system

Abstract

The system according to this embodiment aims to improve the well-being of individuals and communities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a happiness improvement plan based on the analysis results obtained by the analysis unit. The provision unit provides the plan generated by the generation unit.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, a specific plan for improving the happiness of individuals or communities has not been sufficiently provided, and there is room for improvement. ...

[0005] The system according to the embodiment aims to improve the happiness of individuals or communities.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a happiness improvement plan based on the analysis result obtained by the analysis unit. The provision unit provides the plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve the well-being of individuals and communities. [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 happiness-enhancing AI according to an embodiment of the present invention is a system that uses AI to improve the happiness of individuals and communities, referencing the World Happiness Ranking. The happiness-enhancing AI provides a happiness-enhancing plan customized for each user and enables a more personalized experience through collaboration with other fields such as psychology, VR, community, and health technology. For example, the happiness-enhancing AI analyzes the user's psychological state and lifestyle data to generate an individual happiness-enhancing plan. For instance, it analyzes the user's stress level and sleep patterns and proposes appropriate relaxation methods and exercise plans. In this way, the happiness-enhancing AI can provide a happiness-enhancing plan tailored to the individual needs of each user. Next, the happiness-enhancing AI uses VR to allow users to experience countries and regions with high happiness levels in virtual reality. For example, by experiencing the culture and daily life of Nordic countries in VR, users can learn elements of happiness enhancement. This allows the happiness-enhancing AI to learn how to improve happiness from a new perspective. Furthermore, the happiness-enhancing AI facilitates community building, enabling users to connect with each other socially and engage in collaborative activities. With AI support, the happiness of the group as a whole can also be improved. For example, users with shared hobbies can interact online and collaborate on projects. Furthermore, the happiness-enhancing AI integrates with health technology, using wearable devices to analyze physical and mental health data in real time. For instance, it collects data on exercise, diet, and sleep, and the AI ​​analyzes this data to provide appropriate improvement suggestions. This allows the happiness-enhancing AI to also improve physical well-being. Finally, the happiness-enhancing AI gamifies happiness-enhancing behaviors, providing a system that allows users to pursue happiness while enjoying their daily lives. For example, it can award points for healthy eating and exercise, allowing users to improve their happiness while having fun. In this way, the happiness-enhancing AI is an innovative service that enhances users' physical and mental health and happiness through the provision of personalized happiness-enhancing plans, VR experiences, community building, integration with health technology, and a gamified approach. Through these means, the happiness-enhancing AI can improve users' happiness.

[0029] The happiness-enhancing AI according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user data. User data includes, but is not limited to, behavioral data, health data, and emotional data. The collection unit collects heart rate and sleep data, for example, using a wearable device. The collection unit can also collect user behavioral data using a smartphone application. Furthermore, the collection unit can collect social media activity data. For example, the collection unit collects heart rate data from a wearable device in real time to monitor the user's health status. It records the user's steps and exercise level using a smartphone application and collects them as behavioral data. It analyzes the content of social media posts and the number of likes to collect user emotional data. The analysis unit analyzes the data collected by the collection unit. The analysis is performed by, but is not limited to, methods such as data mining, statistical analysis, and machine learning. For example, the analysis unit uses data mining techniques to analyze the user's behavioral patterns. The analysis unit can also analyze users' health data using statistical analysis. Furthermore, the analysis unit can analyze users' emotional data using machine learning algorithms. For example, the analysis unit uses data mining techniques to extract user behavior patterns and analyze behavioral trends. It analyzes users' health data using statistical analysis to understand changes in their health status. It analyzes users' emotional data using machine learning algorithms to predict changes in their emotions. The generation unit generates well-being plans based on the analysis results obtained by the analysis unit. These well-being plans include, but are not limited to, health improvement plans, stress reduction plans, and social connection strengthening plans. For example, the generation unit generates a health improvement plan tailored to the user based on the analysis results. The generation unit can also generate plans that include relaxation methods for stress reduction. Furthermore, the generation unit can generate plans that include activities to strengthen social connections. For example, the generation unit suggests exercise and diet plans tailored to the user based on the analysis results.To reduce stress, the system generates plans that include meditation and relaxation techniques. To strengthen social connections, it suggests participation in online community activities and offline events. The provider provides the plans generated by the generator. Delivery is done by means of, for example, notifications, email, or in-app messages. For example, the provider provides plans to users using the smartphone's notification function. The provider can also provide plans via email. The provider can also provide plans via in-app messages. For example, the provider notifies users of exercise and meal plans using the smartphone's notification function. It provides users with relaxation techniques and plans to strengthen social connections via email. It provides users with happiness-enhancing plans and encourages their implementation via in-app messages. In this way, the happiness-enhancing AI according to the embodiment can improve the user's happiness.

[0030] The data collection unit collects user data. User data includes, but is not limited to, behavioral data, health data, and emotional data. For example, the data collection unit collects heart rate and sleep data using wearable devices. Specifically, wearable devices monitor the user's biometric data such as heart rate, blood pressure, body temperature, and sleep patterns in real time and transmit this data to a cloud server. The data collection unit can also collect user behavioral data using smartphone apps. It uses the smartphone's GPS function to record the user's travel route and time spent in each location, and uses accelerometers and gyroscopes to measure exercise levels and steps. Furthermore, the data collection unit can also collect social media activity data. For example, the data collection unit analyzes the content of the user's social media posts, the number of likes, and the content of comments to understand the user's emotions and social connection status. This allows the data collection unit to centrally collect diverse user data and build a comprehensive database. Moreover, the data collection unit can update this data in real time, always maintaining the latest information. For example, data from wearable devices is updated every second, and data from smartphone apps is updated in real time according to the user's actions. This allows the data collection unit to always have up-to-date information about the user's state and provide it to the analysis and generation units.

[0031] The analysis unit analyzes the data collected by the collection unit. Analysis is performed using methods such as data mining, statistical analysis, and machine learning, but is not limited to these examples. Specifically, the analysis unit uses data mining techniques to analyze user behavior patterns. For example, it analyzes users' travel routes and time spent in different locations to identify daily behavior patterns and abnormal behavior. The analysis unit can also analyze users' health data using statistical analysis. For example, it analyzes heart rate and sleep data to detect changes or abnormalities in the user's health. Furthermore, the analysis unit can analyze users' emotional data using machine learning algorithms. For example, it analyzes social media posts and the number of likes to predict changes and trends in the user's emotions. This allows the analysis unit to analyze the collected data from multiple perspectives and gain a detailed understanding of the user's state. Additionally, the analysis unit can utilize historical data and statistical information to evaluate long-term trends and risks. For example, it can predict health risks in specific seasons or time periods based on historical health data and propose preventative measures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data early and issue warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates a wellness improvement plan based on the analysis results obtained by the analysis unit. This wellness improvement plan may include, but is not limited to, health improvement plans, stress reduction plans, and social connection strengthening plans. Specifically, the generation unit generates a health improvement plan tailored to the user based on the analysis results. For example, it may suggest appropriate exercise and diet based on the user's heart rate and sleep data. The generation unit can also generate plans that include relaxation methods for stress reduction. For example, it may suggest meditation, deep breathing, and relaxation music based on the user's emotional data. Furthermore, the generation unit can generate plans that include activities to strengthen social connections. For example, it may suggest participation in online communities or offline events based on the user's social media activity data. This allows the generation unit to provide a customized wellness improvement plan tailored to the user's condition and needs. Additionally, the generation unit can collect user feedback, evaluate the effectiveness of the plan, and continuously improve it. For example, it can analyze the results of the plan implemented by the user, strengthening the plan if it was effective, and suggesting a new plan if it was ineffective. This allows the generation unit to provide an optimal plan for continuously improving the user's well-being.

[0033] The service provider delivers plans generated by the generation service provider. Delivery is done through methods such as notifications, emails, and in-app messages, but is not limited to these examples. Specifically, the service provider delivers plans to users using smartphone notification functions. For example, it might notify users of exercise or meal plans to encourage them to follow them. The service provider can also deliver plans via email. For example, it might send relaxation methods or social connection strengthening plans via email to provide users with detailed information. Furthermore, the service provider can deliver plans through in-app messages. For example, it might display well-being plans within the app to encourage users to follow them and monitor their progress. This allows the service provider to deliver plans to users at the appropriate time and support their implementation. Additionally, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its delivery methods. For example, based on feedback from users after they have followed a plan, it can adjust the timing and content of notifications to explore more effective delivery methods. The service provider can also reliably transmit information using multiple communication methods. For example, it can use not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the service provider to quickly and reliably provide users with actionable instructions, thereby improving their well-being.

[0034] The provider can use VR to allow users to experience countries and regions with high happiness levels in virtual reality. For example, the provider can enable users to experience the culture and daily life of Nordic countries in VR. For example, the provider can enable users to experience the natural environment of regions with high happiness levels in VR. Furthermore, the provider can enable users to learn about the social systems and education systems of countries with high happiness levels in VR. For example, the provider can enable users to learn about elements that contribute to increased happiness by experiencing the culture and daily life of Nordic countries in VR. The provider can enable users to experience the natural environment of regions with high happiness levels in VR, thereby achieving relaxation. The provider can enable users to learn about ways to improve happiness from a new perspective by learning about the social systems and education systems of countries with high happiness levels in VR. In this way, by utilizing VR, users can experience countries and regions with high happiness levels and learn about elements that contribute to increased happiness.

[0035] The service provides a platform where users can build social connections and engage in collaborative activities through community building. For example, it enables users with shared hobbies to interact online and work together on projects. For example, it allows users to participate in local events and deepen their social connections. Furthermore, it enables users to share information and engage in collaborative activities within online communities. For example, it allows users with shared hobbies to interact online and work together on projects, thereby strengthening their social connections. By enabling users to participate in local events and deepen their social connections, the service can improve the overall well-being of the group. The service allows users to share information and engage in collaborative activities within online communities, thereby strengthening their social connections. In this way, through community building, users can build social connections and engage in collaborative activities, thereby improving the overall well-being of the group.

[0036] The service provider can analyze physical and mental health data in real time using wearable devices and make appropriate improvement suggestions. For example, the service provider can propose exercise plans based on data collected from wearable devices. For example, the service provider can propose meal plans based on data collected from wearable devices. Furthermore, the service provider can propose sleep improvement plans based on data collected from wearable devices. For example, by proposing exercise plans based on data collected from wearable devices, the service provider can improve the user's physical health. By proposing meal plans based on data collected from wearable devices, the service provider can improve the user's nutritional balance. By proposing sleep improvement plans based on data collected from wearable devices, the service provider can improve the user's sleep quality. In this way, by using wearable devices, physical and mental health data can be analyzed in real time and appropriate improvement suggestions can be made.

[0037] The service provider can gamify actions that improve happiness, providing a system that allows users to pursue happiness while having fun in their daily lives. For example, the service provider can award points for eating healthy meals, allowing users to pursue health while having fun. For example, the service provider can award points for exercising, making it possible for users to continue exercising while having fun. Furthermore, the service provider can award points for actions that improve sleep, making it possible for users to improve their sleep while having fun. For example, by awarding points for eating healthy meals, the service provider can improve users' health by allowing them to pursue health while having fun. By awarding points for exercising, the service provider can improve users' physical health by making it possible for users to continue exercising while having fun. By awarding points for actions that improve sleep, the service provider can improve the quality of users' sleep by making it possible for users to improve their sleep while having fun. In this way, by gamifying actions that improve happiness, users can improve their happiness while having fun.

[0038] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can determine the timing of data collection based on actions the user frequently performed in the past. For example, the data collection unit can analyze the user's past behavior patterns and select the most efficient data collection method. Furthermore, the data collection unit can collect data at specific time periods based on the user's past behavior history. For example, by determining the timing of data collection based on actions the user frequently performed in the past, the data collection unit can obtain more accurate data. The data collection unit can improve the efficiency of data collection by analyzing the user's past behavior patterns and selecting the most efficient data collection method. By collecting data at specific time periods based on the user's past behavior history, the data collection unit can perform data collection that aligns with the user's daily rhythm. This allows for the selection of the optimal data collection method by analyzing the user's past behavior history.

[0039] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to areas of interest that the user is currently interested in. For example, the data collection unit can collect only the necessary data according to the user's lifestyle. The data collection unit can also adjust the scope of data collection considering the user's current lifestyle. For example, by prioritizing the collection of data related to areas of interest that the user is currently interested in, the data collection unit can obtain data that matches the user's interests. The data collection unit can improve the efficiency of data collection by collecting only the necessary data according to the user's lifestyle. The data collection unit can reduce the burden on the user by adjusting the scope of data collection considering the user's current lifestyle. This allows the collection of only the necessary data by filtering data based on the user's current lifestyle and areas of interest.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. For example, the data collection unit can select the optimal data collection method based on the user's geographical location information. Furthermore, the data collection unit can efficiently collect necessary data by considering the user's geographical location information. For example, by prioritizing the collection of data related to the user's current location, the data collection unit can obtain data that is relevant to the user's current situation. The data collection unit can improve the efficiency of data collection by selecting the optimal data collection method based on the user's geographical location information. The data collection unit can reduce the burden on the user by efficiently collecting necessary data while considering the user's geographical location information. As a result, by considering the user's geographical location information, it is possible to prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze users' social media activity and collect data related to topics of interest. For example, the data collection unit can estimate a user's current emotional state from their social media activity and collect the necessary data. The data collection unit can also select the optimal data collection method based on users' social media activity. For example, by analyzing users' social media activity and collecting data related to topics of interest, the data collection unit can obtain data tailored to the user's interests. By estimating a user's current emotional state from their social media activity and collecting the necessary data, the data collection unit can obtain data based on the user's emotions. By selecting the optimal data collection method based on users' social media activity, the data collection unit can improve the efficiency of data collection. This allows for the efficient collection of relevant data by analyzing users' social media activity.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data. For example, the analysis unit can perform a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, by performing a detailed analysis on high-importance data, the analysis unit can obtain more accurate information. By performing a simplified analysis on low-importance data, the analysis unit can perform efficient data processing. By determining the priority of the analysis according to the importance of the data, the analysis unit can prioritize the analysis of important data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply health-related analysis algorithms to health data. For example, the analysis unit can apply social media analysis algorithms to social media data. Furthermore, the analysis unit can apply lifestyle-related analysis algorithms to lifestyle data. For example, by applying health-related analysis algorithms to health data, the analysis unit can understand changes in health status. By applying social media analysis algorithms to social media data, the analysis unit can analyze users' emotions and interests. By applying lifestyle-related analysis algorithms to lifestyle data, the analysis unit can analyze users' lifestyle patterns. This allows for more accurate analysis by applying different analysis algorithms depending on the data category.

[0044] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can analyze the most recent data while referring to past data. Furthermore, the analysis unit can determine the priority of analysis according to the data collection period. For example, by prioritizing the analysis of the most recent data, the analysis unit can obtain the latest information. By analyzing the most recent data while referring to past data, the analysis unit can grasp changes in the data. By determining the priority of analysis according to the data collection period, the analysis unit can prioritize the analysis of important data. In this way, by determining the priority of analysis based on the data collection period, the latest data can be analyzed preferentially.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, by prioritizing the analysis of highly relevant data, the analysis unit can obtain important information early. By postponing the analysis of less relevant data, the analysis unit can perform efficient data processing. By adjusting the order of analysis according to the relevance of the data, the analysis unit can prioritize the analysis of important data. In this way, efficient analysis becomes possible by adjusting the order of analysis based on the relevance of the data.

[0046] The generation unit can generate an optimal plan by analyzing the user's past behavioral data during the generation process. For example, the generation unit can generate an optimal happiness improvement plan based on the user's past behavioral data. For instance, the generation unit can analyze the user's past behavioral patterns and generate the most effective plan. Furthermore, the generation unit can generate customized plans by referencing the user's past behavioral data. For example, by generating an optimal happiness improvement plan based on the user's past behavioral data, the generation unit can provide a plan tailored to the user's needs. The generation unit can improve the user's happiness level by analyzing the user's past behavioral patterns and generating the most effective plan. By generating customized plans by referencing the user's past behavioral data, the generation unit can provide a plan tailored to the user's individual needs. This allows for the generation of an optimal happiness improvement plan by analyzing the user's past behavioral data.

[0047] The generation unit can customize the plan based on the user's current living situation during generation. For example, the generation unit can generate an optimal plan considering the user's current living situation. For example, the generation unit can adjust the content of the plan according to the user's current living situation. Furthermore, the generation unit can generate a customized plan based on the user's current living situation. For example, by generating an optimal plan considering the user's current living situation, the generation unit can provide a plan that meets the user's needs. By adjusting the content of the plan according to the user's current living situation, the generation unit can improve the user's well-being. By generating a customized plan based on the user's current living situation, the generation unit can provide a plan that meets the user's individual needs. In this way, by customizing the plan based on the user's current living situation, a more appropriate plan can be provided.

[0048] The generation unit can generate an optimal plan by considering the user's geographical location information during generation. For example, the generation unit can generate a plan related to the user's current location. For example, the generation unit can generate an optimal plan based on the user's geographical location information. Furthermore, the generation unit can efficiently generate the necessary plan by considering the user's geographical location information. For example, by generating a plan related to the user's current location, the generation unit can provide a plan that suits the user's current situation. By generating an optimal plan based on the user's geographical location information, the generation unit can provide a plan that meets the user's needs. By efficiently generating the necessary plan while considering the user's geographical location information, the generation unit can reduce the burden on the user. As a result, by considering the user's geographical location information, it can provide an optimal plan.

[0049] The generation unit can generate plans by analyzing the user's social media activity during the generation process. For example, the generation unit can analyze the user's social media activity and generate plans related to topics of interest. For example, the generation unit can estimate the user's current emotional state from their social media activity and generate the necessary plan. Furthermore, the generation unit can generate the optimal plan based on the user's social media activity. For example, by analyzing the user's social media activity and generating plans related to topics of interest, the generation unit can provide plans tailored to the user's interests. By estimating the user's current emotional state from their social media activity and generating the necessary plan, the generation unit can provide plans based on the user's emotions. By generating the optimal plan based on the user's social media activity, the generation unit can provide plans tailored to the user's needs. In this way, by analyzing the user's social media activity, it is possible to provide more appropriate plans.

[0050] The service provider can select the optimal delivery method by referring to past user feedback at the time of delivery. For example, the service provider can select the optimal delivery method based on past user feedback. For example, the service provider can identify areas for improvement in the delivery method from past user feedback and optimize it. Furthermore, the service provider can select a customized delivery method by referring to past user feedback. For example, by selecting the optimal delivery method based on past user feedback, the service provider can provide a delivery method that meets the user's needs. By identifying areas for improvement in the delivery method from past user feedback and optimizing it, the service provider can improve user satisfaction. By selecting a customized delivery method by referring to past user feedback, the service provider can provide a delivery method that meets the individual needs of the user. In this way, the service provider can select the optimal delivery method by referring to past user feedback.

[0051] The service provider can customize the plan based on the user's current living situation at the time of delivery. For example, the service provider can provide the optimal plan considering the user's current living situation. For example, the service provider can adjust the content of the plan according to the user's current living situation. Furthermore, the service provider can provide a customized plan based on the user's current living situation. For example, by providing the optimal plan considering the user's current living situation, the service provider can provide a plan that meets the user's needs. By adjusting the content of the plan according to the user's current living situation, the service provider can improve the user's well-being. By providing a customized plan based on the user's current living situation, the service provider can provide a plan that meets the user's individual needs. In this way, by customizing the plan based on the user's current living situation, a more appropriate plan can be provided.

[0052] The service provider can select the optimal service delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a service delivery method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a service delivery method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible service delivery method. For example, if the user is using a smartphone, the service provider can improve user convenience by providing a service delivery method that matches the screen size. If the user is using a tablet, the service provider can improve user visibility by providing a service delivery method optimized for a larger screen. If the user is using a smartwatch, the service provider can improve user convenience by providing a concise and highly visible service delivery method. This allows the service provider to select the optimal service delivery method by considering the user's device information.

[0053] The service provider can analyze the user's social media activity and provide a plan at the time of delivery. For example, the service provider can analyze the user's social media activity and provide a plan related to topics of interest. For example, the service provider can estimate the user's current emotional state from their social media activity and provide a necessary plan. Furthermore, the service provider can provide the optimal plan based on the user's social media activity. For example, by analyzing the user's social media activity and providing a plan related to topics of interest, the service provider can provide a plan that matches the user's interests. By estimating the user's current emotional state from their social media activity and providing a necessary plan, the service provider can provide a plan based on the user's emotions. By providing the optimal plan based on the user's social media activity, the service provider can provide a plan that meets the user's needs. In this way, by analyzing the user's social media activity, a more appropriate plan can be provided.

[0054] The provider can use VR to allow users to experience countries and regions with high happiness levels in virtual reality. For example, the provider can enable users to experience the culture and daily life of Nordic countries in VR. For example, the provider can enable users to experience the natural environment of regions with high happiness levels in VR. Furthermore, the provider can enable users to learn about the social systems and education systems of countries with high happiness levels in VR. For example, the provider can enable users to learn about elements that contribute to increased happiness by experiencing the culture and daily life of Nordic countries in VR. The provider can enable users to experience the natural environment of regions with high happiness levels in VR, thereby achieving relaxation. The provider can enable users to learn about ways to improve happiness from a new perspective by learning about the social systems and education systems of countries with high happiness levels in VR. In this way, by utilizing VR, users can experience countries and regions with high happiness levels and learn about elements that contribute to increased happiness.

[0055] The service provides a platform where users can build social connections and engage in collaborative activities through community building. For example, it enables users with shared hobbies to interact online and work together on projects. For example, it allows users to participate in local events and deepen their social connections. Furthermore, it enables users to share information and engage in collaborative activities within online communities. For example, it allows users with shared hobbies to interact online and work together on projects, thereby strengthening their social connections. By enabling users to participate in local events and deepen their social connections, the service can improve the overall well-being of the group. The service allows users to share information and engage in collaborative activities within online communities, thereby strengthening their social connections. In this way, through community building, users can build social connections and engage in collaborative activities, thereby improving the overall well-being of the group.

[0056] The service provider can analyze physical and mental health data in real time using wearable devices and make appropriate improvement suggestions. For example, the service provider can propose exercise plans based on data collected from wearable devices. For example, the service provider can propose meal plans based on data collected from wearable devices. Furthermore, the service provider can propose sleep improvement plans based on data collected from wearable devices. For example, by proposing exercise plans based on data collected from wearable devices, the service provider can improve the user's physical health. By proposing meal plans based on data collected from wearable devices, the service provider can improve the user's nutritional balance. By proposing sleep improvement plans based on data collected from wearable devices, the service provider can improve the user's sleep quality. In this way, by using wearable devices, physical and mental health data can be analyzed in real time and appropriate improvement suggestions can be made.

[0057] The service provider can gamify actions that improve happiness, providing a system that allows users to pursue happiness while having fun in their daily lives. For example, the service provider can award points for eating healthy meals, allowing users to pursue health while having fun. For example, the service provider can award points for exercising, making it possible for users to continue exercising while having fun. Furthermore, the service provider can award points for actions that improve sleep, making it possible for users to improve their sleep while having fun. For example, by awarding points for eating healthy meals, the service provider can improve users' health by allowing them to pursue health while having fun. By awarding points for exercising, the service provider can improve users' physical health by making it possible for users to continue exercising while having fun. By awarding points for actions that improve sleep, the service provider can improve the quality of users' sleep by making it possible for users to improve their sleep while having fun. In this way, by gamifying actions that improve happiness, users can improve their happiness while having fun.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The happiness enhancement AI can take into account the user's geographical location and provide happiness enhancement plans tailored to the characteristics of each region. For example, it can suggest relaxation methods that incorporate the natural environment to users living in urban areas. It can also suggest strengthening social connections through community activities to users living in rural areas. Furthermore, it can provide happiness enhancement plans based on the culture of the region to users living in different cultural areas. This allows for the provision of more appropriate happiness enhancement plans based on the user's geographical location.

[0060] The happiness enhancement AI can analyze a user's past behavioral data and customize happiness enhancement plans based on their behavioral patterns. For example, a user who has consistently exercised in the past can be offered a plan that incorporates exercise. Similarly, a user who has been prone to stress in the past can be offered a plan that includes relaxation methods to reduce stress. Furthermore, a user who has valued social connections in the past can be offered a plan that includes community activities. This allows the AI ​​to provide more effective happiness enhancement plans based on the user's past behavioral data.

[0061] The happiness enhancement AI can analyze a user's social media activity and provide happiness enhancement plans related to topics of interest. For example, if a user frequently posts about health on social media, it can suggest a health improvement plan. If a user posts about stress, it can suggest a plan that includes relaxation methods to reduce stress. Furthermore, if a user posts about social connections, it can suggest a plan that includes community activities. This allows for the provision of more appropriate happiness enhancement plans based on the user's social media activity.

[0062] The happiness enhancement AI can customize happiness enhancement plans based on the user's current lifestyle. For example, if a user is busy with work, it can suggest effective relaxation methods that can be done in a short amount of time. If a user values ​​time at home, it can suggest plans that include activities that can be enjoyed with family. Furthermore, if a user is interested in health, it can suggest health improvement plans. This allows the AI ​​to provide more appropriate happiness enhancement plans based on the user's current lifestyle.

[0063] The happiness-enhancing AI can select the optimal delivery method by considering the user's device information. For example, if the user is using a smartphone, it can provide a delivery method adapted to the screen size. If the user is using a tablet, it can provide a delivery method optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible delivery method. In this way, the AI ​​can select the optimal delivery method based on the user's device information.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects user data. User data includes behavioral data, health data, emotional data, etc. For example, the data collection unit collects heart rate and sleep data using wearable devices and user behavioral data using smartphone apps. It can also collect social media activity data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning. For example, the analysis unit uses data mining techniques to analyze user behavior patterns, statistical analysis to analyze health data, and machine learning algorithms to analyze emotional data. Step 3: The generation unit generates a wellness improvement plan based on the analysis results obtained by the analysis unit. The wellness improvement plan includes health improvement plans, stress reduction plans, and social connection strengthening plans. For example, based on the analysis results, the generation unit suggests exercise plans, meal plans, relaxation methods, and online community activities that are suitable for the user. Step 4: The provider delivers the plans generated by the generator. Delivery is done via methods such as notifications, email, and in-app messages. For example, the provider might use smartphone notifications to send exercise and meal plans, provide relaxation methods and social connection strengthening plans via email, and provide wellness improvement plans via in-app messages.

[0066] (Example of form 2) The happiness-enhancing AI according to an embodiment of the present invention is a system that uses AI to improve the happiness of individuals and communities, referencing the World Happiness Ranking. The happiness-enhancing AI provides a happiness-enhancing plan customized for each user and enables a more personalized experience through collaboration with other fields such as psychology, VR, community, and health technology. For example, the happiness-enhancing AI analyzes the user's psychological state and lifestyle data to generate an individual happiness-enhancing plan. For instance, it analyzes the user's stress level and sleep patterns and proposes appropriate relaxation methods and exercise plans. In this way, the happiness-enhancing AI can provide a happiness-enhancing plan tailored to the individual needs of each user. Next, the happiness-enhancing AI uses VR to allow users to experience countries and regions with high happiness levels in virtual reality. For example, by experiencing the culture and daily life of Nordic countries in VR, users can learn elements of happiness enhancement. This allows the happiness-enhancing AI to learn how to improve happiness from a new perspective. Furthermore, the happiness-enhancing AI facilitates community building, enabling users to connect with each other socially and engage in collaborative activities. With AI support, the happiness of the group as a whole can also be improved. For example, users with shared hobbies can interact online and collaborate on projects. Furthermore, the happiness-enhancing AI integrates with health technology, using wearable devices to analyze physical and mental health data in real time. For instance, it collects data on exercise, diet, and sleep, and the AI ​​analyzes this data to provide appropriate improvement suggestions. This allows the happiness-enhancing AI to also improve physical well-being. Finally, the happiness-enhancing AI gamifies happiness-enhancing behaviors, providing a system that allows users to pursue happiness while enjoying their daily lives. For example, it can award points for healthy eating and exercise, allowing users to improve their happiness while having fun. In this way, the happiness-enhancing AI is an innovative service that enhances users' physical and mental health and happiness through the provision of personalized happiness-enhancing plans, VR experiences, community building, integration with health technology, and a gamified approach. Through these means, the happiness-enhancing AI can improve users' happiness.

[0067] The happiness-enhancing AI according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user data. User data includes, but is not limited to, behavioral data, health data, and emotional data. The collection unit collects heart rate and sleep data, for example, using a wearable device. The collection unit can also collect user behavioral data using a smartphone application. Furthermore, the collection unit can collect social media activity data. For example, the collection unit collects heart rate data from a wearable device in real time to monitor the user's health status. It records the user's steps and exercise level using a smartphone application and collects them as behavioral data. It analyzes the content of social media posts and the number of likes to collect user emotional data. The analysis unit analyzes the data collected by the collection unit. The analysis is performed by, but is not limited to, methods such as data mining, statistical analysis, and machine learning. For example, the analysis unit uses data mining techniques to analyze the user's behavioral patterns. The analysis unit can also analyze users' health data using statistical analysis. Furthermore, the analysis unit can analyze users' emotional data using machine learning algorithms. For example, the analysis unit uses data mining techniques to extract user behavior patterns and analyze behavioral trends. It analyzes users' health data using statistical analysis to understand changes in their health status. It analyzes users' emotional data using machine learning algorithms to predict changes in their emotions. The generation unit generates well-being plans based on the analysis results obtained by the analysis unit. These well-being plans include, but are not limited to, health improvement plans, stress reduction plans, and social connection strengthening plans. For example, the generation unit generates a health improvement plan tailored to the user based on the analysis results. The generation unit can also generate plans that include relaxation methods for stress reduction. Furthermore, the generation unit can generate plans that include activities to strengthen social connections. For example, the generation unit suggests exercise and diet plans tailored to the user based on the analysis results.To reduce stress, the system generates plans that include meditation and relaxation techniques. To strengthen social connections, it suggests participation in online community activities and offline events. The provider provides the plans generated by the generator. Delivery is done by means of, for example, notifications, email, or in-app messages. For example, the provider provides plans to users using the smartphone's notification function. The provider can also provide plans via email. The provider can also provide plans via in-app messages. For example, the provider notifies users of exercise and meal plans using the smartphone's notification function. It provides users with relaxation techniques and plans to strengthen social connections via email. It provides users with happiness-enhancing plans and encourages their implementation via in-app messages. In this way, the happiness-enhancing AI according to the embodiment can improve the user's happiness.

[0068] The data collection unit collects user data. User data includes, but is not limited to, behavioral data, health data, and emotional data. For example, the data collection unit collects heart rate and sleep data using wearable devices. Specifically, wearable devices monitor the user's biometric data such as heart rate, blood pressure, body temperature, and sleep patterns in real time and transmit this data to a cloud server. The data collection unit can also collect user behavioral data using smartphone apps. It uses the smartphone's GPS function to record the user's travel route and time spent in each location, and uses accelerometers and gyroscopes to measure exercise levels and steps. Furthermore, the data collection unit can also collect social media activity data. For example, the data collection unit analyzes the content of the user's social media posts, the number of likes, and the content of comments to understand the user's emotions and social connection status. This allows the data collection unit to centrally collect diverse user data and build a comprehensive database. Moreover, the data collection unit can update this data in real time, always maintaining the latest information. For example, data from wearable devices is updated every second, and data from smartphone apps is updated in real time according to the user's actions. This allows the data collection unit to always have up-to-date information about the user's state and provide it to the analysis and generation units.

[0069] The analysis unit analyzes the data collected by the collection unit. Analysis is performed using methods such as data mining, statistical analysis, and machine learning, but is not limited to these examples. Specifically, the analysis unit uses data mining techniques to analyze user behavior patterns. For example, it analyzes users' travel routes and time spent in different locations to identify daily behavior patterns and abnormal behavior. The analysis unit can also analyze users' health data using statistical analysis. For example, it analyzes heart rate and sleep data to detect changes or abnormalities in the user's health. Furthermore, the analysis unit can analyze users' emotional data using machine learning algorithms. For example, it analyzes social media posts and the number of likes to predict changes and trends in the user's emotions. This allows the analysis unit to analyze the collected data from multiple perspectives and gain a detailed understanding of the user's state. Additionally, the analysis unit can utilize historical data and statistical information to evaluate long-term trends and risks. For example, it can predict health risks in specific seasons or time periods based on historical health data and propose preventative measures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data early and issue warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0070] The generation unit generates a wellness improvement plan based on the analysis results obtained by the analysis unit. This wellness improvement plan may include, but is not limited to, health improvement plans, stress reduction plans, and social connection strengthening plans. Specifically, the generation unit generates a health improvement plan tailored to the user based on the analysis results. For example, it may suggest appropriate exercise and diet based on the user's heart rate and sleep data. The generation unit can also generate plans that include relaxation methods for stress reduction. For example, it may suggest meditation, deep breathing, and relaxation music based on the user's emotional data. Furthermore, the generation unit can generate plans that include activities to strengthen social connections. For example, it may suggest participation in online communities or offline events based on the user's social media activity data. This allows the generation unit to provide a customized wellness improvement plan tailored to the user's condition and needs. Additionally, the generation unit can collect user feedback, evaluate the effectiveness of the plan, and continuously improve it. For example, it can analyze the results of the plan implemented by the user, strengthening the plan if it was effective, and suggesting a new plan if it was ineffective. This allows the generation unit to provide an optimal plan for continuously improving the user's well-being.

[0071] The service provider delivers plans generated by the generation service provider. Delivery is done through methods such as notifications, emails, and in-app messages, but is not limited to these examples. Specifically, the service provider delivers plans to users using smartphone notification functions. For example, it might notify users of exercise or meal plans to encourage them to follow them. The service provider can also deliver plans via email. For example, it might send relaxation methods or social connection strengthening plans via email to provide users with detailed information. Furthermore, the service provider can deliver plans through in-app messages. For example, it might display well-being plans within the app to encourage users to follow them and monitor their progress. This allows the service provider to deliver plans to users at the appropriate time and support their implementation. Additionally, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its delivery methods. For example, based on feedback from users after they have followed a plan, it can adjust the timing and content of notifications to explore more effective delivery methods. The service provider can also reliably transmit information using multiple communication methods. For example, it can use not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the service provider to quickly and reliably provide users with actionable instructions, thereby improving their well-being.

[0072] The provider can use VR to allow users to experience countries and regions with high happiness levels in virtual reality. For example, the provider can enable users to experience the culture and daily life of Nordic countries in VR. For example, the provider can enable users to experience the natural environment of regions with high happiness levels in VR. Furthermore, the provider can enable users to learn about the social systems and education systems of countries with high happiness levels in VR. For example, the provider can enable users to learn about elements that contribute to increased happiness by experiencing the culture and daily life of Nordic countries in VR. The provider can enable users to experience the natural environment of regions with high happiness levels in VR, thereby achieving relaxation. The provider can enable users to learn about ways to improve happiness from a new perspective by learning about the social systems and education systems of countries with high happiness levels in VR. In this way, by utilizing VR, users can experience countries and regions with high happiness levels and learn about elements that contribute to increased happiness.

[0073] The service provides a platform where users can build social connections and engage in collaborative activities through community building. For example, it enables users with shared hobbies to interact online and work together on projects. For example, it allows users to participate in local events and deepen their social connections. Furthermore, it enables users to share information and engage in collaborative activities within online communities. For example, it allows users with shared hobbies to interact online and work together on projects, thereby strengthening their social connections. By enabling users to participate in local events and deepen their social connections, the service can improve the overall well-being of the group. The service allows users to share information and engage in collaborative activities within online communities, thereby strengthening their social connections. In this way, through community building, users can build social connections and engage in collaborative activities, thereby improving the overall well-being of the group.

[0074] The service provider can analyze physical and mental health data in real time using wearable devices and make appropriate improvement suggestions. For example, the service provider can propose exercise plans based on data collected from wearable devices. For example, the service provider can propose meal plans based on data collected from wearable devices. Furthermore, the service provider can propose sleep improvement plans based on data collected from wearable devices. For example, by proposing exercise plans based on data collected from wearable devices, the service provider can improve the user's physical health. By proposing meal plans based on data collected from wearable devices, the service provider can improve the user's nutritional balance. By proposing sleep improvement plans based on data collected from wearable devices, the service provider can improve the user's sleep quality. In this way, by using wearable devices, physical and mental health data can be analyzed in real time and appropriate improvement suggestions can be made.

[0075] The service provider can gamify actions that improve happiness, providing a system that allows users to pursue happiness while having fun in their daily lives. For example, the service provider can award points for eating healthy meals, allowing users to pursue health while having fun. For example, the service provider can award points for exercising, making it possible for users to continue exercising while having fun. Furthermore, the service provider can award points for actions that improve sleep, making it possible for users to improve their sleep while having fun. For example, by awarding points for eating healthy meals, the service provider can improve users' health by allowing them to pursue health while having fun. By awarding points for exercising, the service provider can improve users' physical health by making it possible for users to continue exercising while having fun. By awarding points for actions that improve sleep, the service provider can improve the quality of users' sleep by making it possible for users to improve their sleep while having fun. In this way, by gamifying actions that improve happiness, users can improve their happiness while having fun.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect data during times when the user is relaxed. For example, if the user is relaxed, the data collection unit can perform longer data collection sessions to gather detailed data. Also, if the user is busy, the data collection unit can efficiently collect the necessary data in a short amount of time. For example, if the user is stressed, the data collection unit can obtain more accurate data by collecting data during times when the user is relaxed. If the user is relaxed, the data collection unit can obtain more information by performing longer data collection sessions to gather detailed data. If the user is busy, the data collection unit can reduce the user's burden by efficiently collecting the necessary data in a short amount of time. This allows for the collection of more appropriate data by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can determine the timing of data collection based on actions the user frequently performed in the past. For example, the data collection unit can analyze the user's past behavior patterns and select the most efficient data collection method. Furthermore, the data collection unit can collect data at specific time periods based on the user's past behavior history. For example, by determining the timing of data collection based on actions the user frequently performed in the past, the data collection unit can obtain more accurate data. The data collection unit can improve the efficiency of data collection by analyzing the user's past behavior patterns and selecting the most efficient data collection method. By collecting data at specific time periods based on the user's past behavior history, the data collection unit can perform data collection that aligns with the user's daily rhythm. This allows for the selection of the optimal data collection method by analyzing the user's past behavior history.

[0078] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to areas of interest that the user is currently interested in. For example, the data collection unit can collect only the necessary data according to the user's lifestyle. The data collection unit can also adjust the scope of data collection considering the user's current lifestyle. For example, by prioritizing the collection of data related to areas of interest that the user is currently interested in, the data collection unit can obtain data that matches the user's interests. The data collection unit can improve the efficiency of data collection by collecting only the necessary data according to the user's lifestyle. The data collection unit can reduce the burden on the user by adjusting the scope of data collection considering the user's current lifestyle. This allows the collection of only the necessary data by filtering data based on the user's current lifestyle and areas of interest.

[0079] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to stress reduction. For example, if the user is relaxed, the data collection unit will prioritize collecting data related to happiness improvement. Also, if the user is excited, the data collection unit will prioritize collecting data to calm the excitement. For example, if the user is stressed, the data collection unit can obtain information to reduce the user's stress by prioritizing the collection of data related to stress reduction. If the user is relaxed, the data collection unit can obtain information to improve the user's happiness by prioritizing the collection of data related to happiness improvement. If the user is excited, the data collection unit can obtain information to stabilize the user's emotions by prioritizing the collection of data to calm the excitement. This allows for the prioritization of data collection based on the user's emotions, enabling the collection of more important data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. For example, the data collection unit can select the optimal data collection method based on the user's geographical location information. Furthermore, the data collection unit can efficiently collect necessary data by considering the user's geographical location information. For example, by prioritizing the collection of data related to the user's current location, the data collection unit can obtain data that is relevant to the user's current situation. The data collection unit can improve the efficiency of data collection by selecting the optimal data collection method based on the user's geographical location information. The data collection unit can reduce the burden on the user by efficiently collecting necessary data while considering the user's geographical location information. As a result, by considering the user's geographical location information, it is possible to prioritize the collection of highly relevant data.

[0081] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze users' social media activity and collect data related to topics of interest. For example, the data collection unit can estimate a user's current emotional state from their social media activity and collect the necessary data. The data collection unit can also select the optimal data collection method based on users' social media activity. For example, by analyzing users' social media activity and collecting data related to topics of interest, the data collection unit can obtain data tailored to the user's interests. By estimating a user's current emotional state from their social media activity and collecting the necessary data, the data collection unit can obtain data based on the user's emotions. By selecting the optimal data collection method based on users' social media activity, the data collection unit can improve the efficiency of data collection. This allows for the efficient collection of relevant data by analyzing users' social media activity.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. For example, if the user is stressed, the analysis unit can reduce the user's burden by providing simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can deepen the user's understanding by providing detailed analysis results. If the user is excited, the analysis unit can attract the user's interest by providing visually stimulating analysis results. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data. For example, the analysis unit can perform a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, by performing a detailed analysis on high-importance data, the analysis unit can obtain more accurate information. By performing a simplified analysis on low-importance data, the analysis unit can perform efficient data processing. By determining the priority of the analysis according to the importance of the data, the analysis unit can prioritize the analysis of important data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0084] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply health-related analysis algorithms to health data. For example, the analysis unit can apply social media analysis algorithms to social media data. Furthermore, the analysis unit can apply lifestyle-related analysis algorithms to lifestyle data. For example, by applying health-related analysis algorithms to health data, the analysis unit can understand changes in health status. By applying social media analysis algorithms to social media data, the analysis unit can analyze users' emotions and interests. By applying lifestyle-related analysis algorithms to lifestyle data, the analysis unit can analyze users' lifestyle patterns. This allows for more accurate analysis by applying different analysis algorithms depending on the data category.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide short, concise analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. For example, if the user is in a hurry, the analysis unit can save the user's time by providing short, concise analysis results. If the user is relaxed, the analysis unit can deepen the user's understanding by providing detailed analysis results. If the user is excited, the analysis unit can attract the user's interest by providing visually stimulating analysis results. This allows for the provision of more appropriate analysis results by adjusting the length of the analysis 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.

[0086] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can analyze the most recent data while referring to past data. Furthermore, the analysis unit can determine the priority of analysis according to the data collection period. For example, by prioritizing the analysis of the most recent data, the analysis unit can obtain the latest information. By analyzing the most recent data while referring to past data, the analysis unit can grasp changes in the data. By determining the priority of analysis according to the data collection period, the analysis unit can prioritize the analysis of important data. In this way, by determining the priority of analysis based on the data collection period, the latest data can be analyzed preferentially.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, by prioritizing the analysis of highly relevant data, the analysis unit can obtain important information early. By postponing the analysis of less relevant data, the analysis unit can perform efficient data processing. By adjusting the order of analysis according to the relevance of the data, the analysis unit can prioritize the analysis of important data. In this way, efficient analysis becomes possible by adjusting the order of analysis based on the relevance of the data.

[0088] The generation unit can estimate the user's emotions and adjust the method of generating a happiness-enhancing plan based on the estimated user emotions. For example, if the user is stressed, the generation unit can generate a plan that includes relaxation methods. For example, if the user is relaxed, the generation unit can generate a plan that includes activities related to happiness enhancement. Also, if the user is excited, the generation unit can generate a plan that includes activities to calm the excitement. For example, if the user is stressed, the generation unit can reduce the user's stress by generating a plan that includes relaxation methods. If the user is relaxed, the generation unit can improve the user's happiness by generating a plan that includes activities related to happiness enhancement. If the user is excited, the generation unit can stabilize the user's emotions by generating a plan that includes activities to calm the excitement. This allows for the provision of more appropriate plans by adjusting the method of generating a happiness-enhancing plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The generation unit can generate an optimal plan by analyzing the user's past behavioral data during the generation process. For example, the generation unit can generate an optimal happiness improvement plan based on the user's past behavioral data. For instance, the generation unit can analyze the user's past behavioral patterns and generate the most effective plan. Furthermore, the generation unit can generate customized plans by referencing the user's past behavioral data. For example, by generating an optimal happiness improvement plan based on the user's past behavioral data, the generation unit can provide a plan tailored to the user's needs. The generation unit can improve the user's happiness level by analyzing the user's past behavioral patterns and generating the most effective plan. By generating customized plans by referencing the user's past behavioral data, the generation unit can provide a plan tailored to the user's individual needs. This allows for the generation of an optimal happiness improvement plan by analyzing the user's past behavioral data.

[0090] The generation unit can customize the plan based on the user's current living situation during generation. For example, the generation unit can generate an optimal plan considering the user's current living situation. For example, the generation unit can adjust the content of the plan according to the user's current living situation. Furthermore, the generation unit can generate a customized plan based on the user's current living situation. For example, by generating an optimal plan considering the user's current living situation, the generation unit can provide a plan that meets the user's needs. By adjusting the content of the plan according to the user's current living situation, the generation unit can improve the user's well-being. By generating a customized plan based on the user's current living situation, the generation unit can provide a plan that meets the user's individual needs. In this way, by customizing the plan based on the user's current living situation, a more appropriate plan can be provided.

[0091] The generation unit can estimate the user's emotions and determine the priority of the plans to generate based on the estimated user emotions. For example, if the user is stressed, the generation unit will prioritize generating plans related to stress reduction. For example, if the user is relaxed, the generation unit can prioritize generating plans related to happiness improvement. Also, if the user is excited, the generation unit can prioritize generating plans to calm the excitement. For example, if the user is stressed, the generation unit can provide information to reduce the user's stress by prioritizing the generation of plans related to stress reduction. If the user is relaxed, the generation unit can provide information to improve the user's happiness by prioritizing the generation of plans related to happiness improvement. If the user is excited, the generation unit can provide information to stabilize the user's emotions by prioritizing the generation of plans to calm the excitement. This allows the system to prioritize the provision of more important plans by determining the priority of the plans to generate based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0092] The generation unit can generate an optimal plan by considering the user's geographical location information during generation. For example, the generation unit can generate a plan related to the user's current location. For example, the generation unit can generate an optimal plan based on the user's geographical location information. Furthermore, the generation unit can efficiently generate the necessary plan by considering the user's geographical location information. For example, by generating a plan related to the user's current location, the generation unit can provide a plan that suits the user's current situation. By generating an optimal plan based on the user's geographical location information, the generation unit can provide a plan that meets the user's needs. By efficiently generating the necessary plan while considering the user's geographical location information, the generation unit can reduce the burden on the user. As a result, by considering the user's geographical location information, it can provide an optimal plan.

[0093] The generation unit can generate plans by analyzing the user's social media activity during the generation process. For example, the generation unit can analyze the user's social media activity and generate plans related to topics of interest. For example, the generation unit can estimate the user's current emotional state from their social media activity and generate the necessary plan. Furthermore, the generation unit can generate the optimal plan based on the user's social media activity. For example, by analyzing the user's social media activity and generating plans related to topics of interest, the generation unit can provide plans tailored to the user's interests. By estimating the user's current emotional state from their social media activity and generating the necessary plan, the generation unit can provide plans based on the user's emotions. By generating the optimal plan based on the user's social media activity, the generation unit can provide plans tailored to the user's needs. In this way, by analyzing the user's social media activity, it is possible to provide more appropriate plans.

[0094] The delivery unit can estimate the user's emotions and adjust the way the plan is delivered based on the estimated emotions. For example, if the user is stressed, the delivery unit can provide a simple and visually clear delivery method. For example, if the user is relaxed, the delivery unit can provide a delivery method that includes detailed information. Also, if the user is excited, the delivery unit can provide a visually stimulating delivery method. For example, if the user is stressed, the delivery unit can reduce the user's burden by providing a simple and visually clear delivery method. If the user is relaxed, the delivery unit can deepen the user's understanding by providing a delivery method that includes detailed information. If the user is excited, the delivery unit can attract the user's interest by providing a visually stimulating delivery method. This allows for the delivery of a more appropriate delivery method by adjusting the way the plan 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.

[0095] The service provider can select the optimal delivery method by referring to past user feedback at the time of delivery. For example, the service provider can select the optimal delivery method based on past user feedback. For example, the service provider can identify areas for improvement in the delivery method from past user feedback and optimize it. Furthermore, the service provider can select a customized delivery method by referring to past user feedback. For example, by selecting the optimal delivery method based on past user feedback, the service provider can provide a delivery method that meets the user's needs. By identifying areas for improvement in the delivery method from past user feedback and optimizing it, the service provider can improve user satisfaction. By selecting a customized delivery method by referring to past user feedback, the service provider can provide a delivery method that meets the individual needs of the user. In this way, the service provider can select the optimal delivery method by referring to past user feedback.

[0096] The service provider can customize the plan based on the user's current living situation at the time of delivery. For example, the service provider can provide the optimal plan considering the user's current living situation. For example, the service provider can adjust the content of the plan according to the user's current living situation. Furthermore, the service provider can provide a customized plan based on the user's current living situation. For example, by providing the optimal plan considering the user's current living situation, the service provider can provide a plan that meets the user's needs. By adjusting the content of the plan according to the user's current living situation, the service provider can improve the user's well-being. By providing a customized plan based on the user's current living situation, the service provider can provide a plan that meets the user's individual needs. In this way, by customizing the plan based on the user's current living situation, a more appropriate plan can be provided.

[0097] The service provider can estimate the user's emotions and adjust the order in which plans are provided based on the estimated emotions. For example, if the user is feeling stressed, the service provider can prioritize providing plans related to stress reduction. For example, if the user is relaxed, the service provider can prioritize providing plans related to happiness improvement. Also, if the user is excited, the service provider can prioritize providing plans to calm the excitement. For example, if the user is feeling stressed, the service provider can provide information to reduce the user's stress by prioritizing plans related to stress reduction. If the user is relaxed, the service provider can provide information to improve the user's happiness by prioritizing plans related to happiness improvement. If the user is excited, the service provider can provide information to stabilize the user's emotions by prioritizing plans to calm the excitement. This allows the service provider to prioritize providing more important plans by adjusting the order in which plans are provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0098] The service provider can select the optimal service delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a service delivery method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a service delivery method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible service delivery method. For example, if the user is using a smartphone, the service provider can improve user convenience by providing a service delivery method that matches the screen size. If the user is using a tablet, the service provider can improve user visibility by providing a service delivery method optimized for a larger screen. If the user is using a smartwatch, the service provider can improve user convenience by providing a concise and highly visible service delivery method. This allows the service provider to select the optimal service delivery method by considering the user's device information.

[0099] The service provider can analyze the user's social media activity and provide a plan at the time of delivery. For example, the service provider can analyze the user's social media activity and provide a plan related to topics of interest. For example, the service provider can estimate the user's current emotional state from their social media activity and provide a necessary plan. Furthermore, the service provider can provide the optimal plan based on the user's social media activity. For example, by analyzing the user's social media activity and providing a plan related to topics of interest, the service provider can provide a plan that matches the user's interests. By estimating the user's current emotional state from their social media activity and providing a necessary plan, the service provider can provide a plan based on the user's emotions. By providing the optimal plan based on the user's social media activity, the service provider can provide a plan that meets the user's needs. In this way, by analyzing the user's social media activity, a more appropriate plan can be provided.

[0100] The provider can use VR to allow users to experience countries and regions with high happiness levels in virtual reality. For example, the provider can enable users to experience the culture and daily life of Nordic countries in VR. For example, the provider can enable users to experience the natural environment of regions with high happiness levels in VR. Furthermore, the provider can enable users to learn about the social systems and education systems of countries with high happiness levels in VR. For example, the provider can enable users to learn about elements that contribute to increased happiness by experiencing the culture and daily life of Nordic countries in VR. The provider can enable users to experience the natural environment of regions with high happiness levels in VR, thereby achieving relaxation. The provider can enable users to learn about ways to improve happiness from a new perspective by learning about the social systems and education systems of countries with high happiness levels in VR. In this way, by utilizing VR, users can experience countries and regions with high happiness levels and learn about elements that contribute to increased happiness.

[0101] The service provides a platform where users can build social connections and engage in collaborative activities through community building. For example, it enables users with shared hobbies to interact online and work together on projects. For example, it allows users to participate in local events and deepen their social connections. Furthermore, it enables users to share information and engage in collaborative activities within online communities. For example, it allows users with shared hobbies to interact online and work together on projects, thereby strengthening their social connections. By enabling users to participate in local events and deepen their social connections, the service can improve the overall well-being of the group. The service allows users to share information and engage in collaborative activities within online communities, thereby strengthening their social connections. In this way, through community building, users can build social connections and engage in collaborative activities, thereby improving the overall well-being of the group.

[0102] The service provider can analyze physical and mental health data in real time using wearable devices and make appropriate improvement suggestions. For example, the service provider can propose exercise plans based on data collected from wearable devices. For example, the service provider can propose meal plans based on data collected from wearable devices. Furthermore, the service provider can propose sleep improvement plans based on data collected from wearable devices. For example, by proposing exercise plans based on data collected from wearable devices, the service provider can improve the user's physical health. By proposing meal plans based on data collected from wearable devices, the service provider can improve the user's nutritional balance. By proposing sleep improvement plans based on data collected from wearable devices, the service provider can improve the user's sleep quality. In this way, by using wearable devices, physical and mental health data can be analyzed in real time and appropriate improvement suggestions can be made.

[0103] The service provider can gamify actions that improve happiness, providing a system that allows users to pursue happiness while having fun in their daily lives. For example, the service provider can award points for eating healthy meals, allowing users to pursue health while having fun. For example, the service provider can award points for exercising, making it possible for users to continue exercising while having fun. Furthermore, the service provider can award points for actions that improve sleep, making it possible for users to improve their sleep while having fun. For example, by awarding points for eating healthy meals, the service provider can improve users' health by allowing them to pursue health while having fun. By awarding points for exercising, the service provider can improve users' physical health by making it possible for users to continue exercising while having fun. By awarding points for actions that improve sleep, the service provider can improve the quality of users' sleep by making it possible for users to improve their sleep while having fun. In this way, by gamifying actions that improve happiness, users can improve their happiness while having fun.

[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] The happiness enhancement AI can estimate the user's emotions and adjust how the happiness enhancement plan is delivered based on those estimates. For example, if the user is stressed, it can choose a simple and highly visual delivery method. If the user is relaxed, it can choose a delivery method that includes detailed information. Furthermore, if the user is excited, it can choose a visually stimulating delivery method. By selecting the optimal delivery method according to the user's emotions, it can reduce the user's burden and maximize the effectiveness of the happiness enhancement plan.

[0106] The happiness enhancement AI can take into account the user's geographical location and provide happiness enhancement plans tailored to the characteristics of each region. For example, it can suggest relaxation methods that incorporate the natural environment to users living in urban areas. It can also suggest strengthening social connections through community activities to users living in rural areas. Furthermore, it can provide happiness enhancement plans based on the culture of the region to users living in different cultural areas. This allows for the provision of more appropriate happiness enhancement plans based on the user's geographical location.

[0107] The happiness enhancement AI can analyze a user's past behavioral data and customize happiness enhancement plans based on their behavioral patterns. For example, a user who has consistently exercised in the past can be offered a plan that incorporates exercise. Similarly, a user who has been prone to stress in the past can be offered a plan that includes relaxation methods to reduce stress. Furthermore, a user who has valued social connections in the past can be offered a plan that includes community activities. This allows the AI ​​to provide more effective happiness enhancement plans based on the user's past behavioral data.

[0108] The happiness-enhancing AI can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, data can be collected during times when they are relaxed. Conversely, if the user is relaxed, data can be collected for longer periods to gather more detailed information. Furthermore, if the user is busy, the system can efficiently collect the necessary data in a shorter time. By adjusting the timing of data collection based on the user's emotions, more relevant data can be collected.

[0109] The happiness enhancement AI can analyze a user's social media activity and provide happiness enhancement plans related to topics of interest. For example, if a user frequently posts about health on social media, it can suggest a health improvement plan. If a user posts about stress, it can suggest a plan that includes relaxation methods to reduce stress. Furthermore, if a user posts about social connections, it can suggest a plan that includes community activities. This allows for the provision of more appropriate happiness enhancement plans based on the user's social media activity.

[0110] The happiness-enhancing AI can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is excited, it can provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, it can provide more appropriate analysis results.

[0111] The happiness enhancement AI can customize happiness enhancement plans based on the user's current lifestyle. For example, if a user is busy with work, it can suggest effective relaxation methods that can be done in a short amount of time. If a user values ​​time at home, it can suggest plans that include activities that can be enjoyed with family. Furthermore, if a user is interested in health, it can suggest health improvement plans. This allows the AI ​​to provide more appropriate happiness enhancement plans based on the user's current lifestyle.

[0112] The happiness enhancement AI can estimate the user's emotions and prioritize happiness enhancement plans based on those emotions. For example, if the user is stressed, it can prioritize plans related to stress reduction. If the user is relaxed, it can prioritize plans related to happiness enhancement. Furthermore, if the user is agitated, it can prioritize plans to calm the agitation. By prioritizing happiness enhancement plans based on the user's emotions, it can deliver more important plans first.

[0113] The happiness-enhancing AI can select the optimal delivery method by considering the user's device information. For example, if the user is using a smartphone, it can provide a delivery method adapted to the screen size. If the user is using a tablet, it can provide a delivery method optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible delivery method. In this way, the AI ​​can select the optimal delivery method based on the user's device information.

[0114] The happiness enhancement AI can estimate the user's emotions and adjust how it generates happiness enhancement plans based on those emotions. For example, if the user is stressed, it can generate a plan that includes relaxation methods. If the user is relaxed, it can generate a plan that includes activities related to happiness enhancement. Furthermore, if the user is agitated, it can generate a plan that includes activities to calm the agitation. By adjusting how happiness enhancement plans are generated based on the user's emotions, it can provide more appropriate plans.

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

[0116] Step 1: The data collection unit collects user data. User data includes behavioral data, health data, emotional data, etc. For example, the data collection unit collects heart rate and sleep data using wearable devices and user behavioral data using smartphone apps. It can also collect social media activity data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning. For example, the analysis unit uses data mining techniques to analyze user behavior patterns, statistical analysis to analyze health data, and machine learning algorithms to analyze emotional data. Step 3: The generation unit generates a wellness improvement plan based on the analysis results obtained by the analysis unit. The wellness improvement plan includes health improvement plans, stress reduction plans, and social connection strengthening plans. For example, based on the analysis results, the generation unit suggests exercise plans, meal plans, relaxation methods, and online community activities that are suitable for the user. Step 4: The provider delivers the plans generated by the generator. Delivery is done via methods such as notifications, email, and in-app messages. For example, the provider might use smartphone notifications to send exercise and meal plans, provide relaxation methods and social connection strengthening plans via email, and provide wellness improvement plans via in-app messages.

[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 collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user data using the wearable device or smartphone application of the smart device 14. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The generation unit generates a wellness improvement plan based on the analysis results, and the provision unit provides the plan through the notification function or in-app messages of the smart device 14. The correspondence between each unit 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 collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user data using the camera and microphone of the smart glasses 214. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The generation unit generates a wellness improvement plan based on the analysis results, and the provision unit provides the plan through the notification function of the smart glasses 214 or through in-app messages. The correspondence between each unit 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 collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user data using the camera and microphone of the headset terminal 314. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The generation unit generates a wellness improvement plan based on the analysis results, and the provision unit provides the plan through the notification function or in-app messages of the headset terminal 314. The correspondence between each unit 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 collection unit, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user data using the camera and microphone of the robot 414. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The generation unit generates a happiness improvement plan based on the analysis results, and the provision unit provides the plan through the robot 414's notification function or in-app messages. The correspondence between each unit 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 data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a happiness improvement plan based on the analysis results obtained by the analysis unit, The system comprises a providing unit that provides the plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Using VR, we will allow people to experience countries and regions with high levels of happiness through virtual reality. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Through community building, users can connect with each other socially and engage in collaborative activities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We use wearable devices to analyze physical and mental health data in real time and provide appropriate improvement suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We gamify actions that improve happiness, providing a system that allows people to pursue happiness while enjoying their daily lives. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the method of generating happiness improvement plans based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the system analyzes the user's past behavioral data to generate the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the plan is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and determines the priority of the plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the system considers the user's geographical location to generate the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the user's social media activity is analyzed to generate the plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the plan is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, we will refer to past user feedback to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the plan will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order in which plans are offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide a plan accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, Using VR, we will allow people to experience countries and regions with high levels of happiness through virtual reality. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, Through community building, users can connect with each other socially and engage in collaborative activities. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, We use wearable devices to analyze physical and mental health data in real time and provide appropriate improvement suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, We gamify actions that improve happiness, providing a system that allows people to pursue happiness while enjoying their daily lives. 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 data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a happiness improvement plan based on the analysis results obtained by the analysis unit, The system comprises a providing unit that provides the plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned supply unit is, Using VR, we will allow people to experience countries and regions with high levels of happiness through virtual reality. The system according to feature 1.

3. The aforementioned supply unit is, Through community building, users can connect with each other socially and engage in collaborative activities. The system according to feature 1.

4. The aforementioned supply unit is, We use wearable devices to analyze physical and mental health data in real time and provide appropriate improvement suggestions. The system according to feature 1.

5. The aforementioned supply unit is, We gamify actions that improve happiness, providing a system that allows people to pursue happiness while enjoying their daily lives. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

Images