a user to set a savings percentage when receiving a reward. For example, a user can set 20% of the reward to be saved. The payment linking unit also provides a mechanism through the savings function that automatically increases savings every time a user receives a reward. For example, a set amount can be set to be saved from each reward. This allows a user to automatically save a portion of their reward.

[0039] The payment linking unit can analyze the payment history and suggest quests based on the user's consumption patterns. The payment linking unit, for example, analyzes the payment history of an electronic payment service and suggests optimal quests based on the user's consumption patterns. For example, it sets quests related to stores and services that the user frequently uses. The payment linking unit also analyzes the user's consumption patterns and suggests quests that encourage specific consumption behaviors. For example, it sets quests to purchase healthy foods or to shop at local stores. The payment linking unit also understands the user's consumption trends based on the payment history and optimizes quests based on that data. For example, it suggests quests related to places that the user frequently visits. This makes it possible to provide optimal quests based on the user's consumption patterns.

[0040] The payment linking unit can provide a user with coupons that can be used at local stores when they receive a reward. The payment linking unit builds a system, for example, through an electronic payment service, that provides a user with coupons that can be used at local stores when they receive a reward. For example, a coupon that can be used at a local cafe is provided at the same time that the user receives a reward. The payment linking unit also automatically issues coupons that can be used at local stores when the user receives a reward. For example, when a user receives a reward, a discount coupon that can be used at a local restaurant is issued. The payment linking unit also provides a mechanism, through a coupon provision function, that allows a user to receive coupons that can be used at local stores every time they receive a reward. For example, when a user receives a reward, a coupon that can be used at a local bookstore is provided. This allows the user to receive coupons that can be used at local stores.

[0041] The payment linking unit can provide environmentally friendly options when a user receives a reward. The payment linking unit builds a system that provides environmentally friendly options when a user receives a reward, for example, through an electronic payment service. For example, a carbon offset option is provided at the same time as the user receives a reward. The payment linking unit also automatically suggests environmentally friendly options when a user receives a reward. For example, when a user receives a reward, an option to donate to tree planting activities is displayed. The payment linking unit also provides a mechanism that allows a user to participate in environmental protection activities every time they receive a reward by providing environmentally friendly options. For example, when a user receives a reward, an option to invest in renewable energy is provided. This allows the user to choose environmentally friendly options.

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

[0043] The quest setting unit can also analyze the user's past travel history and suggest travel-related quests. For example, it can suggest new travel destinations based on countries and cities the user has visited in the past. The quest setting unit can also set travel quests based on a specific theme based on the user's travel history. For example, it can suggest quests to visit historical sites or quests to enjoy natural scenery. The quest setting unit can also analyze the user's travel history and suggest quests to revisit places that were particularly memorable among those visited in the past. This makes it possible to provide the most suitable quests based on the user's travel history.

[0044] The quest setting unit can also suggest quests based on the user's hobbies and special skills. For example, if the user is good at cooking, the quest setting unit can suggest a quest to make a specific dish. The quest setting unit can also set quests that make use of the user's hobbies. For example, if a user enjoys gardening, the quest setting unit can suggest a quest to grow a specific plant. The quest setting unit can also set quests that make use of the user's special skills. For example, if a user is good at photography, the quest setting unit can suggest a quest to take photos based on a specific theme. This makes it possible to provide the user with the most suitable quest based on their hobbies and special skills.

[0045] The quest setting unit can also improve quests based on user feedback. For example, after a user completes a quest, the quest setting unit provides feedback and optimizes the next quest based on that feedback. The quest setting unit can also analyze user feedback in real time and adjust the content of the quest. For example, if the user feels that the difficulty of a quest is too high, the quest setting unit can adjust the difficulty of the next quest. The quest setting unit can also customize the content of the quest based on user feedback. For example, if a user likes a particular activity, the quest setting unit can suggest a quest that includes that activity. This makes it possible to provide the optimal quest based on user feedback.

[0046] The quest setting unit can also analyze the user's learning history and suggest educational quests. For example, it can suggest new learning quests based on what the user has learned in the past. The quest setting unit can also set quests related to specific fields based on the user's learning history. For example, it can suggest quests related to historical events to a user who is interested in history. The quest setting unit can also analyze the user's learning history and suggest quests to review what the user has learned in the past. This makes it possible to provide the most suitable quests based on the user's learning history.

[0047] The reward providing unit can also analyze a user's past purchase history and provide customized digital rewards based thereon. For example, it can provide digital rewards related to products the user has previously purchased. The reward providing unit can also generate digital rewards related to specific themes based on the user's purchase history. For example, it can provide digital content related to themes that interest the user. The reward providing unit can also analyze a user's purchase history in real time and suggest optimal digital rewards based on the results. This makes it possible to provide optimal digital rewards based on the user's purchase history.

[0048] The reward providing unit can also analyze the user's activity data and provide customized digital rewards based on the data. For example, when a user performs a specific activity, the reward providing unit can provide a digital reward related to that activity. The reward providing unit can also generate digital rewards related to a specific theme based on the user's activity data. For example, the reward providing unit can provide digital content related to a theme that interests the user. The reward providing unit can also analyze the user's activity data in real time and suggest optimal digital rewards based on the results. This makes it possible to provide optimal digital rewards based on the user's activity data.

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

[0050] Step 1: The quest setting unit uses the generation AI to set a real-world quest. For example, the generation AI may set a quest for the user, such as participating in a local event, visiting a specific location, or volunteering. The generation AI can also customize the quest based on the user's interests. Step 2: The reward provider provides a digital reward to the user who completes the quest set by the quest setting unit. For example, the reward provider provides a digital reward such as special digital art, an exclusive music track, or a rare game item. The reward provider analyzes the user's quest completion status and provides an appropriate reward. Step 3: The payment linking unit adds the digital reward provided by the reward providing unit to the user's account through an electronic payment service. For example, when the user completes a quest, the digital reward is automatically added to the user's account through the electronic payment service.

[0051] (Example 2) The reward system according to an embodiment of the present invention is a system that combines generation AI and electronic payment services. This system provides digitally addicted individuals with digital rewards through real-world activities. This allows digitally addicted individuals to obtain digital rewards through real-world activities, thereby restoring the balance between the digital world and reality.

[0052] A reward system according to an embodiment includes a quest setting unit, a reward providing unit, and a payment linking unit. The quest setting unit sets real-world quests using a generation AI. For example, the generation AI sets quests for a user, such as participating in a local event, visiting a specific location, or volunteering. The generation AI can also customize quests based on the user's interests. The reward providing unit provides digital rewards to users who complete quests set by the quest setting unit. For example, digital rewards may include special digital art, limited music tracks, or rare game items. The reward providing unit analyzes the user's progress in completing the quest and provides appropriate rewards. The payment linking unit adds the digital rewards provided by the reward providing unit to the user's account through an electronic payment service. For example, when a user completes a quest, the digital reward is automatically added to the user's account through the electronic payment service. In this way, the reward system allows digital addicts to obtain digital rewards through real-world activities, thereby restoring a balance between the digital world and reality.

[0053] The quest setting unit can analyze a user's past behavioral history and automatically generate quests optimized for each individual user. For example, the quest setting unit uses a generation AI to analyze a user's past behavioral history and automatically generate optimal quests based on the user's interests and concerns. For example, the quest setting unit suggests new quests that the user can enjoy based on data on events previously attended and places visited. The quest setting unit also analyzes the user's behavioral history and identifies specific patterns and trends to generate quests that are challenging yet achievable for the user. For example, the quest setting unit sets quests that include an appropriate amount of exercise based on past exercise data. The quest setting unit also uses a generation AI to analyze the user's behavioral history in real time and suggest quests that are optimal for the current situation. For example, the quest setting unit suggests nearby events and activities based on the user's current location and time of day. This allows the system to provide optimal quests based on the user's interests and concerns.

[0054] The quest setting unit can adjust the difficulty of the quest based on the user's real-time health data. The quest setting unit acquires health data, such as the user's step count and heart rate, in real time and adjusts the difficulty of the quest based on that data. For example, if the user is in good physical condition, the quest setting unit suggests a quest with a high level of difficulty. The quest setting unit also generates quests based on the user's physical strength and health condition based on the health data. For example, if the heart rate is high, the quest setting unit suggests a quest that includes light exercise, and if the heart rate is stable, the quest setting unit sets a more challenging quest. The quest setting unit also analyzes the user's health data and compares it with past data to provide quests of an appropriate level of difficulty. For example, the quest setting unit sets a quest that the user can easily complete based on their past exercise history. This makes it possible to provide quests appropriate to the user's health condition.

[0055] The quest setting unit can use the emotion estimation function to suggest quests that correspond to the user's current emotional state. For example, the quest setting unit uses the emotion estimation function to analyze the user's current emotional state and suggest quests that elicit positive emotions based on the results. For example, if the user is feeling stressed, the quest setting unit sets quests that include relaxing activities. The quest setting unit also monitors the user's emotional state in real time and adjusts quests according to changes in emotion. For example, if the user is feeling joy, the quest setting unit suggests quests that will further enhance that emotion. The quest setting unit also automatically generates quests that are likely to cause the user to feel positive emotions based on the emotion estimation data. For example, the quest setting unit sets quests that include activities that the user has experienced success in the past, thereby increasing the user's motivation. This makes it possible to provide appropriate quests that correspond to the user's emotional state.

[0056] The quest setting unit can set a joint mission with the user's friends or family to promote social interaction. For example, the quest setting unit uses a generation AI to set a joint mission with the user's friends or family to promote social interaction. For example, the quest setting unit suggests events or activities that can be participated in together with friends as quests. The quest setting unit also analyzes the user's social network data to generate quests that can be enjoyed together with friends or family. For example, it sets local events or volunteer activities that the whole family can participate in. The quest setting unit also suggests quests that allow the user to deepen bonds with friends or family through joint missions. For example, it sets challenges that can be completed in cooperation with friends or outdoor activities that can be enjoyed as a family. This can promote social interaction among users.

[0057] The quest setting unit can incorporate learning elements related to local history or culture into the content of the quest to provide educational value. For example, the quest setting unit uses a generation AI to incorporate information about local history or culture into the quest and set a quest that the user can enjoy while learning. For example, the quest setting unit can suggest a quest to visit historical landmarks in the area. The quest setting unit can also add educational elements to the content of the quest to provide the user with an opportunity to learn about local culture and traditions. For example, the quest setting unit can suggest a quest to visit local museums and cultural facilities. The quest setting unit can also incorporate quizzes and missions related to local history or culture into the quest to allow the user to learn while having fun. For example, the quest setting unit can suggest a system where the user can proceed to the next quest by solving a quiz about local history. This can provide educational value to the user.

[0058] The quest setting unit can use the emotion estimation function to record the emotions felt when the user completes a quest and reflect the emotions in the next quest setting. For example, the quest setting unit can use the emotion estimation function to record the emotions felt when the user completes a quest and reflect the data in the next quest setting. For example, elements of a quest that elicited positive emotions can be incorporated into the next quest. The quest setting unit can also analyze the user's emotion data and optimize the next quest based on the emotional responses obtained in past quests. For example, the quest setting unit can re-suggest a quest that includes an activity that the user enjoyed. The quest setting unit can also utilize the emotion estimation data to analyze the emotions felt when the user completes a quest and reflect the emotions in the next quest. For example, elements of a quest that gave the user a sense of accomplishment can be incorporated into the next quest. This allows the user's emotion data to be reflected in the next quest setting.

[0059] The reward providing unit can analyze a user's hobbies or interests and provide a customized digital reward based on the results. For example, the reward providing unit uses a generation AI to analyze a user's hobbies and interests and provide a customized digital reward based on the results. For example, the reward providing unit may provide limited edition digital art by a user's favorite artist as a reward. The reward providing unit also generates individually customized digital rewards based on the user's past behavioral data and interests. For example, the reward providing unit may provide digital content related to a theme that interests the user. The reward providing unit also uses a generation AI to analyze a user's hobbies and interests in real time and suggest optimal digital rewards based on the results. For example, the reward providing unit may provide digital items related to a field that the user has recently become interested in. This makes it possible to provide optimal digital rewards based on the user's hobbies and interests.

[0060] The reward providing unit can optimize the timing of reward provision based on the user's activity pattern. For example, the reward providing unit analyzes the user's activity pattern and builds a system that provides digital rewards at the optimal timing. For example, rewards are provided during the user's most active time period. The reward providing unit also adjusts the timing of reward provision based on the user's activity data using a generation AI. For example, rewards are provided immediately after the user completes a quest to increase motivation. The reward providing unit also monitors the user's activity pattern in real time and provides digital rewards at the optimal timing. For example, rewards are provided after the user completes their day's activities to increase a sense of accomplishment. This makes it possible to provide rewards at the optimal timing based on the user's activity pattern.

[0061] The reward providing unit can add a trade function to the digital reward, allowing users to exchange rewards with other users. The reward providing unit, for example, adds a trade function to the digital reward and builds a system in which users can exchange rewards with other users. For example, users can exchange digital items with each other, thereby revitalizing the community. The reward providing unit also forms a new community by users exchanging rewards through the trade function. For example, it provides a place where users who own specific digital items can interact with each other. The reward providing unit also promotes communication when users exchange rewards by using the trade function. For example, it adds a function to send messages during the exchange, deepening interaction between users. This allows users to exchange rewards with other users.

[0062] The reward providing unit can provide an option for a user to donate a portion of the reward to a charity of the user's choice. For example, the reward providing unit builds a system that provides an option for a user to donate a portion of the digital reward to a charity of the user's choice. For example, the reward providing unit allows the user to select a donation destination when receiving a reward. The reward providing unit also encourages social contribution by allowing the user to donate a portion of the reward to a charity. For example, the reward providing unit displays a donation history so that the user can check which organizations the user has donated to. The reward providing unit also provides the user with an opportunity to participate in social contribution activities through the option to donate a portion of the reward. For example, the reward providing unit displays a thank-you message from the organization to which the user has donated, allowing the user to realize the user's contribution. This allows the user to donate a portion of the reward to a charity.

[0063] The reward providing unit can use the emotion estimation function to record the emotion a user feels when receiving a reward and reflect that in the next reward design. For example, the reward providing unit can use the emotion estimation function to analyze the emotion a user feels when receiving a reward and reflect that data in the next reward design. For example, elements of a reward that the user found particularly enjoyable can be incorporated into the next reward. The reward providing unit can also analyze the user's emotion data and optimize the next reward based on the user's emotional responses to past rewards. For example, the type and format of reward that the user found most enjoyable can be reflected in the next reward. The reward providing unit can also utilize the emotion estimation data to record the emotion a user feels when receiving a reward and reflect that in the next reward design. For example, features of a reward that the user found particularly satisfying can be incorporated into the next reward. This allows the user's emotion data to be reflected in the next reward design.

[0064] The payment linking unit can provide a function that automatically saves a portion of a reward when a user receives it. The payment linking unit provides a function that automatically saves a portion of a reward when a user receives it, for example, through an electronic payment service. For example, 10% of the reward can be automatically transferred to a savings account. The payment linking unit also provides a function that allows a user to set a savings percentage when receiving a reward. For example, a user can set 20% of the reward to be saved. The payment linking unit also provides a mechanism through the savings function that automatically increases savings every time a user receives a reward. For example, a set amount can be set to be saved from each reward. This allows a user to automatically save a portion of their reward.

[0065] The payment linking unit can analyze the payment history and suggest quests based on the user's consumption patterns. The payment linking unit, for example, analyzes the payment history of an electronic payment service and suggests optimal quests based on the user's consumption patterns. For example, it sets quests related to stores and services that the user frequently uses. The payment linking unit also analyzes the user's consumption patterns and suggests quests that encourage specific consumption behaviors. For example, it sets quests to purchase healthy foods or to shop at local stores. The payment linking unit also understands the user's consumption trends based on the payment history and optimizes quests based on that data. For example, it suggests quests related to places that the user frequently visits. This makes it possible to provide optimal quests based on the user's consumption patterns.

[0066] The payment linking unit can use the emotion estimation function to analyze the emotions the user feels when receiving a reward and optimize the next reward receiving experience. For example, the payment linking unit can use the emotion estimation function to analyze the emotions the user feels when receiving a reward and reflect the data in the next reward receiving experience. For example, the payment linking unit can use the reward receiving method that the user found particularly enjoyable again next time. The payment linking unit can also analyze the user's emotion data and optimize the next experience based on the user's emotional responses to past reward receiving experiences. For example, the payment linking unit can provide the user with the most satisfying receiving method again next time. The payment linking unit can also use the emotion estimation data to record the emotions the user feels when receiving a reward and reflect them in the next experience. For example, the payment linking unit can incorporate features of the user's particularly satisfying receiving experience into the next experience. This allows the user's emotion data to be reflected in the next reward receiving experience.

[0067] The payment linking unit can provide a user with coupons that can be used at local stores when they receive a reward. The payment linking unit builds a system, for example, through an electronic payment service, that provides a user with coupons that can be used at local stores when they receive a reward. For example, a coupon that can be used at a local cafe is provided at the same time that the user receives a reward. The payment linking unit also automatically issues coupons that can be used at local stores when the user receives a reward. For example, when a user receives a reward, a discount coupon that can be used at a local restaurant is issued. The payment linking unit also provides a mechanism, through a coupon provision function, that allows a user to receive coupons that can be used at local stores every time they receive a reward. For example, when a user receives a reward, a coupon that can be used at a local bookstore is provided. This allows the user to receive coupons that can be used at local stores.

[0068] The payment linking unit can provide environmentally friendly options when a user receives a reward. The payment linking unit builds a system that provides environmentally friendly options when a user receives a reward, for example, through an electronic payment service. For example, a carbon offset option is provided at the same time as the user receives a reward. The payment linking unit also automatically suggests environmentally friendly options when a user receives a reward. For example, when a user receives a reward, an option to donate to tree planting activities is displayed. The payment linking unit also provides a mechanism that allows a user to participate in environmental protection activities every time they receive a reward by providing environmentally friendly options. For example, when a user receives a reward, an option to invest in renewable energy is provided. This allows the user to choose environmentally friendly options.

[0069] The payment linking unit can use the emotion estimation function to record the emotions of the user when receiving a reward and reflect them in the next reward design. The payment linking unit, for example, uses the emotion estimation function to record the emotions of the user when receiving a reward and reflects the data in the next reward design. For example, elements of the reward that the user found particularly enjoyable can be incorporated into the next reward. The payment linking unit also analyzes the user's emotion data and optimizes the next reward based on the user's emotional responses to past rewards. For example, the type and format of reward that the user found most enjoyable can be reflected in the next reward. The payment linking unit also utilizes the emotion estimation data to record the emotions of the user when receiving a reward and reflect them in the next reward design. For example, features of the reward that the user found particularly satisfying can be incorporated into the next reward. This allows the user's emotion data to be reflected in the next reward design.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The quest setting unit can also analyze the user's past travel history and suggest travel-related quests. For example, it can suggest new travel destinations based on countries and cities the user has visited in the past. The quest setting unit can also set travel quests based on a specific theme based on the user's travel history. For example, it can suggest quests to visit historical sites or quests to enjoy natural scenery. The quest setting unit can also analyze the user's travel history and suggest quests to revisit places that were particularly memorable among those visited in the past. This makes it possible to provide the most suitable quests based on the user's travel history.

[0072] The quest setting unit can also suggest quests based on the user's hobbies and special skills. For example, if the user is good at cooking, the quest setting unit can suggest a quest to make a specific dish. The quest setting unit can also set quests that make use of the user's hobbies. For example, if a user enjoys gardening, the quest setting unit can suggest a quest to grow a specific plant. The quest setting unit can also set quests that make use of the user's special skills. For example, if a user is good at photography, the quest setting unit can suggest a quest to take photos based on a specific theme. This makes it possible to provide the user with the most suitable quest based on their hobbies and special skills.

[0073] The quest setting unit can also improve quests based on user feedback. For example, after a user completes a quest, the quest setting unit provides feedback and optimizes the next quest based on that feedback. The quest setting unit can also analyze user feedback in real time and adjust the content of the quest. For example, if the user feels that the difficulty of a quest is too high, the quest setting unit can adjust the difficulty of the next quest. The quest setting unit can also customize the content of the quest based on user feedback. For example, if a user likes a particular activity, the quest setting unit can suggest a quest that includes that activity. This makes it possible to provide the optimal quest based on user feedback.

[0074] The quest setting unit can also use the emotion estimation function to provide rewards according to the user's emotional state. For example, if the user is feeling stressed, it can provide a digital reward that helps them relax. The quest setting unit can also monitor the user's emotional state in real time and adjust rewards according to changes in emotion. For example, if the user is feeling joy, it can provide a digital reward that further enhances that emotion. The quest setting unit can also automatically generate rewards that are likely to cause the user to feel positive emotions based on the emotion estimation data. For example, it can provide a digital reward that is associated with a past successful experience, thereby increasing the user's motivation. This makes it possible to provide appropriate rewards according to the user's emotional state.

[0075] The quest setting unit can also analyze the user's learning history and suggest educational quests. For example, it can suggest new learning quests based on what the user has learned in the past. The quest setting unit can also set quests related to specific fields based on the user's learning history. For example, it can suggest quests related to historical events to a user who is interested in history. The quest setting unit can also analyze the user's learning history and suggest quests to review what the user has learned in the past. This makes it possible to provide the most suitable quests based on the user's learning history.

[0076] The quest setting unit can also use the emotion estimation function to provide feedback according to the user's emotional state. For example, if the user is feeling stressed, it can provide an encouraging message. The quest setting unit can also monitor the user's emotional state in real time and adjust the feedback according to changes in emotion. For example, if the user is feeling joy, it can provide feedback that further enhances that emotion. The quest setting unit can also automatically generate feedback that makes the user more likely to feel positive emotions based on the emotion estimation data. For example, it can provide feedback that shows the user has had a successful experience in the past, thereby increasing the user's motivation. This makes it possible to provide appropriate feedback according to the user's emotional state.

[0077] The reward providing unit can also analyze a user's past purchase history and provide customized digital rewards based thereon. For example, it can provide digital rewards related to products the user has previously purchased. The reward providing unit can also generate digital rewards related to specific themes based on the user's purchase history. For example, it can provide digital content related to themes that interest the user. The reward providing unit can also analyze a user's purchase history in real time and suggest optimal digital rewards based on the results. This makes it possible to provide optimal digital rewards based on the user's purchase history.

[0078] The reward providing unit can also use the emotion estimation function to record the emotions felt by the user when they receive a reward and reflect these in the next reward design. For example, elements of the reward that the user found particularly enjoyable can be incorporated into the next reward. The reward providing unit can also analyze the user's emotion data and optimize the next reward based on the user's emotional responses to past rewards. For example, the type and format of reward that the user found most enjoyable can be reflected in the next reward. The reward providing unit can also utilize the emotion estimation data to record the emotions felt by the user when they receive a reward and reflect these in the next reward design. This allows the user's emotion data to be reflected in the next reward design.

[0079] The reward providing unit can also analyze the user's activity data and provide customized digital rewards based on the data. For example, when a user performs a specific activity, the reward providing unit can provide a digital reward related to that activity. The reward providing unit can also generate digital rewards related to a specific theme based on the user's activity data. For example, the reward providing unit can provide digital content related to a theme that interests the user. The reward providing unit can also analyze the user's activity data in real time and suggest optimal digital rewards based on the results. This makes it possible to provide optimal digital rewards based on the user's activity data.

[0080] The reward providing unit can use the emotion estimation function to record the emotions felt by the user when they receive a reward and reflect that data in the next reward design. For example, elements of the reward that the user found particularly enjoyable can be incorporated into the next reward. The reward providing unit can also analyze the user's emotion data and optimize the next reward based on the user's emotional responses to past rewards. For example, the type and format of reward that the user found most enjoyable can be reflected in the next reward. The reward providing unit can also utilize the emotion estimation data to record the emotions felt by the user when they receive a reward and reflect that data in the next reward design. This allows the user's emotion data to be reflected in the next reward design.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The quest setting unit uses the generation AI to set a real-world quest. For example, the generation AI may set a quest for the user, such as participating in a local event, visiting a specific location, or volunteering. The generation AI can also customize the quest based on the user's interests. Step 2: The reward provider provides a digital reward to the user who completes the quest set by the quest setting unit. For example, the reward provider provides a digital reward such as special digital art, an exclusive music track, or a rare game item. The reward provider analyzes the user's quest completion status and provides an appropriate reward. Step 3: The payment linking unit adds the digital reward provided by the reward providing unit to the user's account through an electronic payment service. For example, when the user completes a quest, the digital reward is automatically added to the user's account through the electronic payment service.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a quest setting unit that sets a real-world quest using a generation AI; a reward providing unit that provides a digital reward to a user who completes the quest set by the quest setting unit; a payment linking unit that adds the digital reward provided by the reward providing unit to the user's account through an electronic payment service. A system characterized by:

2. The quest setting unit Analyzing the user's past behavior history and automatically generating the quest optimized for each individual user.

2. The system of claim 1.

3. The reward providing unit: Analyzing the user's hobbies or interests and providing the customized digital rewards based thereon 2. The system of claim 1.

4. The payment linking unit Providing the user with the ability to automatically save a portion of the reward when the user receives the reward 2. The system of claim 1.

5. The quest setting unit Suggesting the quest according to the user's current emotional state 2. The system of claim 1.

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