system

Abstract

The system according to this embodiment aims to measure the user's level of happiness and, based on that, propose an individualized happiness enhancement plan and solutions to social problems. [Solution] The system according to the embodiment comprises an analysis unit, a measurement unit, a proposal unit, a promotion unit, a recording unit, and a utilization unit. The analysis unit analyzes the user's daily behavior and SNS posts. The measurement unit measures happiness levels based on the analysis results obtained by the analysis unit. The proposal unit proposes individual happiness enhancement plans and solutions to social issues based on the measurement results obtained by the measurement unit. The promotion unit promotes mental support and behavioral change based on the plans proposed by the proposal unit. The recording unit records the happiness level evaluation obtained by the measurement unit on a blockchain. The utilization unit utilizes the happiness points recorded by the recording unit as local currency.

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 performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; 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, there is a problem that it is difficult to accurately measure the happiness of a user and propose an individual happiness enhancement plan based on it.

[0005] The system according to the embodiment aims to measure the happiness of a user and propose an individual happiness enhancement plan and a solution to social problems based on it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a measurement unit, a proposal unit, a promotion unit, a recording unit, and a utilization unit. The analysis unit analyzes the user's daily behavior and SNS posts. The measurement unit measures happiness levels based on the analysis results obtained by the analysis unit. The proposal unit proposes individual happiness enhancement plans and solutions to social issues based on the measurement results obtained by the measurement unit. The promotion unit promotes mental support and behavioral change based on the plans proposed by the proposal unit. The recording unit records the happiness level evaluation obtained by the measurement unit on a blockchain. The utilization unit utilizes the happiness points recorded by the recording unit as a local currency. [Effects of the Invention]

[0007] The system according to this embodiment can measure the user's level of happiness and, based on that, propose an individualized happiness enhancement plan and solutions to social problems. [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 I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 device 32. The processor 28, the RAM 30, and the storage device 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 measurement system according to an embodiment of the present invention is a system in which a personalized AI agent analyzes a user's daily behavior and social media posts to measure their happiness level. This happiness measurement system uses multimodal AI technology to analyze the user's behavior, social media posts, voice, facial expressions, etc., and evaluates the happiness level of the individual and society from multiple perspectives. Next, the happiness measurement system proposes an individualized happiness enhancement plan and solutions to social issues. As a result, the user receives mental support and is encouraged to change their behavior. Furthermore, the happiness measurement system provides guidance on social contribution activities through natural dialogue with the user. The happiness level evaluation is recorded on the blockchain, and the acquired "happiness points" are used as local currency. This system directly links economic activity and happiness improvement, realizing a new socioeconomic system. For example, the happiness measurement system analyzes the user's daily behavior and social media posts. Specifically, it uses multimodal AI technology to analyze the user's behavior, social media posts, voice, facial expressions, etc., and evaluates the happiness level of the individual and society from multiple perspectives. Next, the happiness measurement system proposes an individualized happiness enhancement plan and solutions to social issues. This allows users to receive emotional support and promote behavioral change. Furthermore, the happiness measurement system provides guidance on social contribution activities through natural dialogue with users. Happiness scores are recorded on the blockchain, and the earned "happiness points" are used as local currency. This system directly links economic activity with happiness improvement, realizing a new socioeconomic system. As a result, the happiness measurement system can comprehensively evaluate users' happiness and propose individualized happiness enhancement plans and solutions to social issues.

[0029] The happiness measurement system according to this embodiment comprises an analysis unit, a measurement unit, a proposal unit, a promotion unit, a recording unit, and an utilization unit. The analysis unit analyzes the user's daily behavior and SNS posts. For example, the analysis unit records the user's daily behavior and collects SNS posts. The analysis unit uses multimodal AI technology to analyze the user's behavior, SNS posts, voice, facial expressions, etc. For example, the analysis unit analyzes the user's SNS posts using natural language processing technology to extract emotions and topics. The analysis unit can also analyze the user's voice data using speech recognition technology to extract emotions and content. The analysis unit can also analyze the user's facial expression data using image recognition technology to extract emotions and changes in facial expressions. The measurement unit measures happiness based on the analysis results obtained by the analysis unit. For example, the measurement unit scores happiness based on the user's behavior data and SNS post data. As a criterion for measuring happiness, the measurement unit considers, for example, the frequency and intensity of positive emotions. The measurement unit can also analyze the user's behavior data and track changes in happiness over time. The Proposal Department proposes individual well-being enhancement plans and solutions to social issues based on the measurement results obtained by the Measurement Department. For example, the Proposal Department proposes specific action plans to improve the user's well-being. The Proposal Department can also propose social contribution activities and volunteer activities based on the user's interests. The Proposal Department can also provide counseling and mental health care resources to improve the user's well-being. The Promotion Department promotes mental support and behavioral change based on the plans proposed by the Proposal Department. For example, the Promotion Department supports the implementation of the proposed plans through natural dialogue with the user. The Promotion Department can also provide feedback and advice to promote behavioral change in the user. The Promotion Department can also provide counseling and mental health care resources to provide mental support to the user. The Recording Department records the well-being evaluations obtained by the Measurement Department on the blockchain. For example, the Recording Department ensures the reliability and transparency of the evaluations by recording the well-being evaluations on the blockchain. The Recording Department can also encrypt and record the well-being evaluation data. The Recording Department can also prevent data tampering using blockchain's distributed ledger technology.The utilization unit will use the happiness points recorded by the recording unit as local currency. The utilization unit will, for example, provide places and services where happiness points can be used as local currency. The utilization unit can also provide a platform for exchanging happiness points for local currency. The utilization unit can also provide guidelines and rules for using happiness points as local currency. As a result, the happiness measurement system according to this embodiment can analyze users' daily behavior and SNS posts, measure their happiness level, propose individual happiness enhancement plans and solutions to social issues, promote mental support and behavioral change, record happiness evaluations on the blockchain, and utilize the acquired happiness points as local currency.

[0030] The analytics department analyzes users' daily behavior and social media posts. Specifically, it records users' daily activities and collects social media posts. This includes collecting data from users' smartphones and wearable devices. For example, it collects data such as smartphone location information and activity logs, and heart rate and step count from wearable devices. This data is used to understand users' behavioral patterns and health status. Furthermore, collecting social media posts includes acquiring data from multiple social media platforms used by the user. This includes text posts, image posts, and video posts, and this data is analyzed using natural language processing and image recognition technologies. By using multimodal AI technology, it is possible to comprehensively analyze user behavior, social media posts, voice, facial expressions, etc. For example, natural language processing technology is used to analyze users' social media posts and extract emotions and topics. This makes it possible to understand what emotions the user is feeling and what topics they are interested in. For voice data analysis, speech recognition technology is used to extract the content and emotions of the user's statements. This makes it possible to understand what emotions the user is feeling and what they are talking about. The facial expression data analysis uses image recognition technology to extract changes in the user's facial expressions and emotions. This allows us to understand what emotions the user is feeling through changes in their facial expressions. By comprehensively analyzing this data, we can evaluate the user's level of happiness with high accuracy.

[0031] The measurement unit measures happiness levels based on the analysis results obtained by the analysis unit. Specifically, it scores happiness levels based on user behavior data and social media posting data. The frequency and intensity of positive emotions are considered as criteria for measuring happiness levels. For example, happiness levels are evaluated based on the frequency of positive words in users' social media posts and the results of sentiment analysis of the content of those posts. It can also analyze user behavior data and track changes in happiness levels over time. This allows for real-time understanding of fluctuations in users' happiness levels. Furthermore, the measurement unit analyzes users' voice data and facial expression data to evaluate changes in emotions. This allows for understanding under what circumstances users' happiness levels increase and under what circumstances they decrease. The measurement unit comprehensively analyzes this data to measure users' happiness levels with high accuracy.

[0032] The proposal department proposes individual well-being plans and solutions to social issues based on the measurement results obtained by the measurement department. Specifically, it proposes concrete action plans to improve the user's well-being. For example, it can suggest hobbies and leisure activities based on the user's interests. It can also suggest social contribution activities and volunteer activities to improve the user's well-being. This allows users to improve their own well-being while simultaneously contributing to society. Furthermore, the proposal department can also provide counseling and mental health care resources to improve the user's well-being. This includes counseling by professionals and introductions to apps and services for mental health care. The proposal department provides a variety of resources to improve the user's well-being and supports users in taking concrete actions to improve their own well-being.

[0033] The Facilitation Department promotes mental support and behavioral change based on the plan proposed by the Proposal Department. Specifically, it supports the implementation of the proposed plan through natural dialogue with the user. For example, it provides feedback and advice when the user implements the proposed behavior plan. This allows the user to review and improve their behavior. Furthermore, the Facilitation Department can also provide counseling and mental health care resources to provide the user's mental support. This includes professional counseling and introductions to apps and services for mental health care. The Facilitation Department provides specific support to help the user implement the proposed plan and improve their well-being.

[0034] The recording unit records the happiness level evaluation obtained by the measurement unit on the blockchain. Specifically, recording the happiness level evaluation on the blockchain ensures the reliability and transparency of the evaluation. By using blockchain technology, data tampering is prevented, and highly reliable data management is achieved. The recording unit can also record the happiness level evaluation data in encryption. This ensures data reliability while protecting user privacy. Furthermore, the recording unit can also prevent data tampering using blockchain's distributed ledger technology. As a result, happiness level evaluation data is managed in a safe and reliable manner.

[0035] The utilization unit will use the happiness points recorded by the recording unit as local currency. Specifically, it will provide places and services where happiness points can be used as local currency. For example, it will partner with local shops and service providers to enable the use of happiness points as local currency. This will allow users to contribute to the local economy by taking actions that improve their own happiness. Furthermore, the utilization unit can also provide a platform for exchanging happiness points for local currency. This includes providing procedures for exchanging happiness points for local currency through online platforms or apps. The utilization unit can also provide guidelines and rules for using happiness points as local currency. This will allow users to use happiness points as local currency with peace of mind. By utilizing happiness points as local currency, the utilization unit can simultaneously improve users' happiness and revitalize the local economy.

[0036] The analysis unit can analyze a user's daily activities, social media posts, voice, facial expressions, etc., using multimodal AI technology. For example, the analysis unit records a user's daily activities and collects social media posts. The analysis unit analyzes the user's activities, social media posts, voice, facial expressions, etc., using multimodal AI technology. For example, the analysis unit analyzes a user's social media posts using natural language processing technology to extract emotions and topics. The analysis unit can also analyze a user's voice data using speech recognition technology to extract emotions and content. The analysis unit can also analyze a user's facial expression data using image recognition technology to extract emotions and changes in facial expressions. In this way, by using multimodal AI technology, a user's daily activities, social media posts, voice, facial expressions, etc., can be analyzed from multiple perspectives. Multimodal AI technology includes, but is not limited to, speech recognition, image analysis, and text analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis department can input users' social media posts into a generation AI and have the AI ​​extract sentiments and topics.

[0037] The proposal unit can propose individual well-being plans and solutions to social problems. For example, the proposal unit can propose specific action plans to improve the user's well-being. The proposal unit can also propose social contribution activities and volunteer activities based on the user's interests and concerns. The proposal unit can also provide counseling and mental health care resources to improve the user's well-being. In this way, the user's well-being can be improved by proposing individual well-being plans and solutions to social problems. Individual well-being plans include, but are not limited to, counseling and behavioral guidance. Solutions to social problems include, but are not limited to, environmental problems and poverty problems. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input an action plan to improve the user's well-being into a generative AI and have the generative AI execute specific proposals.

[0038] The facilitator can facilitate emotional support and behavioral change through natural dialogue with the user. For example, the facilitator can support the implementation of a proposed plan through natural dialogue with the user. The facilitator can also provide feedback and advice to facilitate the user's behavioral change. The facilitator can also provide counseling and mental health care resources to provide emotional support to the user. This allows for the facilitator to facilitate emotional support and behavioral change through natural dialogue with the user. Natural dialogue includes, but is not limited to, dialogue scenarios and the dialogue engine used. Some or all of the processing described above in the facilitator may be performed, for example, using generative AI or not using generative AI. For example, the facilitator can input the dialogue with the user into a generative AI and have the generative AI execute the dialogue scenario.

[0039] The recording unit can record happiness scores on a blockchain. The recording unit ensures the reliability and transparency of the scores by recording them on a blockchain, for example. The recording unit can also encrypt and record the happiness score data. The recording unit can also prevent data tampering by using blockchain's distributed ledger technology. This ensures the reliability and transparency of the scores by recording them on a blockchain. The blockchain includes, but is not limited to, the blockchain platform used and the method of recording the data. Some or all of the above processing in the recording unit may be performed, for example, using a generative AI, or not using a generative AI. For example, the recording unit can input happiness score data into a generative AI and have the generative AI perform data encryption and recording.

[0040] The utilization unit can use the acquired happiness points as local currency. The utilization unit can, for example, provide places and services where happiness points can be used as local currency. The utilization unit can also provide a platform for exchanging happiness points for local currency. The utilization unit can also provide guidelines and rules for using happiness points as local currency. This allows for a direct link between economic activity and increased happiness by utilizing acquired happiness points as local currency. Happiness points include, but are not limited to, criteria for awarding points for actions and the value of points. Local currency includes, but are not limited to, places where it can be used and exchange rates. Some or all of the above processing in the utilization unit may be performed, for example, using a generative AI, or not using a generative AI. For example, the utilization unit can input happiness points into a generative AI and have the generative AI execute methods for using them as local currency.

[0041] The analysis unit can analyze a user's past behavioral history and select the optimal analysis algorithm. For example, the analysis unit can analyze a user's past behavioral patterns and select the most effective analysis algorithm. The analysis unit can also analyze the content of a user's past social media posts and select an appropriate analysis algorithm. The analysis unit can also select an analysis algorithm that is appropriate for a specific time period or situation based on the user's past behavioral history. In this way, the optimal analysis algorithm can be selected by analyzing the user's past behavioral history. Past behavioral history includes, but is not limited to, behavioral logs and historical data. The optimal analysis algorithm includes, but is not limited to, algorithm performance evaluation and application conditions. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's past behavioral data into a generative AI and have the generative AI select the optimal analysis algorithm.

[0042] The analysis unit can filter data based on the user's current lifestyle and areas of interest during analysis. For example, the analysis unit can prioritize the analysis of posts and actions related to topics the user is currently interested in. The analysis unit can also filter relevant data based on the user's current lifestyle (e.g., work, family, health). The analysis unit can also filter posts and actions containing specific keywords based on the user's current areas of interest. This allows for the analysis of more relevant data by filtering based on the user's current lifestyle and areas of interest. Current lifestyle includes, but is not limited to, survey results and activity logs. Areas of interest include, but is not limited to, social media following information and search history. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input data on the user's current lifestyle and areas of interest into a generative AI and have the generative AI perform the filtering.

[0043] The analysis unit can prioritize the analysis of highly relevant data by considering the user's geographical location during analysis. For example, the analysis unit can prioritize the analysis of posts and actions related to the user's current location. The analysis unit can also prioritize the analysis of data related to local trends and events based on the user's geographical location. The analysis unit can also prioritize the analysis of data related to a specific region by considering the user's geographical location. This allows for the prioritization of highly relevant data by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Highly relevant data includes, but is not limited to, the user's behavioral history and areas of interest. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform a priority analysis of highly relevant data.

[0044] The analysis unit can analyze a user's social media activity and obtain relevant data during the analysis process. For example, the analysis unit can obtain and analyze data from social media platforms where the user frequently posts. The analysis unit can also analyze patterns in a user's social media activity and obtain relevant data. The analysis unit can also analyze a user's social media interactions (e.g., comments, likes) and obtain relevant data. In this way, relevant data can be obtained by analyzing a user's social media activity. Social media activity includes, but is not limited to, posts, follow information, and like history. Relevant data includes, but is not limited to, a user's areas of interest and behavioral history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input user social media activity data into a generative AI and have the generative AI perform the acquisition of relevant data.

[0045] The measurement unit can optimize the measurement algorithm by referring to the user's past happiness data during measurement. For example, the measurement unit can analyze the user's past happiness data and select the optimal measurement algorithm. The measurement unit can also select a measurement algorithm that is appropriate for a specific time of day or situation based on the user's past happiness data. The measurement unit can also optimize the measurement algorithm by referring to the user's past happiness data. Past happiness data includes, but is not limited to, survey results and behavioral logs. Optimization of the measurement algorithm includes, but is not limited to, performance evaluation of the algorithm and application conditions. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the measurement unit can input the user's past happiness data into a generative AI and have the generative AI perform the optimization of the measurement algorithm.

[0046] The measurement unit can improve the accuracy of measurements based on the user's lifestyle and behavioral patterns during measurement. For example, the measurement unit can improve the accuracy of measurements based on the user's current lifestyle (e.g., work, family, health). The measurement unit can also improve the accuracy of measurements by analyzing the user's behavioral patterns. The measurement unit can also improve the accuracy of measurements based on the user's lifestyle and behavioral patterns. This allows for improved accuracy of measurements based on the user's lifestyle and behavioral patterns. Lifestyle includes, but is not limited to, survey data and behavioral logs. Behavioral patterns include, but are not limited to, behavioral logs and historical data. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or without a generative AI. For example, the measurement unit can input data on the user's lifestyle and behavioral patterns into a generative AI and have the generative AI perform the measurement accuracy improvement.

[0047] The measurement unit can measure happiness levels while considering the user's geographical location information. For example, the measurement unit can measure happiness levels related to the user's current location. The measurement unit can also measure happiness levels related to local trends and events based on the user's geographical location information. The measurement unit can also measure happiness levels related to a specific region, taking into account the user's geographical location information. This allows for a more accurate measurement of happiness levels by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Measurement of happiness levels includes, but is not limited to, surveys and analysis of behavioral data. Some or all of the above-described processes in the measurement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the measurement unit can input the user's geographical location information into a generative AI and have the generative AI perform the measurement of happiness levels.

[0048] The measurement unit can improve the accuracy of measurements by referring to the user's relevant literature during measurement. For example, the measurement unit can improve the accuracy of measurements by referring to the user's relevant literature. The measurement unit can also improve the accuracy of measurements by emphasizing specific indicators from the user's relevant literature. The measurement unit can also improve the accuracy of measurements by referring to the user's relevant literature. This allows for improvement of measurement accuracy by referring to the user's relevant literature. Relevant literature includes, but is not limited to, academic papers and technical reports. Improving measurement accuracy includes, but is not limited to, algorithm performance evaluation and data accuracy improvement. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the measurement unit can input the user's relevant literature into a generative AI and have the generative AI perform the measurement accuracy improvement.

[0049] The proposal unit can adjust the level of detail of its proposals based on the importance of happiness. For example, if happiness is highly important, the proposal unit will provide detailed proposals. If happiness is moderately important, the proposal unit may provide proposals with a moderate level of detail. If happiness is not highly important, the proposal unit may provide concise proposals. By adjusting the level of detail of proposals based on the importance of happiness, more effective proposals can be made. The importance of happiness includes, but is not limited to, survey results and analysis of behavioral data. The adjustment of the level of detail of proposals includes, but is not limited to, the depth of the proposal content and the specificity of the information. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input happiness importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the proposals.

[0050] The proposal unit can apply different proposal algorithms depending on the happiness category when making a proposal. For example, if the happiness category is "health," the proposal unit will apply a proposal algorithm related to health. If the happiness category is "relationships," the proposal unit can also apply a proposal algorithm related to relationships. If the happiness category is "work," the proposal unit can also apply a proposal algorithm related to work. By applying different proposal algorithms depending on the happiness category, more appropriate proposals can be made. Happiness categories include, but are not limited to, health, economy, and social relationships. Proposal algorithms include, but are not limited to, recommendation systems and machine learning algorithms. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or without using generative AI. For example, the proposal unit can input happiness category data into a generative AI and have the generative AI perform the application of the proposal algorithm.

[0051] The proposal unit can determine the priority of proposals based on the timing of happiness measurement. For example, if the happiness measurement was recent, the proposal unit will prioritize the proposal. If the happiness measurement was in the past, the proposal unit may also make a proposal with a moderate priority. If the happiness measurement was in the distant past, the proposal unit may also make a proposal with a low priority. This allows for more effective proposals by determining the priority of proposals based on the timing of happiness measurement. The timing of happiness measurement includes, but is not limited to, survey results and analysis of behavioral data. The determination of proposal priority includes, but is not limited to, importance evaluation criteria and prioritization algorithms. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input happiness measurement timing data into a generative AI and have the generative AI determine the priority of proposals.

[0052] The proposal unit can adjust the order of proposals based on their correlation with happiness levels. For example, the proposal unit can prioritize proposals where the correlation with happiness levels is high. If the correlation with happiness levels is moderate, the proposal unit can also make proposals in a moderate order. If the correlation with happiness levels is low, the proposal unit can also make proposals in an order of decreasing correlation. By adjusting the order of proposals based on the correlation with happiness levels, more appropriate proposals can be made. Correlation with happiness levels includes, but is not limited to, survey results and analysis of behavioral data. Adjustment of the order of proposals includes, but is not limited to, importance evaluation criteria and ordering algorithms. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input happiness correlation data into a generative AI and have the generative AI perform the adjustment of the order of proposals.

[0053] The promotion unit can select the optimal promotion method by referring to the user's past behavioral change data during promotion. For example, the promotion unit can analyze patterns of behavioral change that have been successful for the user in the past and propose similar methods. The promotion unit can also select a promotion method that is appropriate for a specific time of day or situation based on the user's past behavioral change data. The promotion unit can also select the optimal promotion method by referring to the user's past behavioral change data. Past behavioral change data includes, but is not limited to, behavioral logs and historical data. The selection of a promotion method includes, but is not limited to, algorithm performance evaluation and application conditions. Some or all of the above processing in the promotion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the promotion unit can input the user's past behavioral change data into a generative AI and have the generative AI select the optimal promotion method.

[0054] The promotion unit can customize the means of promotion based on the user's current living situation during promotion. For example, the promotion unit can suggest appropriate promotion means based on the user's current living situation (e.g., work, family, health). The promotion unit can also provide a customized behavior change plan according to the user's current living situation. The promotion unit can also customize the means of promotion based on the user's current living situation. This allows for more appropriate promotion by customizing the means of promotion based on the user's current living situation. Current living situation includes, but is not limited to, surveys and behavior logs. Customization of promotion means includes, but is not limited to, adjusting the behavior change plan and providing resources. Some or all of the above processing in the promotion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the promotion unit can input the user's current living situation data into a generative AI and have the generative AI perform the customization of promotion means.

[0055] The promotion unit can select the optimal promotion method during promotion, taking into account the user's geographical location information. For example, the promotion unit can provide resources and support relevant to the user's current location. The promotion unit can also select promotion methods related to local trends and events based on the user's geographical location information. The promotion unit can also select promotion methods related to a specific region, taking into account the user's geographical location information. This allows for the selection of the optimal promotion method by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location-based services. The selection of promotion methods includes, but is not limited to, local trends and event information. Some or all of the above processing in the promotion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the promotion unit can input the user's geographical location data into a generative AI and have the generative AI select the optimal promotion method.

[0056] The promotion unit can analyze the user's social media activity and propose promotional measures during the promotion process. For example, the promotion unit can obtain data from social media platforms frequently used by the user and propose appropriate promotional measures. The promotion unit can also analyze patterns in the user's social media activity and propose relevant promotional measures. The promotion unit can also analyze the user's social media interactions (e.g., comments, likes) and propose appropriate promotional measures. In this way, by analyzing the user's social media activity, appropriate promotional measures can be proposed. Social media activity includes, but is not limited to, posts, follow information, and like history. Proposals for promotional measures include, but is not limited to, adjusting behavioral change plans and providing resources. Some or all of the above processing in the promotion unit may be performed using, for example, generative AI, or not using generative AI. For example, the promotion unit can input the user's social media activity data into a generative AI and have the generative AI execute the proposal of promotional measures.

[0057] The recording unit can optimize the recording algorithm by referring to past recording data during recording. For example, the recording unit can analyze the user's past recording data and select the optimal recording algorithm. The recording unit can also select a recording algorithm that is appropriate for a specific time period or situation from the user's past recording data. The recording unit can also optimize the recording algorithm by referring to the user's past recording data. This allows the recording algorithm to be optimized by referring to past recording data. Past recording data includes, but is not limited to, activity logs and historical data. Optimization of the recording algorithm includes, but is not limited to, performance evaluation of the algorithm and application conditions. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recording unit can input the user's past recording data into a generative AI and have the generative AI perform the optimization of the recording algorithm.

[0058] The recording unit can weight the recorded data based on the timing of happiness measurement. For example, if the happiness measurement was recent, the recording unit will record it with a higher weight. If the happiness measurement was in the past, the recording unit can record it with an appropriate weight. If the happiness measurement was in the distant past, the recording unit can record it with a lower weight. By weighting the recorded data based on the timing of happiness measurement, more important data can be prioritized for recording. The timing of happiness measurement includes, but is not limited to, survey results and analysis of behavioral data. The weighting of the recorded data includes, but is not limited to, importance evaluation criteria and weighting algorithms. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recording unit can input happiness measurement timing data into a generative AI and have the generative AI perform the weighting of the recorded data.

[0059] The utilization unit can select the optimal utilization method by referring to the user's past local currency usage history when utilizing the currency. For example, the utilization unit can analyze the user's past local currency usage history and select the optimal utilization method. The utilization unit can also select a utilization method that is appropriate for a specific time of day or situation based on the user's past local currency usage history. The utilization unit can also select the optimal utilization method by referring to the user's past local currency usage history. This allows the optimal utilization method to be selected by referring to the user's past local currency usage history. Past local currency usage history includes, but is not limited to, the history of services and products used, and the frequency of use. The selection of a utilization method includes, but is not limited to, the analysis of usage history and application conditions. Some or all of the above-described processes in the utilization unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the utilization unit can input the user's past local currency usage history into a generating AI and have the generating AI select the optimal utilization method.

[0060] The application unit can customize how the local currency is used based on the user's current living situation. For example, the application unit can suggest an appropriate way to use the local currency based on the user's current living situation (e.g., work, family, health). The application unit can also provide a customized way to use the local currency according to the user's current living situation. The application unit can also customize how the local currency is used based on the user's current living situation. This allows for more appropriate use by customizing how the local currency is used based on the user's current living situation. Current living situation includes, but is not limited to, survey results and activity logs. Customization of how the local currency is used includes, but is not limited to, the selection of services or products and adjustment of how it is used. Some or all of the above processing in the application unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the application unit can input the user's current living situation data into a generative AI and have the generative AI perform the customization of how the local currency is used.

[0061] The application unit can select the optimal way to use the local currency, taking into account the user's geographical location information. For example, the application unit can suggest ways to use the local currency for services or products related to the user's current location. The application unit can also select ways to use the local currency related to local trends or events based on the user's geographical location information. The application unit can also select ways to use the local currency related to a specific region, taking into account the user's geographical location information. This allows for the selection of the optimal way to use the local currency by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Ways to use the local currency include, but is not limited to, places where it can be used and exchange rates. Some or all of the processing described above in the application unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the application unit can input the user's geographical location information into a generating AI and have the generating AI select ways to use the local currency.

[0062] The application unit can analyze the user's social media activity and propose ways to use the local currency when it is being used. For example, the application unit can acquire data from social media platforms that the user frequently uses and propose appropriate ways to use the local currency. The application unit can also analyze patterns in the user's social media activity and propose relevant ways to use the local currency. The application unit can also analyze the user's social media interactions (e.g., comments, likes) and propose appropriate ways to use the local currency. In this way, by analyzing the user's social media activity, it is possible to propose appropriate ways to use the local currency. Social media activity includes, but is not limited to, posts, follow information, and like history. Ways to use the local currency include, but is not limited to, places where it can be used and exchange rates. Some or all of the above processing in the application unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the application unit can input the user's social media activity data into a generative AI and have the generative AI propose ways to use the local currency.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The analytics department can collect user behavior data in real time and analyze it immediately. For example, it can record a user's behavior in real time when they are in a specific location and analyze it on the spot. The analytics department can also collect and analyze a user's behavior in real time when they are participating in a specific event. Furthermore, the analytics department can collect and analyze a user's behavior during a specific time period in real time. This enables the collection and analysis of behavior data in real time, providing faster feedback.

[0065] The measurement unit can predict changes in happiness levels based on user behavior data. For example, it can predict changes in happiness levels after a user performs a specific action. The measurement unit can also predict changes in happiness levels after a user participates in a specific event. Furthermore, the measurement unit can predict changes in happiness levels after actions taken by a user during a specific time period. This allows for more appropriate support to be provided by predicting changes in happiness levels based on user behavior.

[0066] The suggestion department can propose new hobbies and activities to improve user well-being based on user behavior data. For example, it can suggest hobbies and activities that the user has never shown interest in before. The suggestion department can also suggest events and community activities that the user has never participated in before. Furthermore, the suggestion department can suggest new skills and learning opportunities that the user has never tried before. In this way, by suggesting new hobbies and activities that improve user well-being, the quality of life for users can be improved.

[0067] The promotion department can provide incentives to encourage behavioral change based on user behavior data. For example, it can offer rewards when users perform specific actions. The promotion department can also offer bonus points when users achieve specific goals. Furthermore, it can offer special benefits when users achieve behavioral change within a specific period. This provides incentives to encourage user behavioral change, thereby increasing user motivation.

[0068] The recording unit can visualize changes in user happiness levels over time based on user behavior data. For example, it can display changes in user happiness levels in graphs and charts. The recording unit can also display changes in user happiness levels in a calendar format. Furthermore, the recording unit can display changes in user happiness levels in a timeline format. This allows users to intuitively understand the changes in their own happiness levels by visualizing them over time.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The analysis unit analyzes users' daily behavior and social media posts. Specifically, it records users' daily behavior and collects social media posts. Using multimodal AI technology, it analyzes user behavior, social media posts, voice, facial expressions, etc. For example, it uses natural language processing technology to analyze social media posts and extract emotions and topics. It also uses speech recognition technology to analyze voice data and extract emotions and content. It can also use image recognition technology to analyze facial expression data and extract emotions and changes in facial expressions. Step 2: The measurement unit measures happiness levels based on the analysis results obtained by the analysis unit. Specifically, it scores happiness levels based on user behavior data and social media posting data. The frequency and intensity of positive emotions are considered as criteria for measuring happiness levels. It is also possible to analyze user behavior data and track changes in happiness levels over time. Step 3: The proposal team proposes individual well-being plans and solutions to social issues based on the measurement results obtained by the measurement team. Specifically, they propose concrete action plans to improve the user's well-being. Based on the user's interests, they can also propose social contribution activities or volunteer work. They can also provide resources for counseling and mental health care. Step 4: The Facilitation Department promotes mental support and behavioral change based on the plan proposed by the Proposal Department. Specifically, they support the implementation of the proposed plan through natural dialogue with the user. They provide feedback and advice to promote behavioral change in the user. They can also provide counseling and mental health care resources. Step 5: The recording unit records the happiness level evaluation obtained by the measurement unit on the blockchain. Specifically, the reliability and transparency of the evaluation are ensured by recording the happiness level evaluation on the blockchain. The happiness level evaluation data can also be recorded in an encrypted form. Data tampering can also be prevented using blockchain's distributed ledger technology. Step 6: The utilization unit utilizes the happiness points recorded by the recording unit as local currency. Specifically, it provides places and services where happiness points can be used as local currency. It can also provide a platform for exchanging happiness points for local currency. It can also provide guidelines and rules for using happiness points as local currency.

[0071] (Example of form 2) The happiness measurement system according to an embodiment of the present invention is a system in which a personalized AI agent analyzes a user's daily behavior and social media posts to measure their happiness level. This happiness measurement system uses multimodal AI technology to analyze the user's behavior, social media posts, voice, facial expressions, etc., and evaluates the happiness level of the individual and society from multiple perspectives. Next, the happiness measurement system proposes an individualized happiness enhancement plan and solutions to social issues. As a result, the user receives mental support and is encouraged to change their behavior. Furthermore, the happiness measurement system provides guidance on social contribution activities through natural dialogue with the user. The happiness level evaluation is recorded on the blockchain, and the acquired "happiness points" are used as local currency. This system directly links economic activity and happiness improvement, realizing a new socioeconomic system. For example, the happiness measurement system analyzes the user's daily behavior and social media posts. Specifically, it uses multimodal AI technology to analyze the user's behavior, social media posts, voice, facial expressions, etc., and evaluates the happiness level of the individual and society from multiple perspectives. Next, the happiness measurement system proposes an individualized happiness enhancement plan and solutions to social issues. This allows users to receive emotional support and promote behavioral change. Furthermore, the happiness measurement system provides guidance on social contribution activities through natural dialogue with users. Happiness scores are recorded on the blockchain, and the earned "happiness points" are used as local currency. This system directly links economic activity with happiness improvement, realizing a new socioeconomic system. As a result, the happiness measurement system can comprehensively evaluate users' happiness and propose individualized happiness enhancement plans and solutions to social issues.

[0072] The happiness measurement system according to this embodiment comprises an analysis unit, a measurement unit, a proposal unit, a promotion unit, a recording unit, and an utilization unit. The analysis unit analyzes the user's daily behavior and SNS posts. For example, the analysis unit records the user's daily behavior and collects SNS posts. The analysis unit uses multimodal AI technology to analyze the user's behavior, SNS posts, voice, facial expressions, etc. For example, the analysis unit analyzes the user's SNS posts using natural language processing technology to extract emotions and topics. The analysis unit can also analyze the user's voice data using speech recognition technology to extract emotions and content. The analysis unit can also analyze the user's facial expression data using image recognition technology to extract emotions and changes in facial expressions. The measurement unit measures happiness based on the analysis results obtained by the analysis unit. For example, the measurement unit scores happiness based on the user's behavior data and SNS post data. As a criterion for measuring happiness, the measurement unit considers, for example, the frequency and intensity of positive emotions. The measurement unit can also analyze the user's behavior data and track changes in happiness over time. The Proposal Department proposes individual well-being enhancement plans and solutions to social issues based on the measurement results obtained by the Measurement Department. For example, the Proposal Department proposes specific action plans to improve the user's well-being. The Proposal Department can also propose social contribution activities and volunteer activities based on the user's interests. The Proposal Department can also provide counseling and mental health care resources to improve the user's well-being. The Promotion Department promotes mental support and behavioral change based on the plans proposed by the Proposal Department. For example, the Promotion Department supports the implementation of the proposed plans through natural dialogue with the user. The Promotion Department can also provide feedback and advice to promote behavioral change in the user. The Promotion Department can also provide counseling and mental health care resources to provide mental support to the user. The Recording Department records the well-being evaluations obtained by the Measurement Department on the blockchain. For example, the Recording Department ensures the reliability and transparency of the evaluations by recording the well-being evaluations on the blockchain. The Recording Department can also encrypt and record the well-being evaluation data. The Recording Department can also prevent data tampering using blockchain's distributed ledger technology.The utilization unit will use the happiness points recorded by the recording unit as local currency. The utilization unit will, for example, provide places and services where happiness points can be used as local currency. The utilization unit can also provide a platform for exchanging happiness points for local currency. The utilization unit can also provide guidelines and rules for using happiness points as local currency. As a result, the happiness measurement system according to this embodiment can analyze users' daily behavior and SNS posts, measure their happiness level, propose individual happiness enhancement plans and solutions to social issues, promote mental support and behavioral change, record happiness evaluations on the blockchain, and utilize the acquired happiness points as local currency.

[0073] The analytics department analyzes users' daily behavior and social media posts. Specifically, it records users' daily activities and collects social media posts. This includes collecting data from users' smartphones and wearable devices. For example, it collects data such as smartphone location information and activity logs, and heart rate and step count from wearable devices. This data is used to understand users' behavioral patterns and health status. Furthermore, collecting social media posts includes acquiring data from multiple social media platforms used by the user. This includes text posts, image posts, and video posts, and this data is analyzed using natural language processing and image recognition technologies. By using multimodal AI technology, it is possible to comprehensively analyze user behavior, social media posts, voice, facial expressions, etc. For example, natural language processing technology is used to analyze users' social media posts and extract emotions and topics. This makes it possible to understand what emotions the user is feeling and what topics they are interested in. For voice data analysis, speech recognition technology is used to extract the content and emotions of the user's statements. This makes it possible to understand what emotions the user is feeling and what they are talking about. The facial expression data analysis uses image recognition technology to extract changes in the user's facial expressions and emotions. This allows us to understand what emotions the user is feeling through changes in their facial expressions. By comprehensively analyzing this data, we can evaluate the user's level of happiness with high accuracy.

[0074] The measurement unit measures happiness levels based on the analysis results obtained by the analysis unit. Specifically, it scores happiness levels based on user behavior data and social media posting data. The frequency and intensity of positive emotions are considered as criteria for measuring happiness levels. For example, happiness levels are evaluated based on the frequency of positive words in users' social media posts and the results of sentiment analysis of the content of those posts. It can also analyze user behavior data and track changes in happiness levels over time. This allows for real-time understanding of fluctuations in users' happiness levels. Furthermore, the measurement unit analyzes users' voice data and facial expression data to evaluate changes in emotions. This allows for understanding under what circumstances users' happiness levels increase and under what circumstances they decrease. The measurement unit comprehensively analyzes this data to measure users' happiness levels with high accuracy.

[0075] The proposal department proposes individual well-being plans and solutions to social issues based on the measurement results obtained by the measurement department. Specifically, it proposes concrete action plans to improve the user's well-being. For example, it can suggest hobbies and leisure activities based on the user's interests. It can also suggest social contribution activities and volunteer activities to improve the user's well-being. This allows users to improve their own well-being while simultaneously contributing to society. Furthermore, the proposal department can also provide counseling and mental health care resources to improve the user's well-being. This includes counseling by professionals and introductions to apps and services for mental health care. The proposal department provides a variety of resources to improve the user's well-being and supports users in taking concrete actions to improve their own well-being.

[0076] The Facilitation Department promotes mental support and behavioral change based on the plan proposed by the Proposal Department. Specifically, it supports the implementation of the proposed plan through natural dialogue with the user. For example, it provides feedback and advice when the user implements the proposed behavior plan. This allows the user to review and improve their behavior. Furthermore, the Facilitation Department can also provide counseling and mental health care resources to provide the user's mental support. This includes professional counseling and introductions to apps and services for mental health care. The Facilitation Department provides specific support to help the user implement the proposed plan and improve their well-being.

[0077] The recording unit records the happiness level evaluation obtained by the measurement unit on the blockchain. Specifically, recording the happiness level evaluation on the blockchain ensures the reliability and transparency of the evaluation. By using blockchain technology, data tampering is prevented, and highly reliable data management is achieved. The recording unit can also record the happiness level evaluation data in encryption. This ensures data reliability while protecting user privacy. Furthermore, the recording unit can also prevent data tampering using blockchain's distributed ledger technology. As a result, happiness level evaluation data is managed in a safe and reliable manner.

[0078] The utilization unit will use the happiness points recorded by the recording unit as local currency. Specifically, it will provide places and services where happiness points can be used as local currency. For example, it will partner with local shops and service providers to enable the use of happiness points as local currency. This will allow users to contribute to the local economy by taking actions that improve their own happiness. Furthermore, the utilization unit can also provide a platform for exchanging happiness points for local currency. This includes providing procedures for exchanging happiness points for local currency through online platforms or apps. The utilization unit can also provide guidelines and rules for using happiness points as local currency. This will allow users to use happiness points as local currency with peace of mind. By utilizing happiness points as local currency, the utilization unit can simultaneously improve users' happiness and revitalize the local economy.

[0079] The analysis unit can analyze a user's daily activities, social media posts, voice, facial expressions, etc., using multimodal AI technology. For example, the analysis unit records a user's daily activities and collects social media posts. The analysis unit analyzes the user's activities, social media posts, voice, facial expressions, etc., using multimodal AI technology. For example, the analysis unit analyzes a user's social media posts using natural language processing technology to extract emotions and topics. The analysis unit can also analyze a user's voice data using speech recognition technology to extract emotions and content. The analysis unit can also analyze a user's facial expression data using image recognition technology to extract emotions and changes in facial expressions. In this way, by using multimodal AI technology, a user's daily activities, social media posts, voice, facial expressions, etc., can be analyzed from multiple perspectives. Multimodal AI technology includes, but is not limited to, speech recognition, image analysis, and text analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis department can input users' social media posts into a generation AI and have the AI ​​extract sentiments and topics.

[0080] The proposal unit can propose individual well-being plans and solutions to social problems. For example, the proposal unit can propose specific action plans to improve the user's well-being. The proposal unit can also propose social contribution activities and volunteer activities based on the user's interests and concerns. The proposal unit can also provide counseling and mental health care resources to improve the user's well-being. In this way, the user's well-being can be improved by proposing individual well-being plans and solutions to social problems. Individual well-being plans include, but are not limited to, counseling and behavioral guidance. Solutions to social problems include, but are not limited to, environmental problems and poverty problems. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input an action plan to improve the user's well-being into a generative AI and have the generative AI execute specific proposals.

[0081] The facilitator can facilitate emotional support and behavioral change through natural dialogue with the user. For example, the facilitator can support the implementation of a proposed plan through natural dialogue with the user. The facilitator can also provide feedback and advice to facilitate the user's behavioral change. The facilitator can also provide counseling and mental health care resources to provide emotional support to the user. This allows for the facilitator to facilitate emotional support and behavioral change through natural dialogue with the user. Natural dialogue includes, but is not limited to, dialogue scenarios and the dialogue engine used. Some or all of the processing described above in the facilitator may be performed, for example, using generative AI or not using generative AI. For example, the facilitator can input the dialogue with the user into a generative AI and have the generative AI execute the dialogue scenario.

[0082] The recording unit can record happiness scores on a blockchain. The recording unit ensures the reliability and transparency of the scores by recording them on a blockchain, for example. The recording unit can also encrypt and record the happiness score data. The recording unit can also prevent data tampering by using blockchain's distributed ledger technology. This ensures the reliability and transparency of the scores by recording them on a blockchain. The blockchain includes, but is not limited to, the blockchain platform used and the method of recording the data. Some or all of the above processing in the recording unit may be performed, for example, using a generative AI, or not using a generative AI. For example, the recording unit can input happiness score data into a generative AI and have the generative AI perform data encryption and recording.

[0083] The utilization unit can use the acquired happiness points as local currency. The utilization unit can, for example, provide places and services where happiness points can be used as local currency. The utilization unit can also provide a platform for exchanging happiness points for local currency. The utilization unit can also provide guidelines and rules for using happiness points as local currency. This allows for a direct link between economic activity and increased happiness by utilizing acquired happiness points as local currency. Happiness points include, but are not limited to, criteria for awarding points for actions and the value of points. Local currency includes, but are not limited to, places where it can be used and exchange rates. Some or all of the above processing in the utilization unit may be performed, for example, using a generative AI, or not using a generative AI. For example, the utilization unit can input happiness points into a generative AI and have the generative AI execute methods for using them as local currency.

[0084] The analysis unit can estimate a user's emotions and adjust the analysis methods for daily behavior and social media posts based on the estimated emotions. For example, if a user is stressed, the analysis unit will prioritize analyzing posts and behaviors related to stress reduction. If a user is feeling happy, the analysis unit can also focus on analyzing behaviors and posts that help maintain that feeling. If a user is feeling anxious, the analysis unit can identify and analyze behaviors and posts that cause anxiety. By adjusting the analysis method based on the user's emotions, more appropriate analysis becomes possible. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. Adjusting the analysis method includes, but is not limited to, changing the algorithm used or changing the target of analysis. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the analysis method based on that emotion.

[0085] The analysis unit can analyze a user's past behavioral history and select the optimal analysis algorithm. For example, the analysis unit can analyze a user's past behavioral patterns and select the most effective analysis algorithm. The analysis unit can also analyze the content of a user's past social media posts and select an appropriate analysis algorithm. The analysis unit can also select an analysis algorithm that is appropriate for a specific time period or situation based on the user's past behavioral history. In this way, the optimal analysis algorithm can be selected by analyzing the user's past behavioral history. Past behavioral history includes, but is not limited to, behavioral logs and historical data. The optimal analysis algorithm includes, but is not limited to, algorithm performance evaluation and application conditions. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's past behavioral data into a generative AI and have the generative AI select the optimal analysis algorithm.

[0086] The analysis unit can filter data based on the user's current lifestyle and areas of interest during analysis. For example, the analysis unit can prioritize the analysis of posts and actions related to topics the user is currently interested in. The analysis unit can also filter relevant data based on the user's current lifestyle (e.g., work, family, health). The analysis unit can also filter posts and actions containing specific keywords based on the user's current areas of interest. This allows for the analysis of more relevant data by filtering based on the user's current lifestyle and areas of interest. Current lifestyle includes, but is not limited to, survey results and activity logs. Areas of interest include, but is not limited to, social media following information and search history. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input data on the user's current lifestyle and areas of interest into a generative AI and have the generative AI perform the filtering.

[0087] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit will prioritize displaying analysis results related to stress reduction. If the user is feeling happy, the analysis unit can also prioritize displaying analysis results that help maintain that feeling. If the user is feeling anxious, the analysis unit can also prioritize displaying analysis results that cause anxiety. In this way, by prioritizing the analysis results based on the user's emotions, more important results can be displayed preferentially. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. Prioritization includes, but is not limited to, importance evaluation criteria and prioritization algorithms. Emotion estimation is implemented using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI determine the priority of the analysis results.

[0088] The analysis unit can prioritize the analysis of highly relevant data by considering the user's geographical location during analysis. For example, the analysis unit can prioritize the analysis of posts and actions related to the user's current location. The analysis unit can also prioritize the analysis of data related to local trends and events based on the user's geographical location. The analysis unit can also prioritize the analysis of data related to a specific region by considering the user's geographical location. This allows for the prioritization of highly relevant data by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Highly relevant data includes, but is not limited to, the user's behavioral history and areas of interest. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform a priority analysis of highly relevant data.

[0089] The analysis unit can analyze a user's social media activity and obtain relevant data during the analysis process. For example, the analysis unit can obtain and analyze data from social media platforms where the user frequently posts. The analysis unit can also analyze patterns in a user's social media activity and obtain relevant data. The analysis unit can also analyze a user's social media interactions (e.g., comments, likes) and obtain relevant data. In this way, relevant data can be obtained by analyzing a user's social media activity. Social media activity includes, but is not limited to, posts, follow information, and like history. Relevant data includes, but is not limited to, a user's areas of interest and behavioral history. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input user social media activity data into a generative AI and have the generative AI perform the acquisition of relevant data.

[0090] The measurement unit can estimate the user's emotions and adjust the method of measuring happiness based on the estimated user emotions. For example, if the user is feeling stressed, the measurement unit can measure happiness by emphasizing indicators related to stress reduction. If the user is feeling happy, the measurement unit can also measure happiness by emphasizing indicators that help maintain that feeling. If the user is feeling anxious, the measurement unit can also measure happiness by emphasizing indicators that cause anxiety. By adjusting the method of measuring happiness based on the user's emotions, a more accurate measurement of happiness can be achieved. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. Adjustment of the measurement method includes, but is not limited to, changing the measurement algorithm used or changing the object being measured. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using, for example, generative AI, or without generative AI. For example, the measurement unit can input user emotion data into a generating AI, which can then adjust the method for measuring happiness.

[0091] The measurement unit can optimize the measurement algorithm by referring to the user's past happiness data during measurement. For example, the measurement unit can analyze the user's past happiness data and select the optimal measurement algorithm. The measurement unit can also select a measurement algorithm that is appropriate for a specific time of day or situation based on the user's past happiness data. The measurement unit can also optimize the measurement algorithm by referring to the user's past happiness data. Past happiness data includes, but is not limited to, survey results and behavioral logs. Optimization of the measurement algorithm includes, but is not limited to, performance evaluation of the algorithm and application conditions. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the measurement unit can input the user's past happiness data into a generative AI and have the generative AI perform the optimization of the measurement algorithm.

[0092] The measurement unit can improve the accuracy of measurements based on the user's lifestyle and behavioral patterns during measurement. For example, the measurement unit can improve the accuracy of measurements based on the user's current lifestyle (e.g., work, family, health). The measurement unit can also improve the accuracy of measurements by analyzing the user's behavioral patterns. The measurement unit can also improve the accuracy of measurements based on the user's lifestyle and behavioral patterns. This allows for improved accuracy of measurements based on the user's lifestyle and behavioral patterns. Lifestyle includes, but is not limited to, survey data and behavioral logs. Behavioral patterns include, but are not limited to, behavioral logs and historical data. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or without a generative AI. For example, the measurement unit can input data on the user's lifestyle and behavioral patterns into a generative AI and have the generative AI perform the measurement accuracy improvement.

[0093] The measurement unit can estimate the user's emotions and adjust the order in which happiness measurement results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the measurement unit can prioritize displaying measurement results related to stress reduction. If the user is feeling happy, the measurement unit can also prioritize displaying measurement results that help maintain that feeling. If the user is feeling anxious, the measurement unit can also prioritize displaying measurement results that cause anxiety. In this way, by adjusting the display order of measurement results based on the user's emotions, more important results can be prioritized. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. Adjustment of the display order includes, but is not limited to, importance evaluation criteria and display order algorithms. Emotion estimation is implemented using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using, for example, generative AI, or without generative AI. For example, the measurement unit can input user emotion data into a generating AI and have the generating AI adjust the display order of the measurement results.

[0094] The measurement unit can measure happiness levels while considering the user's geographical location information. For example, the measurement unit can measure happiness levels related to the user's current location. The measurement unit can also measure happiness levels related to local trends and events based on the user's geographical location information. The measurement unit can also measure happiness levels related to a specific region, taking into account the user's geographical location information. This allows for a more accurate measurement of happiness levels by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Measurement of happiness levels includes, but is not limited to, surveys and analysis of behavioral data. Some or all of the above-described processes in the measurement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the measurement unit can input the user's geographical location information into a generative AI and have the generative AI perform the measurement of happiness levels.

[0095] The measurement unit can improve the accuracy of measurements by referring to the user's relevant literature during measurement. For example, the measurement unit can improve the accuracy of measurements by referring to the user's relevant literature. The measurement unit can also improve the accuracy of measurements by emphasizing specific indicators from the user's relevant literature. The measurement unit can also improve the accuracy of measurements by referring to the user's relevant literature. This allows for improvement of measurement accuracy by referring to the user's relevant literature. Relevant literature includes, but is not limited to, academic papers and technical reports. Improving measurement accuracy includes, but is not limited to, algorithm performance evaluation and data accuracy improvement. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the measurement unit can input the user's relevant literature into a generative AI and have the generative AI perform the measurement accuracy improvement.

[0096] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated emotions. For example, if the user is stressed, the suggestion unit will use a simple and easy-to-understand expression. If the user is feeling happy, the suggestion unit may also use an expression that helps maintain that feeling. If the user is feeling anxious, the suggestion unit may also use an expression that helps alleviate anxiety. By adjusting the expression of the suggestion based on the user's emotions, more appropriate suggestions can be made. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. Adjustment of the suggestion expression includes, but is not limited to, word choice and suggestion format. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the way the proposal is expressed.

[0097] The proposal unit can adjust the level of detail of its proposals based on the importance of happiness. For example, if happiness is highly important, the proposal unit will provide detailed proposals. If happiness is moderately important, the proposal unit may provide proposals with a moderate level of detail. If happiness is not highly important, the proposal unit may provide concise proposals. By adjusting the level of detail of proposals based on the importance of happiness, more effective proposals can be made. The importance of happiness includes, but is not limited to, survey results and analysis of behavioral data. The adjustment of the level of detail of proposals includes, but is not limited to, the depth of the proposal content and the specificity of the information. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input happiness importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the proposals.

[0098] The proposal unit can apply different proposal algorithms depending on the happiness category when making a proposal. For example, if the happiness category is "health," the proposal unit will apply a proposal algorithm related to health. If the happiness category is "relationships," the proposal unit can also apply a proposal algorithm related to relationships. If the happiness category is "work," the proposal unit can also apply a proposal algorithm related to work. By applying different proposal algorithms depending on the happiness category, more appropriate proposals can be made. Happiness categories include, but are not limited to, health, economy, and social relationships. Proposal algorithms include, but are not limited to, recommendation systems and machine learning algorithms. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or without using generative AI. For example, the proposal unit can input happiness category data into a generative AI and have the generative AI perform the application of the proposal algorithm.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can provide a short, concise suggestion. If the user is feeling happy, the suggestion unit can provide a detailed suggestion. If the user is feeling anxious, the suggestion unit can provide a simple and easy-to-understand suggestion. By adjusting the length of the suggestion based on the user's emotions, more appropriate suggestions can be made. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. Adjusting the length of the suggestion includes, but is not limited to, summarizing the suggestion content and simplifying the information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without using generative AI. For example, the suggestion unit can input user emotion data into a generating AI and have the AI ​​adjust the length of the suggestion.

[0100] The proposal unit can determine the priority of proposals based on the timing of happiness measurement. For example, if the happiness measurement was recent, the proposal unit will prioritize the proposal. If the happiness measurement was in the past, the proposal unit may also make a proposal with a moderate priority. If the happiness measurement was in the distant past, the proposal unit may also make a proposal with a low priority. This allows for more effective proposals by determining the priority of proposals based on the timing of happiness measurement. The timing of happiness measurement includes, but is not limited to, survey results and analysis of behavioral data. The determination of proposal priority includes, but is not limited to, importance evaluation criteria and prioritization algorithms. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input happiness measurement timing data into a generative AI and have the generative AI determine the priority of proposals.

[0101] The proposal unit can adjust the order of proposals based on their correlation with happiness levels. For example, the proposal unit can prioritize proposals where the correlation with happiness levels is high. If the correlation with happiness levels is moderate, the proposal unit can also make proposals in a moderate order. If the correlation with happiness levels is low, the proposal unit can also make proposals in an order of decreasing correlation. By adjusting the order of proposals based on the correlation with happiness levels, more appropriate proposals can be made. Correlation with happiness levels includes, but is not limited to, survey results and analysis of behavioral data. Adjustment of the order of proposals includes, but is not limited to, importance evaluation criteria and ordering algorithms. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input happiness correlation data into a generative AI and have the generative AI perform the adjustment of the order of proposals.

[0102] The facilitator can estimate the user's emotions and adjust the method of mental support based on the estimated emotions. For example, if the user is feeling stressed, the facilitator can suggest relaxation techniques or stress management methods. If the user is feeling happy, the facilitator can also provide positive feedback to maintain that feeling. If the user is feeling anxious, the facilitator can also provide counseling or mental health resources to reduce anxiety. This allows for more appropriate support by adjusting the method of mental support based on the user's emotions. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. Adjustment of mental support methods includes, but is not limited to, relaxation techniques or stress management methods. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the facilitator may be performed using, for example, generative AI, or without generative AI. For example, the promotion unit can input user emotional data into a generating AI and have the generating AI adjust the method of providing emotional support.

[0103] The promotion unit can select the optimal promotion method by referring to the user's past behavioral change data during promotion. For example, the promotion unit can analyze patterns of behavioral change that have been successful for the user in the past and propose similar methods. The promotion unit can also select a promotion method that is appropriate for a specific time of day or situation based on the user's past behavioral change data. The promotion unit can also select the optimal promotion method by referring to the user's past behavioral change data. Past behavioral change data includes, but is not limited to, behavioral logs and historical data. The selection of a promotion method includes, but is not limited to, algorithm performance evaluation and application conditions. Some or all of the above processing in the promotion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the promotion unit can input the user's past behavioral change data into a generative AI and have the generative AI select the optimal promotion method.

[0104] The promotion unit can customize the means of promotion based on the user's current living situation during promotion. For example, the promotion unit can suggest appropriate promotion means based on the user's current living situation (e.g., work, family, health). The promotion unit can also provide a customized behavior change plan according to the user's current living situation. The promotion unit can also customize the means of promotion based on the user's current living situation. This allows for more appropriate promotion by customizing the means of promotion based on the user's current living situation. Current living situation includes, but is not limited to, surveys and behavior logs. Customization of promotion means includes, but is not limited to, adjusting the behavior change plan and providing resources. Some or all of the above processing in the promotion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the promotion unit can input the user's current living situation data into a generative AI and have the generative AI perform the customization of promotion means.

[0105] The promotion unit can estimate the user's emotions and determine the priority of promotions based on the estimated emotions. For example, if the user is feeling stressed, the promotion unit will prioritize stress-reducing promotions. If the user is feeling happy, the promotion unit may also prioritize promotions to maintain that feeling. If the user is feeling anxious, the promotion unit may also prioritize promotions to reduce anxiety. By determining the priority of promotions based on the user's emotions, more effective promotions become possible. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. Determining the priority of promotions includes, but is not limited to, importance evaluation criteria and prioritization algorithms. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the promotion unit may be performed using, for example, generative AI, or without generative AI. For example, the promotion unit can input user emotion data into a generating AI and have the generating AI determine the priority of promotions.

[0106] The promotion unit can select the optimal promotion method during promotion, taking into account the user's geographical location information. For example, the promotion unit can provide resources and support relevant to the user's current location. The promotion unit can also select promotion methods related to local trends and events based on the user's geographical location information. The promotion unit can also select promotion methods related to a specific region, taking into account the user's geographical location information. This allows for the selection of the optimal promotion method by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location-based services. The selection of promotion methods includes, but is not limited to, local trends and event information. Some or all of the above processing in the promotion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the promotion unit can input the user's geographical location data into a generative AI and have the generative AI select the optimal promotion method.

[0107] The promotion unit can analyze the user's social media activity and propose promotional measures during the promotion process. For example, the promotion unit can obtain data from social media platforms frequently used by the user and propose appropriate promotional measures. The promotion unit can also analyze patterns in the user's social media activity and propose relevant promotional measures. The promotion unit can also analyze the user's social media interactions (e.g., comments, likes) and propose appropriate promotional measures. In this way, by analyzing the user's social media activity, appropriate promotional measures can be proposed. Social media activity includes, but is not limited to, posts, follow information, and like history. Proposals for promotional measures include, but is not limited to, adjusting behavioral change plans and providing resources. Some or all of the above processing in the promotion unit may be performed using, for example, generative AI, or not using generative AI. For example, the promotion unit can input the user's social media activity data into a generative AI and have the generative AI execute the proposal of promotional measures.

[0108] The recording unit can estimate the user's emotions and select recording data based on the estimated user emotions. For example, if the user is feeling stressed, the recording unit may prioritize recording data related to stress reduction. If the user is feeling happy, the recording unit may also prioritize recording data that helps maintain that feeling. If the user is feeling anxious, the recording unit may also prioritize recording data that causes anxiety. By selecting recording data based on the user's emotions, more appropriate data can be recorded. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. Selection of recording data includes, but is not limited to, importance evaluation criteria and data prioritization. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the recording unit may be performed using, for example, generative AI, or without generative AI. For example, the recording unit can input user emotion data into a generating AI and have the generating AI select the data to be recorded.

[0109] The recording unit can optimize the recording algorithm by referring to past recording data during recording. For example, the recording unit can analyze the user's past recording data and select the optimal recording algorithm. The recording unit can also select a recording algorithm that is appropriate for a specific time period or situation from the user's past recording data. The recording unit can also optimize the recording algorithm by referring to the user's past recording data. This allows the recording algorithm to be optimized by referring to past recording data. Past recording data includes, but is not limited to, activity logs and historical data. Optimization of the recording algorithm includes, but is not limited to, performance evaluation of the algorithm and application conditions. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recording unit can input the user's past recording data into a generative AI and have the generative AI perform the optimization of the recording algorithm.

[0110] The recording unit can estimate the user's emotions and adjust the recording frequency based on the estimated emotions. For example, if the user is stressed, the recording unit may record frequently to collect data for stress reduction. If the user is feeling happy, the recording unit may also record at a moderate frequency to maintain that feeling. If the user is feeling anxious, the recording unit may also record frequently to collect data that is causing the anxiety. By adjusting the recording frequency based on the user's emotions, data can be recorded at a more appropriate frequency. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. Adjustment of recording frequency includes, but is not limited to, importance evaluation criteria and recording frequency algorithms. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the recording unit may be performed using, for example, generative AI, or without generative AI. For example, the recording unit can input user emotion data into a generating AI and have the generating AI adjust the recording frequency.

[0111] The recording unit can weight the recorded data based on the timing of happiness measurement. For example, if the happiness measurement was recent, the recording unit will record it with a higher weight. If the happiness measurement was in the past, the recording unit can record it with an appropriate weight. If the happiness measurement was in the distant past, the recording unit can record it with a lower weight. By weighting the recorded data based on the timing of happiness measurement, more important data can be prioritized for recording. The timing of happiness measurement includes, but is not limited to, survey results and analysis of behavioral data. The weighting of the recorded data includes, but is not limited to, importance evaluation criteria and weighting algorithms. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recording unit can input happiness measurement timing data into a generative AI and have the generative AI perform the weighting of the recorded data.

[0112] The application unit can estimate the user's emotions and adjust how the local currency is used based on those estimated emotions. For example, if the user is feeling stressed, the application unit can suggest ways to use the local currency for services or products related to stress reduction. If the user is feeling happy, the application unit can also suggest ways to use the local currency for services or products that help maintain that feeling. If the user is feeling anxious, the application unit can also suggest ways to use the local currency for services or products that help alleviate anxiety. By adjusting how the local currency is used based on the user's emotions, more appropriate use becomes possible. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. Adjusting how the local currency is used includes, but is not limited to, the selection of services or products and suggestions for how to use them. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the utilization unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the utilization unit can input user emotion data into a generating AI and have the generating AI adjust how the local currency is used.

[0113] The utilization unit can select the optimal utilization method by referring to the user's past local currency usage history when utilizing the currency. For example, the utilization unit can analyze the user's past local currency usage history and select the optimal utilization method. The utilization unit can also select a utilization method that is appropriate for a specific time of day or situation based on the user's past local currency usage history. The utilization unit can also select the optimal utilization method by referring to the user's past local currency usage history. This allows the optimal utilization method to be selected by referring to the user's past local currency usage history. Past local currency usage history includes, but is not limited to, the history of services and products used, and the frequency of use. The selection of a utilization method includes, but is not limited to, the analysis of usage history and application conditions. Some or all of the above-described processes in the utilization unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the utilization unit can input the user's past local currency usage history into a generating AI and have the generating AI select the optimal utilization method.

[0114] The application unit can customize how the local currency is used based on the user's current living situation. For example, the application unit can suggest an appropriate way to use the local currency based on the user's current living situation (e.g., work, family, health). The application unit can also provide a customized way to use the local currency according to the user's current living situation. The application unit can also customize how the local currency is used based on the user's current living situation. This allows for more appropriate use by customizing how the local currency is used based on the user's current living situation. Current living situation includes, but is not limited to, survey results and activity logs. Customization of how the local currency is used includes, but is not limited to, the selection of services or products and adjustment of how it is used. Some or all of the above processing in the application unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the application unit can input the user's current living situation data into a generative AI and have the generative AI perform the customization of how the local currency is used.

[0115] The application unit can estimate the user's emotions and determine the priority of local currency based on the estimated emotions. For example, if the user is feeling stressed, the application unit will prioritize the use of local currency for services and products related to stress reduction. If the user is feeling happy, the application unit can also prioritize the use of local currency for services and products that maintain that feeling. If the user is feeling anxious, the application unit can also prioritize the use of local currency for services and products that alleviate anxiety. This allows for more appropriate use of local currency by determining the priority of local currency based on the user's emotions. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. Determining the priority of local currency includes, but is not limited to, importance evaluation criteria and prioritization algorithms. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the processing described above in the application unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the application unit can input user emotion data into a generative AI and have the generative AI determine the priority of local currencies.

[0116] The application unit can select the optimal way to use the local currency, taking into account the user's geographical location information. For example, the application unit can suggest ways to use the local currency for services or products related to the user's current location. The application unit can also select ways to use the local currency related to local trends or events based on the user's geographical location information. The application unit can also select ways to use the local currency related to a specific region, taking into account the user's geographical location information. This allows for the selection of the optimal way to use the local currency by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Ways to use the local currency include, but is not limited to, places where it can be used and exchange rates. Some or all of the processing described above in the application unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the application unit can input the user's geographical location information into a generating AI and have the generating AI select ways to use the local currency.

[0117] The application unit can analyze the user's social media activity and propose ways to use the local currency when it is being used. For example, the application unit can acquire data from social media platforms that the user frequently uses and propose appropriate ways to use the local currency. The application unit can also analyze patterns in the user's social media activity and propose relevant ways to use the local currency. The application unit can also analyze the user's social media interactions (e.g., comments, likes) and propose appropriate ways to use the local currency. In this way, by analyzing the user's social media activity, it is possible to propose appropriate ways to use the local currency. Social media activity includes, but is not limited to, posts, follow information, and like history. Ways to use the local currency include, but is not limited to, places where it can be used and exchange rates. Some or all of the above processing in the application unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the application unit can input the user's social media activity data into a generative AI and have the generative AI propose ways to use the local currency.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The analytics department can collect user behavior data in real time and analyze it immediately. For example, it can record a user's behavior in real time when they are in a specific location and analyze it on the spot. The analytics department can also collect and analyze a user's behavior in real time when they are participating in a specific event. Furthermore, the analytics department can collect and analyze a user's behavior during a specific time period in real time. This enables the collection and analysis of behavior data in real time, providing faster feedback.

[0120] The measurement unit can predict changes in happiness levels based on user behavior data. For example, it can predict changes in happiness levels after a user performs a specific action. The measurement unit can also predict changes in happiness levels after a user participates in a specific event. Furthermore, the measurement unit can predict changes in happiness levels after actions taken by a user during a specific time period. This allows for more appropriate support to be provided by predicting changes in happiness levels based on user behavior.

[0121] The suggestion department can propose new hobbies and activities to improve user well-being based on user behavior data. For example, it can suggest hobbies and activities that the user has never shown interest in before. The suggestion department can also suggest events and community activities that the user has never participated in before. Furthermore, the suggestion department can suggest new skills and learning opportunities that the user has never tried before. In this way, by suggesting new hobbies and activities that improve user well-being, the quality of life for users can be improved.

[0122] The promotion department can provide incentives to encourage behavioral change based on user behavior data. For example, it can offer rewards when users perform specific actions. The promotion department can also offer bonus points when users achieve specific goals. Furthermore, it can offer special benefits when users achieve behavioral change within a specific period. This provides incentives to encourage user behavioral change, thereby increasing user motivation.

[0123] The recording unit can visualize changes in user happiness levels over time based on user behavior data. For example, it can display changes in user happiness levels in graphs and charts. The recording unit can also display changes in user happiness levels in a calendar format. Furthermore, the recording unit can display changes in user happiness levels in a timeline format. This allows users to intuitively understand the changes in their own happiness levels by visualizing them over time.

[0124] The analytics department can estimate a user's emotions and filter the content of social media posts based on those emotions. For example, if a user is feeling stressed, posts related to stress reduction will be prioritized. If a user is feeling happy, the analytics department can also prioritize positive posts that help maintain that feeling. Furthermore, if a user is feeling anxious, the analytics department can prioritize posts related to anxiety reduction. By filtering social media posts based on user emotions, more relevant information can be provided.

[0125] The measurement unit can estimate the user's emotions and customize the happiness measurement results based on those emotions. For example, if the user is feeling stressed, it can highlight measurement results related to stress reduction. If the user is feeling happy, the measurement unit can also highlight measurement results that help maintain that feeling. Furthermore, if the user is feeling anxious, the measurement unit can highlight measurement results related to anxiety reduction. By customizing the happiness measurement results based on the user's emotions, more appropriate feedback can be provided.

[0126] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is feeling stressed, it can immediately offer suggestions to reduce stress. If the user is feeling happy, the suggestion function can also offer suggestions at the appropriate time to help maintain that feeling. Furthermore, if the user is feeling anxious, the suggestion function can immediately offer suggestions to reduce anxiety. By adjusting the timing of suggestions based on the user's emotions, more effective suggestions become possible.

[0127] The Facilitation Unit can estimate the user's emotions and adjust the methods of supporting behavioral change based on those estimated emotions. For example, if the user is feeling stressed, it can suggest relaxation techniques or stress management methods. If the user is feeling happy, the Facilitation Unit can also provide positive feedback to help maintain that feeling. Furthermore, if the user is feeling anxious, the Facilitation Unit can provide counseling or mental health resources to reduce anxiety. By adjusting the methods of supporting behavioral change based on the user's emotions, more appropriate support becomes possible.

[0128] The recording unit can estimate the user's emotions and adjust how the recorded data is displayed based on those emotions. For example, if the user is feeling stressed, it can highlight data related to stress reduction. If the user is feeling happy, the recording unit can also highlight data that helps maintain that feeling. Furthermore, if the user is feeling anxious, the recording unit can also highlight data related to anxiety reduction. By adjusting how the recorded data is displayed based on the user's emotions, more relevant information can be provided.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The analysis unit analyzes users' daily behavior and social media posts. Specifically, it records users' daily behavior and collects social media posts. Using multimodal AI technology, it analyzes user behavior, social media posts, voice, facial expressions, etc. For example, it uses natural language processing technology to analyze social media posts and extract emotions and topics. It also uses speech recognition technology to analyze voice data and extract emotions and content. It can also use image recognition technology to analyze facial expression data and extract emotions and changes in facial expressions. Step 2: The measurement unit measures happiness levels based on the analysis results obtained by the analysis unit. Specifically, it scores happiness levels based on user behavior data and social media posting data. The frequency and intensity of positive emotions are considered as criteria for measuring happiness levels. It is also possible to analyze user behavior data and track changes in happiness levels over time. Step 3: The proposal team proposes individual well-being plans and solutions to social issues based on the measurement results obtained by the measurement team. Specifically, they propose concrete action plans to improve the user's well-being. Based on the user's interests, they can also propose social contribution activities or volunteer work. They can also provide resources for counseling and mental health care. Step 4: The Facilitation Department promotes mental support and behavioral change based on the plan proposed by the Proposal Department. Specifically, they support the implementation of the proposed plan through natural dialogue with the user. They provide feedback and advice to promote behavioral change in the user. They can also provide counseling and mental health care resources. Step 5: The recording unit records the happiness level evaluation obtained by the measurement unit on the blockchain. Specifically, the reliability and transparency of the evaluation are ensured by recording the happiness level evaluation on the blockchain. The happiness level evaluation data can also be recorded in an encrypted form. Data tampering can also be prevented using blockchain's distributed ledger technology. Step 6: The utilization unit utilizes the happiness points recorded by the recording unit as local currency. Specifically, it provides places and services where happiness points can be used as local currency. It can also provide a platform for exchanging happiness points for local currency. It can also provide guidelines and rules for using happiness points as local currency.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the analysis unit, measurement unit, proposal unit, promotion unit, recording unit, and utilization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit collects the user's daily activities and SNS posts using the camera 42 and microphone 38B of the smart device 14 and analyzes them using the control unit 46A. The measurement unit is implemented in the specific processing unit 290 of the data processing unit 12 and measures happiness levels based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes individual happiness promotion plans and solutions to social issues. The promotion unit is implemented in the specific processing unit 46A of the smart device 14 and promotes mental support and behavioral change through natural dialogue with the user. The recording unit is implemented in the specific processing unit 290 of the data processing unit 12 and records happiness level evaluations on a blockchain. The utilization unit is implemented in the specific processing unit 46A of the smart device 14 and utilizes happiness points as local currency. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the analysis unit, measurement unit, proposal unit, promotion unit, recording unit, and utilization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit collects the user's daily activities and SNS posts using the camera 42 and microphone 238 of the smart glasses 214 and analyzes them using the control unit 46A. The measurement unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and measures happiness levels based on the analysis results. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes individual happiness enhancement plans and solutions to social issues. The promotion unit is implemented, for example, by the control unit 46A of the smart glasses 214 and promotes mental support and behavioral change through natural dialogue with the user. The recording unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and records happiness level evaluations on a blockchain. The utilization unit is implemented, for example, by the control unit 46A of the smart glasses 214 and utilizes happiness points as local currency. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the analysis unit, measurement unit, proposal unit, promotion unit, recording unit, and utilization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit collects the user's daily activities and SNS posts using the camera 42 and microphone 238 of the headset terminal 314 and analyzes them using the control unit 46A. The measurement unit is implemented in the specific processing unit 290 of the data processing unit 12 and measures happiness levels based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes individual happiness promotion plans and solutions to social issues. The promotion unit is implemented in the specific processing unit 46A of the headset terminal 314 and promotes mental support and behavioral change through natural dialogue with the user. The recording unit is implemented in the specific processing unit 290 of the data processing unit 12 and records happiness level evaluations on a blockchain. The utilization unit is implemented in the specific processing unit 46A of the headset terminal 314 and utilizes happiness points as local currency. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the analysis unit, measurement unit, proposal unit, promotion unit, recording unit, and utilization unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit collects the user's daily activities and SNS posts using the camera 42 and microphone 238 of the robot 414 and analyzes them using the control unit 46A. The measurement unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and measures happiness levels based on the analysis results. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes individual happiness promotion plans and solutions to social issues. The promotion unit is implemented, for example, by the control unit 46A of the robot 414 and promotes mental support and behavioral change through natural dialogue with the user. The recording unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and records happiness level evaluations on a blockchain. The utilization unit is implemented, for example, by the control unit 46A of the robot 414 and utilizes happiness points as local currency. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) The analytics department analyzes users' daily behavior and social media posts, A measurement unit that measures happiness based on the analysis results obtained by the aforementioned analysis unit, Based on the measurement results obtained by the aforementioned measurement unit, the proposal unit proposes individual happiness promotion plans and solutions to social issues. Based on the plan proposed by the aforementioned proposal department, the promotion department will provide mental support and promote behavioral change, A recording unit that records the happiness level evaluation obtained by the measurement unit on a blockchain, The system includes a utilization unit that uses the happiness points recorded by the recording unit as local currency. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Using multimodal AI technology, we analyze users' daily behavior, social media posts, voice, facial expressions, and more. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose individual well-being promotion plans and solutions to social issues. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned promotion unit is Promoting emotional support and behavioral change through natural dialogue with users The system described in Appendix 1, characterized by the features described herein. (Note 5) The recording unit is, Record happiness levels using blockchain technology. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned utilization unit is, The happiness points earned will be used as local currency. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate user sentiment and adjust the analysis methods for daily behavior and social media posts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is Analyze the user's past behavior history and select the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During analysis, 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 10) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During analysis, the system prioritizes analyzing highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During the analysis, we analyze users' social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned measuring unit is We estimate the user's emotions and adjust the method of measuring happiness based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned measuring unit is During measurement, the measurement algorithm is optimized by referring to the user's past happiness data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned measuring unit is During measurement, the accuracy of the measurement is improved based on the user's lifestyle and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned measuring unit is It estimates the user's emotions and adjusts the order in which happiness measurement results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned measuring unit is When measuring happiness, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned measuring unit is During measurement, the system improves measurement accuracy by referring to relevant user literature. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of happiness. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the happiness category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, prioritize them based on when happiness levels are measured. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to happiness levels. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned promotion unit is It estimates the user's emotions and adjusts the method of providing emotional support based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned promotion unit is During the promotion process, the optimal promotion method is selected by referring to the user's past behavioral change data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned promotion unit is During promotion, the promotion methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned promotion unit is It estimates user sentiment and determines promotional priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned promotion unit is During promotion, the optimal promotion method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned promotion unit is During the promotion phase, we analyze users' social media activity and propose promotional strategies. The system described in Appendix 1, characterized by the features described herein. (Note 31) The recording unit is, The system estimates the user's emotions and selects the recording data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The recording unit is, During recording, the recording algorithm is optimized by referring to past recording data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The recording unit is, It estimates the user's emotions and adjusts the recording frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The recording unit is, During recording, the recorded data is weighted based on when happiness levels were measured. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned utilization unit is, The system estimates user sentiment and adjusts how the local currency is used based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned utilization unit is, When using the system, the system will refer to the user's past local currency usage history to select the most suitable method of use. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned utilization unit is, When used, the way the local currency is used is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned utilization unit is, It estimates user sentiment and determines the priority of local currencies based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned utilization unit is, When using the system, the optimal method of using the local currency will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned utilization unit is, When using the system, we analyze users' social media activity and propose ways to utilize the local currency. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0203] 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. The analytics department analyzes users' daily behavior and social media posts, A measurement unit that measures happiness based on the analysis results obtained by the aforementioned analysis unit, Based on the measurement results obtained by the aforementioned measurement unit, the proposal unit proposes individual happiness promotion plans and solutions to social issues. Based on the plan proposed by the aforementioned proposal department, the promotion department will provide mental support and promote behavioral change, A recording unit that records the happiness level evaluation obtained by the measurement unit on a blockchain, The system includes a utilization unit that uses the happiness points recorded by the recording unit as local currency. A system characterized by the following features.

2. The aforementioned analysis unit is Using multimodal AI technology, we analyze users' daily behavior, social media posts, voice, facial expressions, and more. The system according to feature 1.

3. The aforementioned proposal section is, We propose individual well-being promotion plans and solutions to social issues. The system according to feature 1.

4. The aforementioned promotion unit is Promoting emotional support and behavioral change through natural dialogue with users The system according to feature 1.

5. The aforementioned recording unit is Record happiness levels using blockchain technology. The system according to feature 1.

6. The aforementioned utilization unit is, The happiness points earned will be used as local currency. The system according to feature 1.

7. The aforementioned analysis unit is We estimate user sentiment and adjust the analysis methods for daily behavior and social media posts based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned analysis unit is Analyze the user's past behavior history and select the optimal analysis algorithm. The system according to feature 1.

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