system

Abstract

The system according to this embodiment aims to enhance the status of in-game characters based on health data and promote a healthy lifestyle. [Solution] The system according to the embodiment comprises an acquisition unit, an enhancement unit, and a provision unit. The acquisition unit acquires health data. The enhancement unit enhances the character's status based on the data acquired by the acquisition unit. The provision unit provides specific abilities or items upon clearing specific health goals.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the utilization of health data in games has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to enhance the status of in-game characters based on health data and promote a healthy lifestyle.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an enhancement unit, and a provision unit. The acquisition unit acquires health data. The enhancement unit enhances the status of the character based on the data acquired by the acquisition unit. The provision unit provides specific abilities or items by clearing specific health goals. [Effects of the Invention]

[0007] The system according to this embodiment can enhance the stats of in-game characters based on health data and promote a healthy lifestyle. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The next-generation game platform according to an embodiment of the present invention is a system that enhances the status and skills of in-game characters in real time based on health data acquired from a smart device. This system allows users to acquire special abilities and items in the game each time they achieve exercise and health goals in the real world. This provides a new gaming experience that allows users to adopt a healthy lifestyle while having fun. For example, a user wears a smart device and sets daily exercise and health goals. For instance, they might set goals such as walking 10,000 steps a day or running for 30 minutes. These goals are tracked in real time based on data acquired from the smart device. The acquired health data is then reflected in the status and skills of the in-game character. For example, if a user walks 10,000 steps, the in-game character's stamina and speed will improve. Furthermore, by achieving specific health goals, users can acquire special abilities and items in the game. For example, running for 30 minutes can result in acquiring special weapons and armor in the game. In addition, the game incorporates educational content and interactive tutorials, allowing users to learn basic health management knowledge. For example, health advice and training guides are provided within the game, making them intuitively understandable to the user. Furthermore, regular health checkpoints are provided, and feedback is given according to progress. In this way, the present invention targets gamers and fitness enthusiasts and offers a new gaming experience that addresses issues such as lack of exercise, lack of health management awareness, and balancing gaming and life. Users can overcome a sedentary lifestyle and adopt a healthy lifestyle by incorporating daily exercise as part of gameplay. In addition, by linking daily achievements in the game with real-world activities, users can optimize their life balance. As a result, the next-generation gaming platform allows users to adopt a healthy lifestyle while having fun.

[0029] The next-generation game platform according to this embodiment comprises an acquisition unit, an enhancement unit, and a provision unit. The acquisition unit acquires health data. The acquisition unit can acquire data such as heart rate, steps taken, and calorie consumption from a smart device, for example. The acquisition unit can also acquire data from a smart device in real time. Furthermore, the acquisition unit can acquire data from a smart device to track the user's exercise and health goals. For example, the acquisition unit acquires heart rate data from a smartwatch to monitor the user's exercise intensity. The acquisition unit acquires step count data from a fitness tracker to measure the user's walking distance. The acquisition unit acquires calorie consumption data from a smartphone to record the user's energy consumption. The enhancement unit enhances the character's status based on the data acquired by the acquisition unit. For example, if the user walks 10,000 steps, the enhancement unit can improve the in-game character's stamina and speed. The enhancement unit can also increase the character's skill level if the user achieves a specific health goal. Furthermore, the enhancement unit can enhance the character's status in real time based on the acquired data. For example, the Enhancement Department can improve a character's endurance if the user runs for 30 minutes. The Enhancement Department can strengthen a character's attack power if the user exercises three times a week. The Enhancement Department can improve a character's defense power if the user meets their daily calorie expenditure goal. The Provision Department can provide special abilities and items for achieving specific health goals. For example, the Provision Department can provide special weapons and armor in-game if the user runs for 30 minutes. It can also provide special skills in-game if the user walks 10,000 steps. Furthermore, the Provision Department can also provide special items for achieving specific health goals. For example, the Provision Department can provide special equipment in-game if the user exercises three times a week. The Provision Department can provide special currency in-game if the user meets their daily calorie expenditure goal. The Provision Department can provide a special title in-game if the user meets their monthly health goal.This allows the next-generation game platform according to the embodiment to enable users to adopt a healthy lifestyle while having fun.

[0030] The data acquisition unit acquires health data. For example, the data acquisition unit can acquire data such as heart rate, steps taken, and calorie consumption from smart devices. Specifically, it monitors the user's heart rate in real time and records fluctuations in heart rate during exercise through wearable devices such as smartwatches and fitness trackers. This allows the system to understand the user's exercise intensity and cardiopulmonary function. In addition, fitness trackers measure the user's steps taken and distance traveled, recording daily activity levels in detail. This allows the system to accurately understand how far the user has walked and how many calories they have burned. Furthermore, it is possible to record the user's meals and calorie intake through smartphone applications, enabling overall energy balance management. The data acquisition unit centrally collects this data and sends it to a cloud server, monitoring the user's health status in real time and providing the data to the analysis unit and enhancement unit as needed. In this way, the data acquisition unit can efficiently collect user health data and support user health management in conjunction with other functions of the next-generation game platform.

[0031] The Enhancement Department strengthens character stats based on data acquired by the Acquisition Department. Specifically, if a user walks 10,000 steps, the in-game character's stamina and speed can be improved. For example, if a user achieves 10,000 steps in a day, the character's stamina increases by 10 points and speed increases by 5%. Furthermore, if a user achieves specific health goals, the character's skill level can be increased. For example, if a user exercises three times a week, the character's attack power increases by 15 points and a new skill is unlocked. In addition, the Enhancement Department can strengthen character stats in real time based on acquired data. For example, if a user goes for a 30-minute run, the character's endurance increases by 20 points, and the chance of acquiring specific items during the run increases. The Enhancement Department can also increase a character's defense by 10 points if a user achieves their daily calorie expenditure goal. This allows the Enhancement Department to leverage user health data to strengthen in-game characters and motivate users to maintain a healthy lifestyle. Moreover, the Enhancement Department can analyze user health data and propose optimal enhancement plans for individual users. This allows users to perform effective training tailored to their own health condition and efficiently strengthen their in-game characters.

[0032] The service provider offers special abilities and items for achieving specific health goals. Specifically, if a user runs for 30 minutes, they can be given special weapons and armor in the game. For example, if a user achieves a 30-minute run, they will receive a powerful sword and armor in the game. The service provider can also offer special skills in the game if a user walks 10,000 steps. For example, if a user achieves 10,000 steps, their character will learn a new magic skill, giving them an advantage in combat. Furthermore, the service provider can offer special items for achieving specific health goals. For example, if a user exercises three times a week, they will receive special equipment in the game. This allows users to enjoy progressing in the game while maintaining a healthy lifestyle. The service provider also offers special in-game currency if a user achieves their daily calorie expenditure goal. For example, if a user achieves their daily calorie expenditure goal, they will receive special in-game currency that can be used to purchase items or strengthen their character. The service provider also offers special titles in the game if a user achieves their monthly health goals. For example, when a user achieves their monthly health goals, they are awarded a special title within the game, allowing them to showcase their achievements to other players. This allows the service provider to support users in adopting a healthy lifestyle while having fun. Furthermore, the service provider can analyze users' health data and propose reward plans tailored to each individual user. This enables users to engage in effective training suited to their health condition and enjoy progressing through the game.

[0033] The system includes an Education Department that provides educational content. The Education Department provides educational content. For example, the Education Department may provide videos on health management. The Education Department may also provide articles on health management. Furthermore, the Education Department may also provide quizzes on health management. For example, the Education Department may provide videos for users to learn basic knowledge of health management. The Education Department may provide articles for users to understand the importance of health management. The Education Department may provide quizzes for users to check their knowledge of health management. In this way, the Education Department enables users to learn basic knowledge of health management. Some or all of the above processes in the Education Department may be performed using AI, for example, or not using AI. For example, the Education Department may input the user's learning history into AI and select the most suitable educational content.

[0034] The system includes a tutorial section that provides interactive tutorials. The tutorial section provides interactive tutorials. For example, the tutorial section can provide simulations that users can learn by operating. The tutorial section can also provide interactive guides. Furthermore, the tutorial section can provide interactive content that users can understand intuitively. For example, the tutorial section provides simulations for users to learn basic knowledge of health management. The tutorial section provides interactive guides for users to understand the importance of health management. The tutorial section provides interactive quizzes for users to check their knowledge of health management. In this way, the tutorial section enables users to intuitively understand knowledge of health management. Some or all of the above processes in the tutorial section may be performed using AI, for example, or without AI. For example, the tutorial section can input the user's operation history into AI and select the most suitable tutorial.

[0035] The system includes a check unit that sets up regular health checkpoints. The check unit can set up regular health checkpoints. For example, the check unit can perform weekly weight measurements. The check unit can also perform monthly health checkups. Furthermore, the check unit can set up checkpoints to periodically evaluate the user's health status. For example, the check unit can set up checkpoints for the user to perform weekly weight measurements. The check unit can set up checkpoints for the user to perform monthly health checkups. The check unit can set up checkpoints for the user to periodically evaluate their health status. This allows the check unit to provide the user with feedback according to their progress. Some or all of the above-described processes in the check unit may be performed using AI, for example, or without AI. For example, the check unit can input the user's health data into AI and select the optimal checkpoints.

[0036] The data acquisition unit can acquire health data from smart devices. For example, the data acquisition unit can acquire heart rate data from a smartphone. It can also acquire step count data from a smartwatch. Furthermore, it can acquire calorie consumption data from a fitness tracker. For example, the data acquisition unit acquires heart rate data from a smartphone to monitor the user's exercise intensity. The data acquisition unit acquires step count data from a smartwatch to measure the user's walking distance. The data acquisition unit acquires calorie consumption data from a fitness tracker to record the user's energy consumption. This allows for real-time tracking of the user's exercise and health goals by acquiring health data from smart devices. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input data acquired from smart devices into an AI and have the AI ​​perform data analysis.

[0037] The enhancement unit can enhance a character's stats in real time based on acquired health data. For example, if a user walks 10,000 steps, the enhancement unit will improve the character's stamina and speed in the game. The enhancement unit can also increase a character's skill level if the user achieves specific health goals. Furthermore, the enhancement unit can enhance a character's stats in real time based on acquired data. For example, if a user runs for 30 minutes, the enhancement unit will improve the character's endurance. If a user exercises three times a week, the enhancement unit will improve the character's attack power. If a user achieves their daily calorie consumption goal, the enhancement unit will improve the character's defense power. In this way, by enhancing the character's stats in real time based on acquired health data, the user's exercise and achievement of health goals are reflected in the game. Some or all of the above processes in the enhancement unit may be performed using AI, for example, or not. For example, the enhancement unit can input acquired health data into an AI and have the AI ​​perform the character stat enhancements.

[0038] The data acquisition unit can analyze the user's past health data and select the optimal acquisition method. For example, the acquisition unit can analyze the user's past exercise patterns and acquire health data at the most effective time. The acquisition unit can also select a method for acquiring data after a specific exercise based on the user's past health data. Furthermore, the acquisition unit can adjust the frequency of data acquisition based on the user's past health data. For example, the acquisition unit can analyze the user's past exercise patterns and acquire heart rate data at the most effective time. The acquisition unit can select a method for acquiring calorie consumption data after a specific exercise based on the user's past health data. The acquisition unit adjusts the frequency of data acquisition based on the user's past health data. In this way, the optimal acquisition method can be selected by analyzing the user's past health data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past health data into AI and have the AI ​​select the optimal acquisition method.

[0039] The data acquisition unit can filter health data based on the user's current lifestyle and exercise habits. For example, if the user is busy, the acquisition unit prioritizes data that can be acquired in a short time. It can also prioritize acquiring post-exercise data if the user exercises regularly. Furthermore, the acquisition unit can adjust the timing of data acquisition to match the user's lifestyle. For example, if the user is busy, the acquisition unit prioritizes heart rate data that can be acquired in a short time. If the user exercises regularly, the acquisition unit prioritizes acquiring post-exercise calorie consumption data. The acquisition unit adjusts the timing of data acquisition to match the user's lifestyle. This allows for the acquisition of more relevant data by filtering the data based on the user's lifestyle and exercise habits. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's lifestyle and exercise habit data into AI and have the AI ​​perform the data filtering.

[0040] The data acquisition unit can prioritize acquiring highly relevant data based on the user's geographical location when acquiring health data. For example, if the user is in a park, the data acquisition unit will prioritize acquiring data on steps and exercise distance. Similarly, if the user is in a gym, the data acquisition unit can prioritize acquiring data on exercise intensity and calorie consumption. Furthermore, if the user is at home, the data acquisition unit can prioritize acquiring data on relaxation state and sleep. This allows for the acquisition of more appropriate data by prioritizing the acquisition of highly relevant data based on the user's geographical location. Some or all of the above processing in the data acquisition unit may be performed using AI, or without AI. For example, the data acquisition unit can input the user's geographical location into AI and have AI select highly relevant data.

[0041] The data acquisition unit can analyze a user's social media activity and acquire relevant data when acquiring health data. For example, if a user posts about exercise on social media, the data acquisition unit will prioritize acquiring that exercise data. The data acquisition unit can also prioritize acquiring stress level data if a user posts about stress. Furthermore, if a user posts about health, the data acquisition unit can prioritize acquiring relevant health data. For example, if a user posts about exercise on social media, the data acquisition unit will prioritize acquiring that exercise data. If a user posts about stress, the data acquisition unit will prioritize acquiring stress level data. If a user posts about health, the data acquisition unit will prioritize acquiring relevant health data. In this way, relevant health data can be acquired by analyzing a user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity data into AI and have AI select relevant health data.

[0042] The enhancement unit can adjust the level of detail in status enhancement based on the importance of health data during enhancement. For example, the enhancement unit can increase the level of detail in status enhancement based on important health data. It can also set the level of detail in status enhancement to a moderate level based on general health data. Furthermore, it can also decrease the level of detail in status enhancement based on supplementary health data. For example, the enhancement unit can increase the level of detail in status enhancement based on important health data. It can set the level of detail in status enhancement to a moderate level based on general health data. It can decrease the level of detail in status enhancement based on supplementary health data. This allows for more effective enhancement by adjusting the level of detail in status enhancement based on the importance of health data. Some or all of the above processing in the enhancement unit may be performed using AI, for example, or without AI. For example, the enhancement unit can input the importance of health data into the AI ​​and have the AI ​​adjust the level of detail in status enhancement.

[0043] The enhancement unit can apply different enhancement algorithms depending on the category of health data during enhancement. For example, the enhancement unit can apply an algorithm to enhance physical strength and speed based on exercise data. It can also apply an algorithm to enhance concentration and recovery based on sleep data. Furthermore, it can apply an algorithm to enhance mental strength and endurance based on stress data. For example, the enhancement unit can apply an algorithm to enhance physical strength and speed based on exercise data. The enhancement unit can apply an algorithm to enhance concentration and recovery based on sleep data. The enhancement unit can apply an algorithm to enhance mental strength and endurance based on stress data. This allows for more appropriate enhancement by applying different enhancement algorithms depending on the category of health data. Some or all of the above processing in the enhancement unit may be performed using AI, for example, or without AI. For example, the enhancement unit can input the categories of health data into the AI ​​and have the AI ​​execute the application of the enhancement algorithms.

[0044] The enhancement unit can determine the priority of status enhancements based on the timing of health data acquisition during enhancement. For example, the enhancement unit can determine the priority of status enhancements based on recently acquired health data. The enhancement unit can also adjust the priority of status enhancements based on past health data. Furthermore, the enhancement unit can determine the priority of status enhancements based on health data acquired during a specific period. For example, the enhancement unit can determine the priority of status enhancements based on recently acquired health data. The enhancement unit can adjust the priority of status enhancements based on past health data. The enhancement unit can determine the priority of status enhancements based on health data acquired during a specific period. This allows for enhancements to be performed at a more appropriate time by determining the priority of status enhancements based on the timing of health data acquisition. Some or all of the above processing in the enhancement unit may be performed using AI, for example, or without AI. For example, the enhancement unit can input the timing of health data acquisition into the AI ​​and have the AI ​​determine the priority of status enhancements.

[0045] The enhancement unit can adjust the order of status enhancements based on the relevance of health data during enhancement. For example, the enhancement unit determines the order of status enhancements based on directly related health data. The enhancement unit can also adjust the order of status enhancements based on indirectly related health data. Furthermore, the enhancement unit can also determine the order of status enhancements based on supplementary health data. For example, the enhancement unit determines the order of status enhancements based on directly related health data. The enhancement unit adjusts the order of status enhancements based on indirectly related health data. The enhancement unit determines the order of status enhancements based on supplementary health data. This allows for more effective enhancement by adjusting the order of status enhancements based on the relevance of health data. Some or all of the above processing in the enhancement unit may be performed using AI, for example, or without AI. For example, the enhancement unit can input the relevance of health data into AI and have AI adjust the order of status enhancements.

[0046] The service provider can adjust the level of detail of the abilities and items provided based on the degree of achievement of health goals. For example, if a high health goal is achieved, the service provider can provide detailed abilities and items. If a moderate health goal is achieved, the service provider can also provide general abilities and items. Furthermore, if a low health goal is achieved, the service provider can provide simple abilities and items. For example, if a high health goal is achieved, the service provider can provide detailed abilities and items. If a moderate health goal is achieved, the service provider can provide general abilities and items. If a low health goal is achieved, the service provider can provide simple abilities and items. This allows for more effective service provision by adjusting the level of detail of the abilities and items provided based on the degree of achievement of health goals. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the degree of achievement of health goals into AI and have AI adjust the level of detail of the abilities and items provided.

[0047] The service provider can apply different service provision algorithms depending on the health goal category at the time of provision. For example, if the exercise goal is achieved, the service provider can provide abilities or items to improve physical strength and speed. Similarly, if the sleep goal is achieved, the service provider can provide abilities or items to improve concentration and recovery. Furthermore, if the stress management goal is achieved, the service provider can provide abilities or items to improve mental strength and endurance. This allows for more appropriate provision by applying different service provision algorithms depending on the health goal category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the health goal category into the AI ​​and have the AI ​​apply the service provision algorithm.

[0048] The service provider can determine the priority of the abilities and items to be provided based on the timing of achieving health goals. For example, the service provider can determine the priority of abilities and items based on recently achieved health goals. The service provider can also adjust the priority of abilities and items based on health goals achieved in the past. Furthermore, the service provider can determine the priority of abilities and items based on health goals achieved within a specific period. For example, the service provider can determine the priority of abilities and items based on recently achieved health goals. The service provider can adjust the priority of abilities and items based on health goals achieved in the past. The service provider can determine the priority of abilities and items based on health goals achieved within a specific period. By determining the priority of abilities and items to be provided based on the timing of achieving health goals, services can be provided at a more appropriate time. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the timing of achieving health goals into the AI ​​and have the AI ​​determine the priority of the abilities and items to be provided.

[0049] The delivery unit can adjust the order of abilities and items provided based on the relevance of health goals at the time of delivery. For example, the delivery unit can determine the order of abilities and items based on directly relevant health goals. It can also adjust the order of abilities and items based on indirectly relevant health goals. Furthermore, it can determine the order of abilities and items based on supplementary health goals. For example, the delivery unit can determine the order of abilities and items based on directly relevant health goals. The delivery unit can adjust the order of abilities and items based on indirectly relevant health goals. The delivery unit can determine the order of abilities and items based on supplementary health goals. This allows for more effective delivery by adjusting the order of abilities and items provided based on the relevance of health goals. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance of health goals into AI and have AI adjust the order of abilities and items provided.

[0050] The Ministry of Education can select the most suitable educational content by referring to the user's past learning history when providing educational content. For example, the Ministry of Education can provide relevant educational content based on what the user has learned in the past. The Ministry of Education can also prioritize providing content that the user has a high level of understanding of based on their past learning history. Furthermore, the Ministry of Education can analyze the user's past learning history and select the most effective educational content. For example, the Ministry of Education can provide relevant educational content based on what the user has learned in the past. The Ministry of Education can prioritize providing content that the user has a high level of understanding of based on their past learning history. The Ministry of Education can analyze the user's past learning history and select the most effective educational content. In this way, the Ministry of Education can provide the most suitable educational content by referring to the user's past learning history. Some or all of the above processes by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input the user's past learning history into AI and have the AI ​​select the most suitable educational content.

[0051] The Ministry of Education can provide optimal educational content based on the user's device information. For example, if the user is using a smartphone, the Ministry of Education can provide educational content that is adapted to the screen size. Furthermore, if the user is using a tablet, the Ministry of Education can provide educational content optimized for a larger screen. Additionally, if the user is using a smartwatch, the Ministry of Education can provide concise and highly visible educational content. This allows for more effective education by providing optimal educational content based on the user's device information. Some or all of the above processes by the Ministry of Education may be performed using AI, or not. For example, the Ministry of Education can input the user's device information into an AI and have the AI ​​select the optimal educational content.

[0052] The tutorial section can select the most appropriate tutorial by referring to the user's past operation history when providing tutorials. For example, the tutorial section provides relevant tutorials based on operations the user has performed in the past. The tutorial section can also prioritize providing tutorials that are easier for the user to understand based on the user's past operation history. Furthermore, the tutorial section can analyze the user's past operation history and select the most effective tutorial. For example, the tutorial section provides relevant tutorials based on operations the user has performed in the past. The tutorial section prioritizes providing tutorials that are easier for the user to understand based on the user's past operation history. The tutorial section analyzes the user's past operation history and selects the most effective tutorial. In this way, the optimal tutorial can be provided by referring to the user's past operation history. Some or all of the above processes in the tutorial section may be performed using AI, for example, or not using AI. For example, the tutorial section can input the user's past operation history into AI and have the AI ​​select the most appropriate tutorial.

[0053] The tutorial section can provide the most suitable tutorial based on the user's device information when delivering a tutorial. For example, if the user is using a smartphone, the tutorial section will provide a tutorial that is adapted to the screen size. Furthermore, if the user is using a tablet, the tutorial section can provide a tutorial optimized for a larger screen. Additionally, if the user is using a smartwatch, the tutorial section can provide a concise and easy-to-understand tutorial. This allows for more effective tutorials by providing the most suitable tutorial based on the user's device information. Some or all of the above processing in the tutorial section may be performed using AI, or not. For example, the tutorial section can input the user's device information into AI and have the AI ​​select the most suitable tutorial.

[0054] The checking unit can select the most appropriate health checkpoints by referring to the user's past health data when providing health checkpoints. For example, the checking unit provides relevant health checkpoints based on health data previously acquired by the user. The checking unit can also prioritize providing health checkpoints that the user understands well based on their past health data. Furthermore, the checking unit can analyze the user's past health data and select the most effective health checkpoints. For example, the checking unit provides relevant health checkpoints based on health data previously acquired by the user. The checking unit prioritizes providing health checkpoints that the user understands well based on their past health data. The checking unit analyzes the user's past health data and selects the most effective health checkpoints. This allows the checking unit to provide the most appropriate health checkpoints by referring to the user's past health data. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the user's past health data into AI and have AI select the most appropriate health checkpoints.

[0055] The checking unit can provide optimal health checkpoints based on the user's device information when providing health checkpoints. For example, if the user is using a smartphone, the checking unit provides health checkpoints that are adapted to the screen size. Furthermore, if the user is using a tablet, the checking unit can provide health checkpoints optimized for a larger screen. Additionally, if the user is using a smartwatch, the checking unit can provide concise and highly visible health checkpoints. This allows for more effective checks by providing optimal health checkpoints based on the user's device information. Some or all of the above processing in the checking unit may be performed using AI, or not. For example, the checking unit can input the user's device information into AI and have AI select the optimal health checkpoints.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] Next-generation game platforms not only enhance character stats based on user health data, but can also analyze users' past gameplay data to suggest optimal in-game goals and challenges. For example, they can suggest the next goal to tackle based on goals and challenges the user has achieved and completed in the past. They can also provide challenges of appropriate difficulty according to the user's play style. Furthermore, they can analyze the user's past play data and provide advice on how to enhance specific skills and stats. This allows users to work on goals and challenges that suit them, enabling them to enjoy the game more effectively.

[0058] Next-generation game platforms can not only enhance character stats based on user health data, but also offer in-game events and quests that only occur in specific real-world locations based on the user's geographical location. For example, if a user is in a park, they can be offered special quests that only occur within the park. Similarly, if a user is in a gym, they can be offered fitness challenges that only occur within the gym. Furthermore, if a user is in a tourist destination, they can be offered quests based on the history and culture associated with that location. This allows users to enjoy a game experience that is linked to their real-world location, resulting in a greater sense of immersion.

[0059] Next-generation game platforms can not only enhance character stats based on users' health data, but also analyze users' social media activity to promote in-game interaction and cooperative play. For example, if a user posts about exercise on social media, it can match them with other users who share the same interests and suggest cooperative play. Similarly, if a user posts about health, it can suggest relevant in-game events and challenges. Furthermore, if a user posts about stress, it can suggest relaxing in-game activities. This allows for a more enriching gaming experience by leveraging users' social media activity.

[0060] Next-generation game platforms can not only enhance character stats based on user health data, but also analyze past health data to suggest optimal fitness plans and training menus. For example, they can suggest the next fitness plan to challenge based on exercise goals the user has achieved in the past. They can also provide appropriate training menus according to the user's exercise patterns. Furthermore, they can analyze the user's health data and provide advice to achieve specific health goals. This allows users to engage in fitness plans and training menus tailored to them, enabling them to maintain their health more effectively.

[0061] Next-generation game platforms can not only enhance character stats based on user health data, but also provide special items and abilities usable only in specific real-world locations based on the user's geographical location. For example, if a user is in a park, they can be offered special items usable only within the park. Similarly, if a user is in a gym, they can be offered special abilities usable only within the gym. Furthermore, if a user is in a tourist destination, they can be offered special items and abilities related to that location. This allows users to enjoy special experiences linked to real-world locations, resulting in a greater sense of immersion.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The acquisition unit acquires health data. The acquisition unit can acquire data such as heart rate, steps taken, and calorie consumption from smart devices, for example. The acquisition unit can also acquire data from smart devices in real time. Furthermore, the acquisition unit can acquire data from smart devices to track the user's exercise and health goals. For example, the acquisition unit acquires heart rate data from a smartwatch to monitor the user's exercise intensity. The acquisition unit acquires step count data from a fitness tracker to measure the user's walking distance. The acquisition unit acquires calorie consumption data from a smartphone to record the user's energy expenditure. Step 2: The Enhancement Unit strengthens the character's stats based on the data acquired by the Acquisition Unit. For example, if the user walks 10,000 steps, the Enhancement Unit can improve the character's stamina and speed in the game. The Enhancement Unit can also increase the character's skill level if the user achieves specific health goals. Furthermore, the Enhancement Unit can strengthen the character's stats in real time based on the acquired data. For example, if the user runs for 30 minutes, the Enhancement Unit will improve the character's endurance. If the user exercises three times a week, the Enhancement Unit will strengthen the character's attack power. If the user achieves their daily calorie consumption goal, the Enhancement Unit will improve the character's defense power. Step 3: The provider will offer special abilities or items for achieving specific health goals. For example, the provider can offer special weapons or armor in the game if a user goes for a 30-minute run. The provider can also offer special skills in the game if a user walks 10,000 steps. Furthermore, the provider can offer special items for achieving specific health goals. For example, the provider can offer special equipment in the game if a user exercises three times a week. The provider can offer special currency in the game if a user achieves their daily calorie expenditure goal. The provider can offer a special title in the game if a user achieves their monthly health goal.

[0064] (Example of form 2) The next-generation game platform according to an embodiment of the present invention is a system that enhances the status and skills of in-game characters in real time based on health data acquired from a smart device. This system allows users to acquire special abilities and items in the game each time they achieve exercise and health goals in the real world. This provides a new gaming experience that allows users to adopt a healthy lifestyle while having fun. For example, a user wears a smart device and sets daily exercise and health goals. For instance, they might set goals such as walking 10,000 steps a day or running for 30 minutes. These goals are tracked in real time based on data acquired from the smart device. The acquired health data is then reflected in the status and skills of the in-game character. For example, if a user walks 10,000 steps, the in-game character's stamina and speed will improve. Furthermore, by achieving specific health goals, users can acquire special abilities and items in the game. For example, running for 30 minutes can result in acquiring special weapons and armor in the game. In addition, the game incorporates educational content and interactive tutorials, allowing users to learn basic health management knowledge. For example, health advice and training guides are provided within the game, making them intuitively understandable to the user. Furthermore, regular health checkpoints are provided, and feedback is given according to progress. In this way, the present invention targets gamers and fitness enthusiasts and offers a new gaming experience that addresses issues such as lack of exercise, lack of health management awareness, and balancing gaming and life. Users can overcome a sedentary lifestyle and adopt a healthy lifestyle by incorporating daily exercise as part of gameplay. In addition, by linking daily achievements in the game with real-world activities, users can optimize their life balance. As a result, the next-generation gaming platform allows users to adopt a healthy lifestyle while having fun.

[0065] The next-generation game platform according to this embodiment comprises an acquisition unit, an enhancement unit, and a provision unit. The acquisition unit acquires health data. The acquisition unit can acquire data such as heart rate, steps taken, and calorie consumption from a smart device, for example. The acquisition unit can also acquire data from a smart device in real time. Furthermore, the acquisition unit can acquire data from a smart device to track the user's exercise and health goals. For example, the acquisition unit acquires heart rate data from a smartwatch to monitor the user's exercise intensity. The acquisition unit acquires step count data from a fitness tracker to measure the user's walking distance. The acquisition unit acquires calorie consumption data from a smartphone to record the user's energy consumption. The enhancement unit enhances the character's status based on the data acquired by the acquisition unit. For example, if the user walks 10,000 steps, the enhancement unit can improve the in-game character's stamina and speed. The enhancement unit can also increase the character's skill level if the user achieves a specific health goal. Furthermore, the enhancement unit can enhance the character's status in real time based on the acquired data. For example, the Enhancement Department can improve a character's endurance if the user runs for 30 minutes. The Enhancement Department can strengthen a character's attack power if the user exercises three times a week. The Enhancement Department can improve a character's defense power if the user meets their daily calorie expenditure goal. The Provision Department can provide special abilities and items for achieving specific health goals. For example, the Provision Department can provide special weapons and armor in-game if the user runs for 30 minutes. It can also provide special skills in-game if the user walks 10,000 steps. Furthermore, the Provision Department can also provide special items for achieving specific health goals. For example, the Provision Department can provide special equipment in-game if the user exercises three times a week. The Provision Department can provide special currency in-game if the user meets their daily calorie expenditure goal. The Provision Department can provide a special title in-game if the user meets their monthly health goal.This allows the next-generation game platform according to the embodiment to enable users to adopt a healthy lifestyle while having fun.

[0066] The data acquisition unit acquires health data. For example, the data acquisition unit can acquire data such as heart rate, steps taken, and calorie consumption from smart devices. Specifically, it monitors the user's heart rate in real time and records fluctuations in heart rate during exercise through wearable devices such as smartwatches and fitness trackers. This allows the system to understand the user's exercise intensity and cardiopulmonary function. In addition, fitness trackers measure the user's steps taken and distance traveled, recording daily activity levels in detail. This allows the system to accurately understand how far the user has walked and how many calories they have burned. Furthermore, it is possible to record the user's meals and calorie intake through smartphone applications, enabling overall energy balance management. The data acquisition unit centrally collects this data and sends it to a cloud server, monitoring the user's health status in real time and providing the data to the analysis unit and enhancement unit as needed. In this way, the data acquisition unit can efficiently collect user health data and support user health management in conjunction with other functions of the next-generation game platform.

[0067] The Enhancement Department strengthens character stats based on data acquired by the Acquisition Department. Specifically, if a user walks 10,000 steps, the in-game character's stamina and speed can be improved. For example, if a user achieves 10,000 steps in a day, the character's stamina increases by 10 points and speed increases by 5%. Furthermore, if a user achieves specific health goals, the character's skill level can be increased. For example, if a user exercises three times a week, the character's attack power increases by 15 points and a new skill is unlocked. In addition, the Enhancement Department can strengthen character stats in real time based on acquired data. For example, if a user goes for a 30-minute run, the character's endurance increases by 20 points, and the chance of acquiring specific items during the run increases. The Enhancement Department can also increase a character's defense by 10 points if a user achieves their daily calorie expenditure goal. This allows the Enhancement Department to leverage user health data to strengthen in-game characters and motivate users to maintain a healthy lifestyle. Moreover, the Enhancement Department can analyze user health data and propose optimal enhancement plans for individual users. This allows users to perform effective training tailored to their own health condition and efficiently strengthen their in-game characters.

[0068] The service provider offers special abilities and items for achieving specific health goals. Specifically, if a user runs for 30 minutes, they can be given special weapons and armor in the game. For example, if a user achieves a 30-minute run, they will receive a powerful sword and armor in the game. The service provider can also offer special skills in the game if a user walks 10,000 steps. For example, if a user achieves 10,000 steps, their character will learn a new magic skill, giving them an advantage in combat. Furthermore, the service provider can offer special items for achieving specific health goals. For example, if a user exercises three times a week, they will receive special equipment in the game. This allows users to enjoy progressing in the game while maintaining a healthy lifestyle. The service provider also offers special in-game currency if a user achieves their daily calorie expenditure goal. For example, if a user achieves their daily calorie expenditure goal, they will receive special in-game currency that can be used to purchase items or strengthen their character. The service provider also offers special titles in the game if a user achieves their monthly health goals. For example, when a user achieves their monthly health goals, they are awarded a special title within the game, allowing them to showcase their achievements to other players. This allows the service provider to support users in adopting a healthy lifestyle while having fun. Furthermore, the service provider can analyze users' health data and propose reward plans tailored to each individual user. This enables users to engage in effective training suited to their health condition and enjoy progressing through the game.

[0069] The system includes an Education Department that provides educational content. The Education Department provides educational content. For example, the Education Department may provide videos on health management. The Education Department may also provide articles on health management. Furthermore, the Education Department may also provide quizzes on health management. For example, the Education Department may provide videos for users to learn basic knowledge of health management. The Education Department may provide articles for users to understand the importance of health management. The Education Department may provide quizzes for users to check their knowledge of health management. In this way, the Education Department enables users to learn basic knowledge of health management. Some or all of the above processes in the Education Department may be performed using AI, for example, or not using AI. For example, the Education Department may input the user's learning history into AI and select the most suitable educational content.

[0070] The system includes a tutorial section that provides interactive tutorials. The tutorial section provides interactive tutorials. For example, the tutorial section can provide simulations that users can learn by operating. The tutorial section can also provide interactive guides. Furthermore, the tutorial section can provide interactive content that users can understand intuitively. For example, the tutorial section provides simulations for users to learn basic knowledge of health management. The tutorial section provides interactive guides for users to understand the importance of health management. The tutorial section provides interactive quizzes for users to check their knowledge of health management. In this way, the tutorial section enables users to intuitively understand knowledge of health management. Some or all of the above processes in the tutorial section may be performed using AI, for example, or without AI. For example, the tutorial section can input the user's operation history into AI and select the most suitable tutorial.

[0071] The system includes a check unit that sets up regular health checkpoints. The check unit can set up regular health checkpoints. For example, the check unit can perform weekly weight measurements. The check unit can also perform monthly health checkups. Furthermore, the check unit can set up checkpoints to periodically evaluate the user's health status. For example, the check unit can set up checkpoints for the user to perform weekly weight measurements. The check unit can set up checkpoints for the user to perform monthly health checkups. The check unit can set up checkpoints for the user to periodically evaluate their health status. This allows the check unit to provide the user with feedback according to their progress. Some or all of the above-described processes in the check unit may be performed using AI, for example, or without AI. For example, the check unit can input the user's health data into AI and select the optimal checkpoints.

[0072] The data acquisition unit can acquire health data from smart devices. For example, the data acquisition unit can acquire heart rate data from a smartphone. It can also acquire step count data from a smartwatch. Furthermore, it can acquire calorie consumption data from a fitness tracker. For example, the data acquisition unit acquires heart rate data from a smartphone to monitor the user's exercise intensity. The data acquisition unit acquires step count data from a smartwatch to measure the user's walking distance. The data acquisition unit acquires calorie consumption data from a fitness tracker to record the user's energy consumption. This allows for real-time tracking of the user's exercise and health goals by acquiring health data from smart devices. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input data acquired from smart devices into an AI and have the AI ​​perform data analysis.

[0073] The enhancement unit can enhance a character's stats in real time based on acquired health data. For example, if a user walks 10,000 steps, the enhancement unit will improve the character's stamina and speed in the game. The enhancement unit can also increase a character's skill level if the user achieves specific health goals. Furthermore, the enhancement unit can enhance a character's stats in real time based on acquired data. For example, if a user runs for 30 minutes, the enhancement unit will improve the character's endurance. If a user exercises three times a week, the enhancement unit will improve the character's attack power. If a user achieves their daily calorie consumption goal, the enhancement unit will improve the character's defense power. In this way, by enhancing the character's stats in real time based on acquired health data, the user's exercise and achievement of health goals are reflected in the game. Some or all of the above processes in the enhancement unit may be performed using AI, for example, or not. For example, the enhancement unit can input acquired health data into an AI and have the AI ​​perform the character stat enhancements.

[0074] The data acquisition unit estimates the user's emotions and adjusts the timing of health data acquisition based on the estimated emotions. For example, if the user is stressed, the data acquisition unit adjusts the timing to acquire health data during periods of relaxation. It can also adjust the timing to acquire health data during rest periods if the user is tired after exercise. Furthermore, if the user is excited, the data acquisition unit can adjust the timing to acquire health data in real time during exercise. For example, if the user is stressed, the data acquisition unit acquires heart rate data during periods of relaxation. If the user is tired after exercise, the data acquisition unit acquires calorie consumption data during rest periods. If the user is excited, the data acquisition unit acquires step count data in real time during exercise. This allows for data acquisition at more appropriate times by adjusting the timing of health data acquisition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user emotion data into AI and have AI adjust the timing of health data acquisition.

[0075] The data acquisition unit can analyze the user's past health data and select the optimal acquisition method. For example, the acquisition unit can analyze the user's past exercise patterns and acquire health data at the most effective time. The acquisition unit can also select a method for acquiring data after a specific exercise based on the user's past health data. Furthermore, the acquisition unit can adjust the frequency of data acquisition based on the user's past health data. For example, the acquisition unit can analyze the user's past exercise patterns and acquire heart rate data at the most effective time. The acquisition unit can select a method for acquiring calorie consumption data after a specific exercise based on the user's past health data. The acquisition unit adjusts the frequency of data acquisition based on the user's past health data. In this way, the optimal acquisition method can be selected by analyzing the user's past health data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past health data into AI and have the AI ​​select the optimal acquisition method.

[0076] The data acquisition unit can filter health data based on the user's current lifestyle and exercise habits. For example, if the user is busy, the acquisition unit prioritizes data that can be acquired in a short time. It can also prioritize acquiring post-exercise data if the user exercises regularly. Furthermore, the acquisition unit can adjust the timing of data acquisition to match the user's lifestyle. For example, if the user is busy, the acquisition unit prioritizes heart rate data that can be acquired in a short time. If the user exercises regularly, the acquisition unit prioritizes acquiring post-exercise calorie consumption data. The acquisition unit adjusts the timing of data acquisition to match the user's lifestyle. This allows for the acquisition of more relevant data by filtering the data based on the user's lifestyle and exercise habits. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's lifestyle and exercise habit data into AI and have the AI ​​perform the data filtering.

[0077] The data acquisition unit can estimate the user's emotions and determine the priority of health data to acquire based on the estimated user emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring heart rate and stress level data. It can also prioritize acquiring sleep data if the user is relaxed. Furthermore, if the user is exercising, the data acquisition unit can prioritize acquiring exercise intensity and calorie consumption data. This allows for the acquisition of more important data by prioritizing health data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input user emotion data into the AI ​​and allow the AI ​​to determine the priority of health data.

[0078] The data acquisition unit can prioritize acquiring highly relevant data based on the user's geographical location when acquiring health data. For example, if the user is in a park, the data acquisition unit will prioritize acquiring data on steps and exercise distance. Similarly, if the user is in a gym, the data acquisition unit can prioritize acquiring data on exercise intensity and calorie consumption. Furthermore, if the user is at home, the data acquisition unit can prioritize acquiring data on relaxation state and sleep. This allows for the acquisition of more appropriate data by prioritizing the acquisition of highly relevant data based on the user's geographical location. Some or all of the above processing in the data acquisition unit may be performed using AI, or without AI. For example, the data acquisition unit can input the user's geographical location into AI and have AI select highly relevant data.

[0079] The data acquisition unit can analyze a user's social media activity and acquire relevant data when acquiring health data. For example, if a user posts about exercise on social media, the data acquisition unit will prioritize acquiring that exercise data. The data acquisition unit can also prioritize acquiring stress level data if a user posts about stress. Furthermore, if a user posts about health, the data acquisition unit can prioritize acquiring relevant health data. For example, if a user posts about exercise on social media, the data acquisition unit will prioritize acquiring that exercise data. If a user posts about stress, the data acquisition unit will prioritize acquiring stress level data. If a user posts about health, the data acquisition unit will prioritize acquiring relevant health data. In this way, relevant health data can be acquired by analyzing a user's social media activity. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity data into AI and have AI select relevant health data.

[0080] The enhancement unit can estimate the user's emotions and adjust the method of enhancing the character's stats based on the estimated emotions. For example, if the user is relaxed, the enhancement unit may moderate the effect of the stat enhancements. Conversely, if the user is excited, the enhancement unit may also increase the effect of the stat enhancements. Furthermore, if the user is stressed, the enhancement unit may also stabilize the effect of the stat enhancements. For example, if the user is relaxed, the enhancement unit may moderate the effect of the stat enhancements. If the user is excited, the enhancement unit may increase the effect of the stat enhancements. If the user is stressed, the enhancement unit may stabilize the effect of the stat enhancements. This allows for more appropriate enhancement by adjusting the method of enhancing the character's stats based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the enhancement unit may be performed using AI, for example, or without AI. For example, the enhancement unit can input user emotion data into the AI ​​and have the AI ​​adjust the method of status enhancement.

[0081] The enhancement unit can adjust the level of detail in status enhancement based on the importance of health data during enhancement. For example, the enhancement unit can increase the level of detail in status enhancement based on important health data. It can also set the level of detail in status enhancement to a moderate level based on general health data. Furthermore, it can also decrease the level of detail in status enhancement based on supplementary health data. For example, the enhancement unit can increase the level of detail in status enhancement based on important health data. It can set the level of detail in status enhancement to a moderate level based on general health data. It can decrease the level of detail in status enhancement based on supplementary health data. This allows for more effective enhancement by adjusting the level of detail in status enhancement based on the importance of health data. Some or all of the above processing in the enhancement unit may be performed using AI, for example, or without AI. For example, the enhancement unit can input the importance of health data into the AI ​​and have the AI ​​adjust the level of detail in status enhancement.

[0082] The enhancement unit can apply different enhancement algorithms depending on the category of health data during enhancement. For example, the enhancement unit can apply an algorithm to enhance physical strength and speed based on exercise data. It can also apply an algorithm to enhance concentration and recovery based on sleep data. Furthermore, it can apply an algorithm to enhance mental strength and endurance based on stress data. For example, the enhancement unit can apply an algorithm to enhance physical strength and speed based on exercise data. The enhancement unit can apply an algorithm to enhance concentration and recovery based on sleep data. The enhancement unit can apply an algorithm to enhance mental strength and endurance based on stress data. This allows for more appropriate enhancement by applying different enhancement algorithms depending on the category of health data. Some or all of the above processing in the enhancement unit may be performed using AI, for example, or without AI. For example, the enhancement unit can input the categories of health data into the AI ​​and have the AI ​​execute the application of the enhancement algorithms.

[0083] The enhancement unit can estimate the user's emotions and determine the priority of status enhancements based on the estimated emotions. For example, if the user is relaxed, the enhancement unit will prioritize enhancing physical strength and recovery. If the user is excited, the enhancement unit may also prioritize enhancing speed and attack power. Furthermore, if the user is stressed, the enhancement unit may also prioritize enhancing mental strength and endurance. For example, if the user is relaxed, the enhancement unit will prioritize enhancing physical strength and recovery. If the user is excited, the enhancement unit will prioritize enhancing speed and attack power. If the user is stressed, the enhancement unit will prioritize enhancing mental strength and endurance. This allows for prioritizing the enhancement of more important stats by determining the priority of status enhancements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AIs (e.g., LLMs) or multimodal generation AIs. Some or all of the above processing in the enhancement unit may be performed using AI, for example, or without AI. For example, the enhancement unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of status enhancements.

[0084] The enhancement unit can determine the priority of status enhancements based on the timing of health data acquisition during enhancement. For example, the enhancement unit can determine the priority of status enhancements based on recently acquired health data. The enhancement unit can also adjust the priority of status enhancements based on past health data. Furthermore, the enhancement unit can determine the priority of status enhancements based on health data acquired during a specific period. For example, the enhancement unit can determine the priority of status enhancements based on recently acquired health data. The enhancement unit can adjust the priority of status enhancements based on past health data. The enhancement unit can determine the priority of status enhancements based on health data acquired during a specific period. This allows for enhancements to be performed at a more appropriate time by determining the priority of status enhancements based on the timing of health data acquisition. Some or all of the above processing in the enhancement unit may be performed using AI, for example, or without AI. For example, the enhancement unit can input the timing of health data acquisition into the AI ​​and have the AI ​​determine the priority of status enhancements.

[0085] The enhancement unit can adjust the order of status enhancements based on the relevance of health data during enhancement. For example, the enhancement unit determines the order of status enhancements based on directly related health data. The enhancement unit can also adjust the order of status enhancements based on indirectly related health data. Furthermore, the enhancement unit can also determine the order of status enhancements based on supplementary health data. For example, the enhancement unit determines the order of status enhancements based on directly related health data. The enhancement unit adjusts the order of status enhancements based on indirectly related health data. The enhancement unit determines the order of status enhancements based on supplementary health data. This allows for more effective enhancement by adjusting the order of status enhancements based on the relevance of health data. Some or all of the above processing in the enhancement unit may be performed using AI, for example, or without AI. For example, the enhancement unit can input the relevance of health data into AI and have AI adjust the order of status enhancements.

[0086] The service provider can estimate the user's emotions and adjust the method of providing special abilities and items based on the estimated emotions. For example, if the user is relaxed, the service provider will provide special abilities and items slowly. If the user is excited, the service provider can also provide special abilities and items quickly. Furthermore, if the user is stressed, the service provider can provide special abilities and items steadily. For example, if the user is relaxed, the service provider will provide special abilities and items slowly. If the user is excited, the service provider will provide special abilities and items quickly. If the user is stressed, the service provider will provide special abilities and items steadily. This allows for more appropriate provision by adjusting the method of providing special abilities and items based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotional data into the AI ​​and have the AI ​​adjust how special abilities or items are provided.

[0087] The service provider can adjust the level of detail of the abilities and items provided based on the degree of achievement of health goals. For example, if a high health goal is achieved, the service provider can provide detailed abilities and items. If a moderate health goal is achieved, the service provider can also provide general abilities and items. Furthermore, if a low health goal is achieved, the service provider can provide simple abilities and items. For example, if a high health goal is achieved, the service provider can provide detailed abilities and items. If a moderate health goal is achieved, the service provider can provide general abilities and items. If a low health goal is achieved, the service provider can provide simple abilities and items. This allows for more effective service provision by adjusting the level of detail of the abilities and items provided based on the degree of achievement of health goals. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the degree of achievement of health goals into AI and have AI adjust the level of detail of the abilities and items provided.

[0088] The service provider can apply different service provision algorithms depending on the health goal category at the time of provision. For example, if the exercise goal is achieved, the service provider can provide abilities or items to improve physical strength and speed. Similarly, if the sleep goal is achieved, the service provider can provide abilities or items to improve concentration and recovery. Furthermore, if the stress management goal is achieved, the service provider can provide abilities or items to improve mental strength and endurance. This allows for more appropriate provision by applying different service provision algorithms depending on the health goal category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the health goal category into the AI ​​and have the AI ​​apply the service provision algorithm.

[0089] The service provider can estimate the user's emotions and determine the priority of the abilities and items to offer based on the estimated emotions. For example, if the user is relaxed, the service provider will prioritize providing healing abilities and items. If the user is excited, the service provider can also prioritize providing offensive abilities and items. Furthermore, if the user is stressed, the service provider can also prioritize providing defensive abilities and items. For example, if the user is relaxed, the service provider will prioritize providing healing abilities and items. If the user is excited, the service provider will prioritize providing offensive abilities and items. If the user is stressed, the service provider will prioritize providing defensive abilities and items. By determining the priority of abilities and items to offer based on the user's emotions, more important abilities and items can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of the abilities and items to be offered.

[0090] The service provider can determine the priority of the abilities and items to be provided based on the timing of achieving health goals. For example, the service provider can determine the priority of abilities and items based on recently achieved health goals. The service provider can also adjust the priority of abilities and items based on health goals achieved in the past. Furthermore, the service provider can determine the priority of abilities and items based on health goals achieved within a specific period. For example, the service provider can determine the priority of abilities and items based on recently achieved health goals. The service provider can adjust the priority of abilities and items based on health goals achieved in the past. The service provider can determine the priority of abilities and items based on health goals achieved within a specific period. By determining the priority of abilities and items to be provided based on the timing of achieving health goals, services can be provided at a more appropriate time. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the timing of achieving health goals into the AI ​​and have the AI ​​determine the priority of the abilities and items to be provided.

[0091] The delivery unit can adjust the order of abilities and items provided based on the relevance of health goals at the time of delivery. For example, the delivery unit can determine the order of abilities and items based on directly relevant health goals. It can also adjust the order of abilities and items based on indirectly relevant health goals. Furthermore, it can determine the order of abilities and items based on supplementary health goals. For example, the delivery unit can determine the order of abilities and items based on directly relevant health goals. The delivery unit can adjust the order of abilities and items based on indirectly relevant health goals. The delivery unit can determine the order of abilities and items based on supplementary health goals. This allows for more effective delivery by adjusting the order of abilities and items provided based on the relevance of health goals. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance of health goals into AI and have AI adjust the order of abilities and items provided.

[0092] The Ministry of Education can estimate the user's emotions and adjust the way educational content is delivered based on those estimated emotions. For example, if the user is relaxed, the Ministry of Education can provide educational content that includes detailed explanations. If the user is excited, the Ministry of Education can also provide visually stimulating educational content. Furthermore, if the user is stressed, the Ministry of Education can provide simple and easy-to-understand educational content. For example, if the user is relaxed, the Ministry of Education can provide educational content that includes detailed explanations. If the user is excited, the Ministry of Education can provide visually stimulating educational content. If the user is stressed, the Ministry of Education can provide simple and easy-to-understand educational content. This allows for more appropriate education by adjusting the way educational content is delivered based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Ministry of Education may be performed using AI, for example, or without AI. For example, the Ministry of Education can input user emotion data into AI and have the AI ​​adjust how educational content is delivered.

[0093] The Ministry of Education can select the most suitable educational content by referring to the user's past learning history when providing educational content. For example, the Ministry of Education can provide relevant educational content based on what the user has learned in the past. The Ministry of Education can also prioritize providing content that the user has a high level of understanding of based on their past learning history. Furthermore, the Ministry of Education can analyze the user's past learning history and select the most effective educational content. For example, the Ministry of Education can provide relevant educational content based on what the user has learned in the past. The Ministry of Education can prioritize providing content that the user has a high level of understanding of based on their past learning history. The Ministry of Education can analyze the user's past learning history and select the most effective educational content. In this way, the Ministry of Education can provide the most suitable educational content by referring to the user's past learning history. Some or all of the above processes by the Ministry of Education may be performed using AI, for example, or not using AI. For example, the Ministry of Education can input the user's past learning history into AI and have the AI ​​select the most suitable educational content.

[0094] The Ministry of Education can estimate a user's emotions and prioritize educational content based on those emotions. For example, if a user is relaxed, the Ministry of Education may prioritize detailed educational content. If a user is excited, the Ministry of Education may prioritize visually stimulating educational content. Furthermore, if a user is stressed, the Ministry of Education may prioritize simple and easily viewable educational content. For example, if a user is relaxed, the Ministry of Education may prioritize detailed educational content. If a user is excited, the Ministry of Education may prioritize visually stimulating educational content. If a user is stressed, the Ministry of Education may prioritize simple and easily viewable educational content. This allows for the prioritization of more important content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Ministry of Education may be performed using AI, for example, or without AI. For example, the Ministry of Education could input user sentiment data into an AI and have the AI ​​determine the priority of educational content.

[0095] The Ministry of Education can provide optimal educational content based on the user's device information. For example, if the user is using a smartphone, the Ministry of Education can provide educational content that is adapted to the screen size. Furthermore, if the user is using a tablet, the Ministry of Education can provide educational content optimized for a larger screen. Additionally, if the user is using a smartwatch, the Ministry of Education can provide concise and highly visible educational content. This allows for more effective education by providing optimal educational content based on the user's device information. Some or all of the above processes by the Ministry of Education may be performed using AI, or not. For example, the Ministry of Education can input the user's device information into an AI and have the AI ​​select the optimal educational content.

[0096] The tutorial section can estimate the user's emotions and adjust how the tutorial is delivered based on those emotions. For example, if the user is relaxed, the tutorial section can provide a tutorial with detailed explanations. If the user is excited, the tutorial section can also provide a visually stimulating tutorial. Furthermore, if the user is stressed, the tutorial section can provide a simple and easy-to-understand tutorial. For example, if the user is relaxed, the tutorial section can provide a tutorial with detailed explanations. If the user is excited, the tutorial section can provide a visually stimulating tutorial. If the user is stressed, the tutorial section can provide a simple and easy-to-understand tutorial. This allows for more appropriate tutorials by adjusting how the tutorial is delivered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tutorial section may be performed using AI, for example, or without AI. For example, the tutorial section can input user emotion data into an AI and have the AI ​​adjust how the tutorial is delivered.

[0097] The tutorial section can select the most appropriate tutorial by referring to the user's past operation history when providing tutorials. For example, the tutorial section provides relevant tutorials based on operations the user has performed in the past. The tutorial section can also prioritize providing tutorials that are easier for the user to understand based on the user's past operation history. Furthermore, the tutorial section can analyze the user's past operation history and select the most effective tutorial. For example, the tutorial section provides relevant tutorials based on operations the user has performed in the past. The tutorial section prioritizes providing tutorials that are easier for the user to understand based on the user's past operation history. The tutorial section analyzes the user's past operation history and selects the most effective tutorial. In this way, the optimal tutorial can be provided by referring to the user's past operation history. Some or all of the above processes in the tutorial section may be performed using AI, for example, or not using AI. For example, the tutorial section can input the user's past operation history into AI and have the AI ​​select the most appropriate tutorial.

[0098] The tutorial section can estimate the user's emotions and prioritize tutorials based on those emotions. For example, if the user is relaxed, the tutorial section will prioritize detailed tutorials. If the user is excited, the tutorial section may also prioritize visually stimulating tutorials. Furthermore, if the user is stressed, the tutorial section may prioritize simple and easy-to-understand tutorials. For example, if the user is relaxed, the tutorial section will prioritize detailed tutorials. If the user is excited, the tutorial section will prioritize visually stimulating tutorials. If the user is stressed, the tutorial section will prioritize simple and easy-to-understand tutorials. This allows for prioritizing more important tutorials based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tutorial section may be performed using AI, for example, or without AI. For example, the tutorial section can input user emotion data into the AI ​​and have the AI ​​determine the priority of the tutorials.

[0099] The tutorial section can provide the most suitable tutorial based on the user's device information when delivering a tutorial. For example, if the user is using a smartphone, the tutorial section will provide a tutorial that is adapted to the screen size. Furthermore, if the user is using a tablet, the tutorial section can provide a tutorial optimized for a larger screen. Additionally, if the user is using a smartwatch, the tutorial section can provide a concise and easy-to-understand tutorial. This allows for more effective tutorials by providing the most suitable tutorial based on the user's device information. Some or all of the above processing in the tutorial section may be performed using AI, or not. For example, the tutorial section can input the user's device information into AI and have the AI ​​select the most suitable tutorial.

[0100] The checking unit can estimate the user's emotions and adjust the method of providing health checkpoints based on the estimated user emotions. For example, if the user is relaxed, the checking unit can provide health checkpoints with detailed explanations. If the user is excited, the checking unit can also provide visually stimulating health checkpoints. Furthermore, if the user is stressed, the checking unit can also provide simple and easily visible health checkpoints. For example, if the user is relaxed, the checking unit can provide health checkpoints with detailed explanations. If the user is excited, the checking unit can provide visually stimulating health checkpoints. If the user is stressed, the checking unit can provide simple and easily visible health checkpoints. This allows for more appropriate checks by adjusting the method of providing health checkpoints based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input user emotion data into the AI ​​and have the AI ​​adjust how health check points are provided.

[0101] The checking unit can select the most appropriate health checkpoints by referring to the user's past health data when providing health checkpoints. For example, the checking unit provides relevant health checkpoints based on health data previously acquired by the user. The checking unit can also prioritize providing health checkpoints that the user understands well based on their past health data. Furthermore, the checking unit can analyze the user's past health data and select the most effective health checkpoints. For example, the checking unit provides relevant health checkpoints based on health data previously acquired by the user. The checking unit prioritizes providing health checkpoints that the user understands well based on their past health data. The checking unit analyzes the user's past health data and selects the most effective health checkpoints. This allows the checking unit to provide the most appropriate health checkpoints by referring to the user's past health data. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the user's past health data into AI and have AI select the most appropriate health checkpoints.

[0102] The checking unit can estimate the user's emotions and prioritize health checkpoints based on the estimated emotions. For example, if the user is relaxed, the checking unit will prioritize providing detailed health checkpoints. If the user is excited, the checking unit can also prioritize providing visually stimulating health checkpoints. Furthermore, if the user is stressed, the checking unit can also prioritize providing simple and easily visible health checkpoints. For example, if the user is relaxed, the checking unit will prioritize providing detailed health checkpoints. If the user is excited, the checking unit will prioritize providing visually stimulating health checkpoints. If the user is stressed, the checking unit will prioritize providing simple and easily visible health checkpoints. This allows for the prioritization of health checkpoints based on the user's emotions, thereby prioritizing the provision of more important checkpoints. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of health checkpoints.

[0103] The checking unit can provide optimal health checkpoints based on the user's device information when providing health checkpoints. For example, if the user is using a smartphone, the checking unit provides health checkpoints that are adapted to the screen size. Furthermore, if the user is using a tablet, the checking unit can provide health checkpoints optimized for a larger screen. Additionally, if the user is using a smartwatch, the checking unit can provide concise and highly visible health checkpoints. This allows for more effective checks by providing optimal health checkpoints based on the user's device information. Some or all of the above processing in the checking unit may be performed using AI, or not. For example, the checking unit can input the user's device information into AI and have AI select the optimal health checkpoints.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] Next-generation game platforms can not only enhance the stats and skills of in-game characters based on user health data, but also estimate user emotions and dynamically change in-game events and scenarios based on those emotions. For example, if a user is stressed, it can provide calming scenarios and events to help them relax. If a user is excited, it can provide action-packed events and scenarios. Furthermore, if a user is relaxed, it can provide scenarios with strong exploration and puzzle elements. This allows for a game experience tailored to the user's emotions, resulting in more immersive gameplay.

[0106] Next-generation game platforms not only enhance character stats based on user health data, but can also analyze users' past gameplay data to suggest optimal in-game goals and challenges. For example, they can suggest the next goal to tackle based on goals and challenges the user has achieved and completed in the past. They can also provide challenges of appropriate difficulty according to the user's play style. Furthermore, they can analyze the user's past play data and provide advice on how to enhance specific skills and stats. This allows users to work on goals and challenges that suit them, enabling them to enjoy the game more effectively.

[0107] Next-generation game platforms can not only enhance character stats based on user health data, but also estimate user emotions and dynamically change in-game music and sound effects based on those emotions. For example, if a user is stressed, calming music can be played. Conversely, if a user is excited, fast-paced music and intense sound effects can be played. Furthermore, if a user is relaxed, music emphasizing natural or ambient sounds can be played. This allows for a more immersive gaming experience by providing music and sound effects that respond to the user's emotions.

[0108] Next-generation game platforms can not only enhance character stats based on user health data, but also offer in-game events and quests that only occur in specific real-world locations based on the user's geographical location. For example, if a user is in a park, they can be offered special quests that only occur within the park. Similarly, if a user is in a gym, they can be offered fitness challenges that only occur within the gym. Furthermore, if a user is in a tourist destination, they can be offered quests based on the history and culture associated with that location. This allows users to enjoy a game experience that is linked to their real-world location, resulting in a greater sense of immersion.

[0109] Next-generation game platforms can not only enhance character stats based on user health data, but also estimate user emotions and dynamically change the dialogue and actions of in-game characters based on those emotions. For example, if a user is stressed, the in-game character can offer words of encouragement. If a user is excited, the character can engage in empathetic dialogue. Furthermore, if a user is relaxed, the character can engage in calm dialogue. This allows for a more immersive gaming experience by providing dialogue and actions that respond to the user's emotions.

[0110] Next-generation game platforms can not only enhance character stats based on users' health data, but also analyze users' social media activity to promote in-game interaction and cooperative play. For example, if a user posts about exercise on social media, it can match them with other users who share the same interests and suggest cooperative play. Similarly, if a user posts about health, it can suggest relevant in-game events and challenges. Furthermore, if a user posts about stress, it can suggest relaxing in-game activities. This allows for a more enriching gaming experience by leveraging users' social media activity.

[0111] Next-generation game platforms can not only enhance character stats based on user health data, but also estimate user emotions and dynamically change the in-game reward system based on those emotions. For example, if a user is stressed, they can be offered relaxing rewards. If a user is excited, they can be offered action-oriented rewards. Furthermore, if a user is relaxed, they can be offered rewards with strong exploration or puzzle elements. This allows for increased motivation by providing rewards tailored to the user's emotions.

[0112] Next-generation game platforms can not only enhance character stats based on user health data, but also analyze past health data to suggest optimal fitness plans and training menus. For example, they can suggest the next fitness plan to challenge based on exercise goals the user has achieved in the past. They can also provide appropriate training menus according to the user's exercise patterns. Furthermore, they can analyze the user's health data and provide advice to achieve specific health goals. This allows users to engage in fitness plans and training menus tailored to them, enabling them to maintain their health more effectively.

[0113] Next-generation game platforms can not only enhance character stats based on user health data, but also estimate user emotions and dynamically adjust in-game difficulty based on those emotions. For example, if a user is stressed, the game difficulty can be lowered to allow them to relax. Conversely, if a user is excited, the game difficulty can be increased to provide a challenging experience. Furthermore, if a user is relaxed, a difficulty level with strong exploration and puzzle elements can be offered. This allows for a more appropriate gaming experience by providing difficulty levels that match the user's emotions.

[0114] Next-generation game platforms can not only enhance character stats based on user health data, but also provide special items and abilities usable only in specific real-world locations based on the user's geographical location. For example, if a user is in a park, they can be offered special items usable only within the park. Similarly, if a user is in a gym, they can be offered special abilities usable only within the gym. Furthermore, if a user is in a tourist destination, they can be offered special items and abilities related to that location. This allows users to enjoy special experiences linked to real-world locations, resulting in a greater sense of immersion.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The acquisition unit acquires health data. The acquisition unit can acquire data such as heart rate, steps taken, and calorie consumption from smart devices, for example. The acquisition unit can also acquire data from smart devices in real time. Furthermore, the acquisition unit can acquire data from smart devices to track the user's exercise and health goals. For example, the acquisition unit acquires heart rate data from a smartwatch to monitor the user's exercise intensity. The acquisition unit acquires step count data from a fitness tracker to measure the user's walking distance. The acquisition unit acquires calorie consumption data from a smartphone to record the user's energy expenditure. Step 2: The Enhancement Unit strengthens the character's stats based on the data acquired by the Acquisition Unit. For example, if the user walks 10,000 steps, the Enhancement Unit can improve the character's stamina and speed in the game. The Enhancement Unit can also increase the character's skill level if the user achieves specific health goals. Furthermore, the Enhancement Unit can strengthen the character's stats in real time based on the acquired data. For example, if the user runs for 30 minutes, the Enhancement Unit will improve the character's endurance. If the user exercises three times a week, the Enhancement Unit will strengthen the character's attack power. If the user achieves their daily calorie consumption goal, the Enhancement Unit will improve the character's defense power. Step 3: The provider will offer special abilities or items for achieving specific health goals. For example, the provider can offer special weapons or armor in the game if a user goes for a 30-minute run. The provider can also offer special skills in the game if a user walks 10,000 steps. Furthermore, the provider can offer special items for achieving specific health goals. For example, the provider can offer special equipment in the game if a user exercises three times a week. The provider can offer special currency in the game if a user achieves their daily calorie expenditure goal. The provider can offer a special title in the game if a user achieves their monthly health goal.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the acquisition unit, enhancement unit, provision unit, education unit, tutorial unit, check unit, and emotion estimation function, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit is implemented by the processor 46 of the smart device 14 and acquires health data from the sensors of the smart device 14. The enhancement unit is implemented by the specific processing unit 290 of the data processing unit 12 and enhances the status of the in-game character based on the acquired health data. The provision unit is implemented by the control unit 46A of the smart device 14 and provides special abilities or items when specific health goals are achieved. The education unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides educational content related to health management. The tutorial unit is implemented by the control unit 46A of the smart device 14 and provides an interactive tutorial. The check unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets up periodic health checkpoints. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which adjusts the timing of health data acquisition based on the user's emotions. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the acquisition unit, enhancement unit, provision unit, education unit, tutorial unit, check unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit is implemented by the processor 46 of the smart glasses 214 and acquires health data from the sensors of the smart glasses 214. The enhancement unit is implemented by the specific processing unit 290 of the data processing unit 12 and enhances the status of the in-game character based on the acquired health data. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides special abilities or items when specific health goals are achieved. The education unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides educational content related to health management. The tutorial unit is implemented by the control unit 46A of the smart glasses 214 and provides an interactive tutorial. The check unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets up periodic health checkpoints. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which adjusts the timing of health data acquisition based on the user's emotions. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the acquisition unit, enhancement unit, provision unit, education unit, tutorial unit, check unit, and emotion estimation function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit is implemented by the processor 46 of the headset terminal 314 and acquires health data from the sensors of the headset terminal 314. The enhancement unit is implemented by the specific processing unit 290 of the data processing unit 12 and enhances the status of the in-game character based on the acquired health data. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides special abilities or items when specific health goals are achieved. The education unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides educational content related to health management. The tutorial unit is implemented by the control unit 46A of the headset terminal 314 and provides an interactive tutorial. The check unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets up periodic health checkpoints. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which adjusts the timing of health data acquisition based on the user's emotions. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the acquisition unit, enhancement unit, provision unit, education unit, tutorial unit, check unit, and emotion estimation function, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the processor 46 of the robot 414 and acquires health data from the robot 414's sensors. The enhancement unit is implemented by the specific processing unit 290 of the data processing unit 12 and enhances the status of the in-game character based on the acquired health data. The provision unit is implemented by the control unit 46A of the robot 414 and provides special abilities or items when specific health goals are achieved. The education unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides educational content related to health management. The tutorial unit is implemented by the control unit 46A of the robot 414 and provides an interactive tutorial. The check unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets up periodic health checkpoints. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which adjusts the timing of health data acquisition based on the user's emotions. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) A unit for acquiring health data, An enhancement unit that enhances the character's status based on the data acquired by the acquisition unit, It comprises a provisioning unit that provides specific abilities or items by achieving specific health goals. A system characterized by the following features. (Note 2) It has an education department that provides educational content. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a tutorial section that provides interactive tutorials. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a check section that provides regular health check points. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, Obtain health data from smart devices The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reinforced section is The game enhances character stats in real time based on acquired health data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of health data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the user's past health data and select the appropriate method of data acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring health data, filtering is performed based on the user's current lifestyle and exercise habits. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of health data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring health data, the system prioritizes acquiring highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring health data, we analyze the user's social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reinforced section is The system estimates the user's emotions and adjusts how character stats are enhanced based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reinforced section is During enhancement, the specific method of status enhancement will be adjusted based on the importance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reinforced section is During enhancement, different enhancement algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reinforced section is It estimates the user's emotions and determines the priority of status enhancements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reinforced section is When enhancing a character, the priority of status enhancements is determined based on when health data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reinforced section is During enhancement, the order of status enhancements is adjusted based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how special abilities and items are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, we adjust the specific abilities and items offered based on the degree to which health goals have been achieved. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, different delivery algorithms are applied depending on the health goal category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the abilities and items to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing services, we prioritize the abilities and items to be offered based on the timeline for achieving health goals. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing services, adjust the order of the abilities and items offered based on their relevance to health goals. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Ministry of Education, We estimate user emotions and adjust the delivery method of educational content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Ministry of Education, When providing educational content, the system selects the most suitable content by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Ministry of Education, It estimates user sentiment and prioritizes educational content based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Ministry of Education, When providing educational content, we deliver the most suitable content based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned tutorial section is, It estimates the user's emotions and adjusts how the tutorial is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned tutorial section is, When providing tutorials, the system selects the most suitable tutorial by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned tutorial section is, It estimates the user's emotions and prioritizes tutorials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned tutorial section is, When providing tutorials, we will provide the most suitable tutorial based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned checking unit is The system estimates the user's emotions and adjusts how health checkpoints are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned checking unit is When providing health checkpoints, the system selects the most suitable checkpoints by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned checking unit is The system estimates the user's emotions and prioritizes health checkpoints based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned checking unit is When providing health checkpoints, the system provides the optimal checkpoint based on the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A unit for acquiring health data, An enhancement unit that enhances the character's status based on the data acquired by the acquisition unit, It comprises a provisioning unit that provides specific abilities or items by achieving specific health goals. A system characterized by the following features.

2. It has an education department that provides educational content. The system according to feature 1.

3. It includes a tutorial section that provides interactive tutorials. The system according to feature 1.

4. It is equipped with a check section that provides regular health check points. The system according to feature 1.

5. The acquisition unit is, Obtain health data from smart devices The system according to feature 1.

6. The aforementioned reinforced section is The game enhances character stats in real time based on acquired health data. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of health data acquisition based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, Analyze the user's past health data and select the appropriate method of data acquisition. The system according to feature 1.

9. The acquisition unit is, When acquiring health data, filtering is performed based on the user's current lifestyle and exercise habits. The system according to feature 1.

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