system

Abstract

The system according to this embodiment aims to enable the user to gain insights through dialogue with their future self. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a simulation unit, and a provision unit. The collection unit collects the user's current behavioral data and lifestyle. The analysis unit analyzes the data collected by the collection unit. The simulation unit simulates a dialogue with one's future self based on the results analyzed by the analysis unit. The provision unit provides messages and advice generated by the simulation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the means for a user to gain insights into their own future is limited, and it is difficult to enhance the motivation for self-growth and goal setting.

[0005] The system according to the embodiment aims to enable a user to gain insights through a conversation with their future self.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a simulation unit, and a provision unit. The data collection unit collects the user's current behavioral data and lifestyle. The analysis unit analyzes the data collected by the data collection unit. The simulation unit simulates a dialogue with one's future self based on the results of the analysis performed by the analysis unit. The provision unit provides messages and advice generated by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows the user to gain insights through dialogue with their future self. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls 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 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 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 future dialogue system according to an embodiment of the present invention is a system that simulates a dialogue with the user's future self based on the user's current data. The future dialogue system analyzes the user's current behavioral data and lifestyle, and provides messages and advice from the future self, allowing the user to gain insights into their current choices and actions. This enables the user to reaffirm their life goals and direction and increase their motivation. For example, the future dialogue system collects the user's current behavioral data and lifestyle. For example, data such as what activities the user does every day, what they eat, and what kind of exercise they do is collected. Next, the future dialogue system predicts the user's future state based on the collected data. For example, it predicts what kind of health condition the user will have in the future and what kind of career they will build if they continue their current lifestyle. Based on this prediction, messages and advice from the future self are generated. The generated messages and advice are provided to the user. For example, the future self may provide advice such as, "If you continue your current lifestyle, you may harm your health in the future, so you should increase your exercise." This enables the user to gain insights into their current choices and actions. Furthermore, the future dialogue system supports the user in reaffirming their future goals and direction and creating a concrete action plan. For example, the system may suggest specific exercise and diet plans to maintain health in the future. This allows users to take concrete actions toward their goals. The future dialogue system helps users reaffirm their life goals and direction, boosting their motivation. For instance, creating a concrete action plan to stay healthy in the future increases awareness of daily life and improves motivation. Similarly, in their careers, taking concrete steps toward future goals increases their motivation at work. This allows the future dialogue system to simulate conversations with their future selves based on the user's current data, gaining insights along the way.

[0029] The future dialogue system according to this embodiment comprises a data collection unit, an analysis unit, a simulation unit, and a data provision unit. The data collection unit collects the user's current behavioral data and lifestyle. For example, the data collection unit collects data such as the user's daily habits, work patterns, and hobbies. For example, the data collection unit can collect data such as what activities the user does on a daily basis, what they eat, and what kind of exercise they do. The data collection unit can also collect data such as the user's travel history and sleep patterns. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit predicts the user's future state based on the collected data. For example, the analysis unit can predict what kind of health condition the user will have in the future and what kind of career they will build if they continue their current lifestyle. The analysis unit can also provide a basis for simulating the user's future state based on the collected data. The simulation unit simulates a dialogue with one's future self based on the results analyzed by the analysis unit. For example, the simulation unit generates messages and advice from one's future self. For example, the simulation unit can generate advice that continuing current lifestyle habits could harm one's health in the future and therefore exercise should be increased. The simulation unit can also generate advice on what kind of career to build in the future. The delivery unit provides the messages and advice generated by the simulation unit. The delivery unit provides the generated messages and advice to the user. For example, the delivery unit can provide messages and advice from one's future self as text messages or voice advice. The delivery unit can also support the user in reaffirming their future goals and direction and creating concrete action plans. For example, the delivery unit can suggest specific exercise and diet plans to maintain health in the future. In this way, the future dialogue system according to the embodiment can simulate a dialogue with one's future self based on the user's current data and gain insights.

[0030] The data collection unit collects the user's current behavioral data and lifestyle. For example, it collects data on the user's daily habits, work patterns, and hobbies. Specifically, it can collect data on what activities the user engages in daily, what they eat, and what kind of exercise they do. This includes data from smartphones and wearable devices. For example, it can collect data such as the user's steps, distance traveled, and calories burned through smartphone apps. It can also collect health data such as heart rate, sleep patterns, and stress levels from wearable devices. Furthermore, it can collect work schedules and task progress from the user's calendar and task management apps. This allows the data collection unit to centrally collect detailed data on all aspects of the user's life and gain a comprehensive understanding of their lifestyle. The collected data is securely stored on a cloud server and made accessible to the analysis and simulation units. The frequency and accuracy of data collection can be adjusted according to the user's settings and needs, allowing for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit predicts the user's future state based on the collected data. Specifically, it can predict what kind of health condition the user will have in the future, what kind of career they will build, etc., if they continue their current lifestyle habits. The analysis uses AI to process data in real time and analyze the user's behavior patterns and health condition in detail. For example, the AI ​​analyzes the user's diet and exercise data to identify nutritional imbalances and deficiencies in exercise. It can also analyze sleep data to evaluate the quality and quantity of sleep and find areas for improvement. Furthermore, it can analyze work patterns and hobby data to evaluate stress levels and work efficiency. As a result, the analysis unit can predict how the user's current lifestyle will affect them in the future and suggest specific areas for improvement. The analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past health data, it can evaluate the impact of specific lifestyle habits on health and predict future risks. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The simulation unit simulates a dialogue with one's future self based on the results analyzed by the analysis unit. For example, the simulation unit generates messages and advice from one's future self. Specifically, it can generate advice such as, "If you continue your current lifestyle, you may harm your health in the future, so you should increase your exercise." The simulation uses generative AI to realistically reproduce the user's future state. For example, the generative AI generates multiple future health and career scenarios based on the user's current data and provides advice based on each scenario. This allows the user to concretely understand how different lifestyles and choices will affect them in the future. The simulation unit can collect user feedback and continuously improve the accuracy and realism of the simulation. For example, it updates the simulation model based on the user's actual actions and results, providing more realistic scenarios. Furthermore, the simulation unit can generate customized advice tailored to the user's goals and desires. This allows the simulation unit to provide specific and practical advice to the user, supporting them in achieving their future goals.

[0033] The service provider delivers messages and advice generated by the simulation service provider. For example, the service provider delivers the generated messages and advice to the user. Specifically, it can deliver messages and advice from your future self as text messages or voice advice. The service provider can also support the user in reaffirming their future goals and direction and creating concrete action plans. For example, it can suggest specific exercise and diet plans to maintain health in the future. The service provider can collect user feedback to continuously improve the accuracy and effectiveness of the advice in order to provide customized advice tailored to the user's preferences and lifestyle. For example, it can update the advice based on the user's actual actions and results, providing a more realistic and actionable plan. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to provide users with quick and reliable instructions and support them in achieving their future goals. In addition, the service provider regularly provides feedback on the user's progress toward the goals they have set, supporting them in maintaining their motivation. This allows the service provider to support users in continuously striving towards their goals and facilitate their eventual achievement.

[0034] The data collection unit can collect data on the user's lifestyle, work patterns, hobbies, and more. For example, the data collection unit can collect data on the user's lifestyle, such as sleep duration, diet, and exercise frequency. The data collection unit can also collect data on the user's work patterns, such as working hours, job content, and work stress levels. Furthermore, the data collection unit can collect data on the user's hobbies, such as the type of hobby, time spent on it, and satisfaction with it. By collecting data on the user's lifestyle, work patterns, and hobbies, the data collection unit enables detailed analysis. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle into AI and have AI perform the data collection.

[0035] The analysis unit can predict the user's future state based on the collected data. For example, the analysis unit can predict the user's future health state based on the collected data. For example, it can predict what kind of health state the user will have in the future if they continue their current lifestyle. The analysis unit can also predict the user's future career based on the collected data. For example, it can predict what kind of career the user will build in the future if they continue their current work patterns. The analysis unit can also make predictions about the user's future hobbies based on the collected data. For example, it can predict what kind of hobbies the user will have in the future if they continue their current hobbies. In this way, the analysis unit can provide a basis for simulating a dialogue with one's future self by predicting the user's future state. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform the prediction of the user's future state.

[0036] The simulation unit can generate messages and advice from your future self. For example, the simulation unit can generate a message from your future self stating that continuing your current lifestyle may harm your health in the future and that you should increase your exercise. The simulation unit can also generate advice from your future self stating that you should increase your exercise. For example, the simulation unit can generate advice on what kind of career to build in the future. The simulation unit can also perform simulations based on collected data in order to generate messages and advice from your future self. For example, the simulation unit can simulate the user's future state based on collected data and generate messages and advice based on the results. In this way, the simulation unit can provide the user with insights by generating messages and advice from their future self. Some or all of the above processing in the simulation unit may be performed using AI, for example, or not using AI. For example, the simulation unit can input collected data into AI and have the AI ​​perform the generation of messages and advice from their future self.

[0037] The service provider can deliver generated messages and advice to the user. For example, the service provider can deliver generated messages to the user. For example, the service provider can deliver a message from one's future self as a text message. The service provider can also deliver generated advice to the user. For example, the service provider can deliver advice from one's future self as voice advice. Furthermore, the service provider can adjust the timing and format of delivery based on collected data in order to deliver generated messages and advice to the user. For example, the service provider can deliver messages and advice at the optimal time according to the user's current situation. In this way, the service provider can enable the user to gain insights by delivering generated messages and advice to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input generated messages and advice into AI and have the AI ​​adjust the timing and format of delivery.

[0038] The service provider can support users in reaffirming their future goals and direction and in developing concrete action plans. For example, the service provider can support users in reaffirming their future goals. For example, the service provider can provide guidelines to help users clarify the goals they want to achieve in the future. The service provider can also support users in developing concrete action plans. For example, the service provider can provide tools for users to plan daily tasks and long-term projects. The service provider can also adjust the content of support based on collected data to help users reaffirm their future goals and direction and develop concrete action plans. For example, the service provider can provide optimal support according to the user's current situation. In this way, the service provider can increase user motivation by supporting users in reaffirming their future goals and direction and in developing concrete action plans. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input guidelines for users to reaffirm their future goals and direction into AI and have the AI ​​adjust the content of support.

[0039] The data collection unit may include an anonymization unit that anonymizes user data. The anonymization unit anonymizes, for example, the user's personal information. For example, the anonymization unit can mask personal information such as the user's name and address. The anonymization unit can also pseudo-anonymize user data. For example, the anonymization unit can replace user data with a unique identifier. The anonymization unit can also adjust the anonymization method based on the collected data in order to anonymize user data. For example, the anonymization unit can select the optimal anonymization method depending on the type and content of the user's data. In this way, the anonymization unit can protect privacy by anonymizing user data. Some or all of the above processing in the anonymization unit may be performed using, for example, AI, or not using AI. For example, the anonymization unit can input user data into AI and have the AI ​​select an anonymization method.

[0040] The data collection unit may include a security unit to ensure data security. The security unit may, for example, encrypt the collected data. For example, the security unit may protect the data using encryption technology when transmitting the data. The security unit may also protect the data using encryption technology when storing the data. Furthermore, the security unit may control access to the data. For example, the security unit may set access permissions to the data, so that only specific users can access the data. In addition, the security unit may adjust security measures based on the collected data to ensure data security. For example, the security unit may select the optimal security measures depending on the type and content of the data. In this way, the security unit can safely protect user data by ensuring data security. Some or all of the above processing in the security unit may be performed using AI, for example, or not using AI. For example, the security unit may input the collected data into AI and have the AI ​​select security measures.

[0041] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can analyze data such as the user's past travel history, meal records, and exercise habits. The data collection unit can also analyze the user's past data collection history. For example, the data collection unit can analyze the types and content of data collected in the past and select the optimal data collection method. The data collection unit can also select the types of data to collect at specific time periods based on the user's past behavioral data. For example, the data collection unit can optimize the timing of data collection based on the user's frequently performed behavioral patterns in the past. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0042] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can filter data based on the user's current lifestyle. For example, the data collection unit can prioritize the collection of data related to areas of interest that the user is currently interested in. The data collection unit can also collect only the necessary data and exclude unnecessary data according to the user's lifestyle. For example, the data collection unit can adjust the content of data collection in real time based on the user's current activity status. This allows the data collection unit to collect only the necessary data by filtering data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect data considering the user's geographical location information. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. The data collection unit can also collect highly relevant data based on the user's travel history. For example, the data collection unit can prioritize the collection of data related to places the user has visited in the past. The data collection unit can also prioritize the collection of data related to the travel destination if the user is traveling. For example, the data collection unit can collect data related to the user's activities at the travel destination. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0044] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the user's social media activity. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activity of the user's social media followers and friends and collect relevant data. For example, the data collection unit can collect relevant data based on information shared by the user's followers and friends. The data collection unit can also collect data related to topics the user has shown interest in on social media. For example, the data collection unit can collect data related to posts that the user has "liked" or commented on. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. Conversely, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can select an analysis algorithm according to the data category. For example, the analysis unit can apply a health-related analysis algorithm to health data. Similarly, the analysis unit can apply a work-related analysis algorithm to work data. For example, the analysis unit can apply a hobby-related analysis algorithm to hobby data. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select the analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the latest data. The analysis unit can also analyze current data based on past data. For example, the analysis unit can adjust the priority of analysis according to the data collection period. This allows the analysis unit to prioritize the analysis of the latest data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can adjust the order of analysis according to the relevance of the data. In this way, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0049] The simulation unit can improve the accuracy of the simulation by considering the interrelationships between data during the simulation. For example, the simulation unit can perform a simulation by considering the interrelationships between data. For example, the simulation unit can perform a simulation by considering the interrelationships between health data and exercise data. The simulation unit can also perform a simulation by considering the interrelationships between work data and lifestyle data. For example, the simulation unit can perform a simulation by considering the interrelationships between hobby data and social media data. In this way, the simulation unit can improve the accuracy of the simulation by considering the interrelationships between data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the interrelationships between data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0050] The simulation unit can perform simulations while considering the user's attribute information. For example, the simulation unit can perform simulations while considering the user's attribute information. For example, the simulation unit can perform simulations while considering the user's age and gender. The simulation unit can also perform simulations while considering the user's occupation and lifestyle. For example, the simulation unit can perform simulations while considering the user's health condition and exercise habits. In this way, the simulation unit can provide more personalized simulations by considering the user's attribute information. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the user's attribute information into a generating AI and have the generating AI perform the simulation.

[0051] The simulation unit can perform simulations while considering the geographical distribution of the data. For example, the simulation unit can perform simulations while considering the geographical distribution of the data. For example, if the user is in a specific region, the simulation unit can perform simulations based on data related to that region. The simulation unit can also perform simulations while considering geographically relevant data based on the user's travel history. For example, if the user is traveling, the simulation unit can perform simulations based on data related to the travel destination. In this way, the simulation unit can provide more realistic simulations by considering the geographical distribution of the data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the simulation.

[0052] The simulation unit can improve the accuracy of simulations by referring to relevant literature during the simulation process. For example, the simulation unit performs simulations by referring to relevant literature. For example, in health-related simulations, the simulation unit can refer to the latest medical literature. In work-related simulations, the simulation unit can also refer to the latest industry trends. For example, in hobby-related simulations, the simulation unit can refer to relevant research papers. In this way, the simulation unit can improve the accuracy of simulations by referring to relevant literature. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input relevant literature into a generating AI and have the generating AI perform the simulation.

[0053] The information delivery unit can adjust the level of detail provided based on the importance of the message or advice at the time of delivery. For example, the information delivery unit can adjust the level of detail based on the importance of the message or advice. For example, the information delivery unit can provide detailed explanations for high-importance messages or advice. Conversely, the information delivery unit can provide concise explanations for low-importance messages or advice. For example, the information delivery unit can determine the priority of delivery according to the importance of the message or advice. This allows the information delivery unit to provide information efficiently by adjusting the level of detail based on the importance of the message or advice. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the importance of the message or advice into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0054] The delivery unit can apply different delivery algorithms depending on the category of the message or advice at the time of delivery. For example, the delivery unit can select a delivery algorithm according to the category of the message or advice. For example, the delivery unit can apply a health-related delivery algorithm to health-related messages or advice. It can also apply a work-related delivery algorithm to work-related messages or advice. For example, the delivery unit can apply a hobby-related delivery algorithm to hobby-related messages or advice. In this way, the delivery unit can provide more appropriate information by applying different delivery algorithms depending on the category of the message or advice. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the category of the message or advice into a generating AI and have the generating AI select a delivery algorithm.

[0055] The service provider can determine the priority of service delivery based on the generation time of messages and advice. For example, the service provider can prioritize the delivery of the latest messages and advice. The service provider can also provide current messages and advice based on past messages and advice. For example, the service provider can adjust the priority of service delivery according to the generation time of messages and advice. This allows the service provider to prioritize the delivery of the latest information by determining the priority of service delivery based on the generation time of messages and advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generation time of messages and advice into a generation AI and have the generation AI perform the determination of the service delivery priority.

[0056] The delivery unit can adjust the order of delivery based on the relevance of messages and advice. For example, the delivery unit can prioritize the delivery of highly relevant messages and advice. It can also postpone less relevant messages and advice. For example, the delivery unit can adjust the order of delivery according to the relevance of messages and advice. This allows the delivery unit to prioritize the delivery of information that is important to the user by adjusting the order of delivery based on the relevance of messages and advice. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the relevance of messages and advice into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0057] The anonymization unit can adjust the level of detail of anonymization based on the importance of the data during the anonymization process. For example, the anonymization unit can perform detailed anonymization on high-importance data. It can also perform simplified anonymization on low-importance data. For example, the anonymization unit can determine the priority of anonymization according to the importance of the data. This allows the anonymization unit to perform efficient anonymization by adjusting the level of detail of anonymization based on the importance of the data. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of anonymization.

[0058] The anonymization unit can weight the anonymization process based on the data collection timing. For example, the anonymization unit can prioritize anonymizing the most recent data. It can also anonymize current data based on past data. For example, the anonymization unit can adjust the anonymization weighting according to the data collection timing. This allows the anonymization unit to perform efficient anonymization by weighting the anonymization process based on the data collection timing. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input the data collection timing into a generating AI and have the generating AI perform the anonymization weighting.

[0059] The security department can adjust the level of detail of security measures based on the importance of the data. For example, the security department can implement detailed security measures for high-importance data and simplified security measures for low-importance data. For example, the security department can prioritize security measures according to the importance of the data. This allows the security department to implement efficient security measures by adjusting the level of detail of security measures based on the importance of the data. Some or all of the above processes in the security department may be performed using AI, for example, or without AI. For example, the security department can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the security measures.

[0060] The security department can weight security measures based on the data collection timing. For example, the security department can prioritize security measures on the most recent data. The security department can also implement security measures on current data based on past data. For example, the security department can adjust the weighting of security measures according to the data collection timing. This allows the security department to implement efficient security measures by weighting security measures based on the data collection timing. Some or all of the above processing in the security department may be performed using AI, for example, or without AI. For example, the security department can input the data collection timing into a generating AI and have the generating AI perform the weighting of security measures.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The future dialogue system can further analyze the user's past behavioral data and generate messages and advice from their future self based on past successes and failures. For example, it can provide advice on maintaining health in the future based on past successful diet methods. It can also provide advice on future careers based on lessons learned from past failed projects. Furthermore, it can suggest future hobbies based on past interests. In this way, the future dialogue system can provide specific advice that leverages the user's past experiences.

[0063] The future dialogue system can further customize messages and advice from the user's future self based on the user's current life situation and areas of interest. For example, if the user is currently interested in health, the system can provide specific advice on health. If the user is interested in career, the system can provide specific advice on career. Furthermore, if the user is interested in hobbies, the system can provide specific advice on hobbies. In this way, the future dialogue system can provide specific advice tailored to the user's areas of interest.

[0064] The future dialogue system can also provide messages and advice from your future self, taking into account the user's geographical location. For example, if the user is in a specific region, it can provide health and career information relevant to that region. If the user is traveling, it can provide health and career advice related to their destination. Furthermore, if the user is on the move, it can suggest hobbies related to their destination. In this way, the future dialogue system can provide specific advice based on the user's geographical location.

[0065] The future dialogue system can further analyze the user's social media activity and provide messages and advice from their future self based on their interests and trends on social media. For example, if a user frequently posts about health on social media, the system can provide specific health advice. Similarly, if a user frequently posts about their career, the system can provide specific career advice. Furthermore, if a user frequently posts about their hobbies, the system can provide specific advice on their hobbies. In this way, the future dialogue system can provide specific advice based on the user's social media activity.

[0066] The future dialogue system can further predict future risks based on the user's current data and propose countermeasures against those risks. For example, it can predict potential health risks that may arise if the current lifestyle continues and propose specific exercise and diet plans to mitigate those risks. It can also predict potential career risks that may arise if the current career path continues and provide advice on skill development or job changes to avoid those risks. Furthermore, it can predict potential hobby-related risks that may arise if the current hobbies and lifestyle continue and provide specific suggestions to mitigate those risks. In this way, the future dialogue system can support users in predicting future risks and taking concrete countermeasures.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects the user's current behavioral data and lifestyle. For example, it collects data such as the user's daily habits, work patterns, hobbies, travel history, and sleep patterns. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it predicts future health status and career prospects if current lifestyle habits are continued. Step 3: The simulation unit simulates a dialogue with your future self based on the results analyzed by the analysis unit. For example, it generates messages and advice from your future self. Step 4: The delivery unit provides messages and advice generated by the simulation unit. For example, it provides these to the user as text messages or voice advice to support concrete action plans.

[0069] (Example of form 2) The future dialogue system according to an embodiment of the present invention is a system that simulates a dialogue with the user's future self based on the user's current data. The future dialogue system analyzes the user's current behavioral data and lifestyle, and provides messages and advice from the future self, allowing the user to gain insights into their current choices and actions. This enables the user to reaffirm their life goals and direction and increase their motivation. For example, the future dialogue system collects the user's current behavioral data and lifestyle. For example, data such as what activities the user does every day, what they eat, and what kind of exercise they do is collected. Next, the future dialogue system predicts the user's future state based on the collected data. For example, it predicts what kind of health condition the user will have in the future and what kind of career they will build if they continue their current lifestyle. Based on this prediction, messages and advice from the future self are generated. The generated messages and advice are provided to the user. For example, the future self may provide advice such as, "If you continue your current lifestyle, you may harm your health in the future, so you should increase your exercise." This enables the user to gain insights into their current choices and actions. Furthermore, the future dialogue system supports the user in reaffirming their future goals and direction and creating a concrete action plan. For example, the system may suggest specific exercise and diet plans to maintain health in the future. This allows users to take concrete actions toward their goals. The future dialogue system helps users reaffirm their life goals and direction, boosting their motivation. For instance, creating a concrete action plan to stay healthy in the future increases awareness of daily life and improves motivation. Similarly, in their careers, taking concrete steps toward future goals increases their motivation at work. This allows the future dialogue system to simulate conversations with their future selves based on the user's current data, gaining insights along the way.

[0070] The future dialogue system according to this embodiment comprises a data collection unit, an analysis unit, a simulation unit, and a data provision unit. The data collection unit collects the user's current behavioral data and lifestyle. For example, the data collection unit collects data such as the user's daily habits, work patterns, and hobbies. For example, the data collection unit can collect data such as what activities the user does on a daily basis, what they eat, and what kind of exercise they do. The data collection unit can also collect data such as the user's travel history and sleep patterns. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit predicts the user's future state based on the collected data. For example, the analysis unit can predict what kind of health condition the user will have in the future and what kind of career they will build if they continue their current lifestyle. The analysis unit can also provide a basis for simulating the user's future state based on the collected data. The simulation unit simulates a dialogue with one's future self based on the results analyzed by the analysis unit. For example, the simulation unit generates messages and advice from one's future self. For example, the simulation unit can generate advice that continuing current lifestyle habits could harm one's health in the future and therefore exercise should be increased. The simulation unit can also generate advice on what kind of career to build in the future. The delivery unit provides the messages and advice generated by the simulation unit. The delivery unit provides the generated messages and advice to the user. For example, the delivery unit can provide messages and advice from one's future self as text messages or voice advice. The delivery unit can also support the user in reaffirming their future goals and direction and creating concrete action plans. For example, the delivery unit can suggest specific exercise and diet plans to maintain health in the future. In this way, the future dialogue system according to the embodiment can simulate a dialogue with one's future self based on the user's current data and gain insights.

[0071] The data collection unit collects the user's current behavioral data and lifestyle. For example, it collects data on the user's daily habits, work patterns, and hobbies. Specifically, it can collect data on what activities the user engages in daily, what they eat, and what kind of exercise they do. This includes data from smartphones and wearable devices. For example, it can collect data such as the user's steps, distance traveled, and calories burned through smartphone apps. It can also collect health data such as heart rate, sleep patterns, and stress levels from wearable devices. Furthermore, it can collect work schedules and task progress from the user's calendar and task management apps. This allows the data collection unit to centrally collect detailed data on all aspects of the user's life and gain a comprehensive understanding of their lifestyle. The collected data is securely stored on a cloud server and made accessible to the analysis and simulation units. The frequency and accuracy of data collection can be adjusted according to the user's settings and needs, allowing for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit predicts the user's future state based on the collected data. Specifically, it can predict what kind of health condition the user will have in the future, what kind of career they will build, etc., if they continue their current lifestyle habits. The analysis uses AI to process data in real time and analyze the user's behavior patterns and health condition in detail. For example, the AI ​​analyzes the user's diet and exercise data to identify nutritional imbalances and deficiencies in exercise. It can also analyze sleep data to evaluate the quality and quantity of sleep and find areas for improvement. Furthermore, it can analyze work patterns and hobby data to evaluate stress levels and work efficiency. As a result, the analysis unit can predict how the user's current lifestyle will affect them in the future and suggest specific areas for improvement. The analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past health data, it can evaluate the impact of specific lifestyle habits on health and predict future risks. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0073] The simulation unit simulates a dialogue with one's future self based on the results analyzed by the analysis unit. For example, the simulation unit generates messages and advice from one's future self. Specifically, it can generate advice such as, "If you continue your current lifestyle, you may harm your health in the future, so you should increase your exercise." The simulation uses generative AI to realistically reproduce the user's future state. For example, the generative AI generates multiple future health and career scenarios based on the user's current data and provides advice based on each scenario. This allows the user to concretely understand how different lifestyles and choices will affect them in the future. The simulation unit can collect user feedback and continuously improve the accuracy and realism of the simulation. For example, it updates the simulation model based on the user's actual actions and results, providing more realistic scenarios. Furthermore, the simulation unit can generate customized advice tailored to the user's goals and desires. This allows the simulation unit to provide specific and practical advice to the user, supporting them in achieving their future goals.

[0074] The service provider delivers messages and advice generated by the simulation service provider. For example, the service provider delivers the generated messages and advice to the user. Specifically, it can deliver messages and advice from your future self as text messages or voice advice. The service provider can also support the user in reaffirming their future goals and direction and creating concrete action plans. For example, it can suggest specific exercise and diet plans to maintain health in the future. The service provider can collect user feedback to continuously improve the accuracy and effectiveness of the advice in order to provide customized advice tailored to the user's preferences and lifestyle. For example, it can update the advice based on the user's actual actions and results, providing a more realistic and actionable plan. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to provide users with quick and reliable instructions and support them in achieving their future goals. In addition, the service provider regularly provides feedback on the user's progress toward the goals they have set, supporting them in maintaining their motivation. This allows the service provider to support users in continuously striving towards their goals and facilitate their eventual achievement.

[0075] The data collection unit can collect data on the user's lifestyle, work patterns, hobbies, and more. For example, the data collection unit can collect data on the user's lifestyle, such as sleep duration, diet, and exercise frequency. The data collection unit can also collect data on the user's work patterns, such as working hours, job content, and work stress levels. Furthermore, the data collection unit can collect data on the user's hobbies, such as the type of hobby, time spent on it, and satisfaction with it. By collecting data on the user's lifestyle, work patterns, and hobbies, the data collection unit enables detailed analysis. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle into AI and have AI perform the data collection.

[0076] The analysis unit can predict the user's future state based on the collected data. For example, the analysis unit can predict the user's future health state based on the collected data. For example, it can predict what kind of health state the user will have in the future if they continue their current lifestyle. The analysis unit can also predict the user's future career based on the collected data. For example, it can predict what kind of career the user will build in the future if they continue their current work patterns. The analysis unit can also make predictions about the user's future hobbies based on the collected data. For example, it can predict what kind of hobbies the user will have in the future if they continue their current hobbies. In this way, the analysis unit can provide a basis for simulating a dialogue with one's future self by predicting the user's future state. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform the prediction of the user's future state.

[0077] The simulation unit can generate messages and advice from your future self. For example, the simulation unit can generate a message from your future self stating that continuing your current lifestyle may harm your health in the future and that you should increase your exercise. The simulation unit can also generate advice from your future self stating that you should increase your exercise. For example, the simulation unit can generate advice on what kind of career to build in the future. The simulation unit can also perform simulations based on collected data in order to generate messages and advice from your future self. For example, the simulation unit can simulate the user's future state based on collected data and generate messages and advice based on the results. In this way, the simulation unit can provide the user with insights by generating messages and advice from their future self. Some or all of the above processing in the simulation unit may be performed using AI, for example, or not using AI. For example, the simulation unit can input collected data into AI and have the AI ​​perform the generation of messages and advice from their future self.

[0078] The service provider can deliver generated messages and advice to the user. For example, the service provider can deliver generated messages to the user. For example, the service provider can deliver a message from one's future self as a text message. The service provider can also deliver generated advice to the user. For example, the service provider can deliver advice from one's future self as voice advice. Furthermore, the service provider can adjust the timing and format of delivery based on collected data in order to deliver generated messages and advice to the user. For example, the service provider can deliver messages and advice at the optimal time according to the user's current situation. In this way, the service provider can enable the user to gain insights by delivering generated messages and advice to the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input generated messages and advice into AI and have the AI ​​adjust the timing and format of delivery.

[0079] The service provider can support users in reaffirming their future goals and direction and in developing concrete action plans. For example, the service provider can support users in reaffirming their future goals. For example, the service provider can provide guidelines to help users clarify the goals they want to achieve in the future. The service provider can also support users in developing concrete action plans. For example, the service provider can provide tools for users to plan daily tasks and long-term projects. The service provider can also adjust the content of support based on collected data to help users reaffirm their future goals and direction and develop concrete action plans. For example, the service provider can provide optimal support according to the user's current situation. In this way, the service provider can increase user motivation by supporting users in reaffirming their future goals and direction and in developing concrete action plans. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input guidelines for users to reaffirm their future goals and direction into AI and have the AI ​​adjust the content of support.

[0080] The data collection unit may include an anonymization unit that anonymizes user data. The anonymization unit anonymizes, for example, the user's personal information. For example, the anonymization unit can mask personal information such as the user's name and address. The anonymization unit can also pseudo-anonymize user data. For example, the anonymization unit can replace user data with a unique identifier. The anonymization unit can also adjust the anonymization method based on the collected data in order to anonymize user data. For example, the anonymization unit can select the optimal anonymization method depending on the type and content of the user's data. In this way, the anonymization unit can protect privacy by anonymizing user data. Some or all of the above processing in the anonymization unit may be performed using, for example, AI, or not using AI. For example, the anonymization unit can input user data into AI and have the AI ​​select an anonymization method.

[0081] The data collection unit may include a security unit to ensure data security. The security unit may, for example, encrypt the collected data. For example, the security unit may protect the data using encryption technology when transmitting the data. The security unit may also protect the data using encryption technology when storing the data. Furthermore, the security unit may control access to the data. For example, the security unit may set access permissions to the data, so that only specific users can access the data. In addition, the security unit may adjust security measures based on the collected data to ensure data security. For example, the security unit may select the optimal security measures depending on the type and content of the data. In this way, the security unit can safely protect user data by ensuring data security. Some or all of the above processing in the security unit may be performed using AI, for example, or not using AI. For example, the security unit may input the collected data into AI and have the AI ​​select security measures.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Alternatively, the data collection unit can estimate the user's emotions using speech analysis technology. For example, it can record the user's voice and estimate the emotions using a speech analysis algorithm. Furthermore, the data collection unit can estimate emotions using the user's biometric data. For example, it can collect the user's heart rate and skin electrical activity with sensors and estimate the emotions using an emotion estimation algorithm. This allows the data collection unit to reduce the user's burden by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0083] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can analyze data such as the user's past travel history, meal records, and exercise habits. The data collection unit can also analyze the user's past data collection history. For example, the data collection unit can analyze the types and content of data collected in the past and select the optimal data collection method. The data collection unit can also select the types of data to collect at specific time periods based on the user's past behavioral data. For example, the data collection unit can optimize the timing of data collection based on the user's frequently performed behavioral patterns in the past. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0084] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can filter data based on the user's current lifestyle. For example, the data collection unit can prioritize the collection of data related to areas of interest that the user is currently interested in. The data collection unit can also collect only the necessary data and exclude unnecessary data according to the user's lifestyle. For example, the data collection unit can adjust the content of data collection in real time based on the user's current activity status. This allows the data collection unit to collect only the necessary data by filtering data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0085] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, the data collection unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Alternatively, the data collection unit can estimate the user's emotions using speech analysis technology. For example, it can record the user's voice and estimate the emotions using a speech analysis algorithm. Furthermore, the data collection unit can estimate emotions using the user's biometric data. For example, it can collect the user's heart rate and skin electrical activity with sensors and estimate the emotions using an emotion estimation algorithm. This allows the data collection unit to collect more appropriate data by prioritizing the data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0086] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect data considering the user's geographical location information. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. The data collection unit can also collect highly relevant data based on the user's travel history. For example, the data collection unit can prioritize the collection of data related to places the user has visited in the past. The data collection unit can also prioritize the collection of data related to the travel destination if the user is traveling. For example, the data collection unit can collect data related to the user's activities at the travel destination. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0087] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the user's social media activity. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activity of the user's social media followers and friends and collect relevant data. For example, the data collection unit can collect relevant data based on information shared by the user's followers and friends. The data collection unit can also collect data related to topics the user has shown interest in on social media. For example, the data collection unit can collect data related to posts that the user has "liked" or commented on. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can use facial recognition technology to estimate the user's emotions. For instance, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using speech analysis technology. For example, the analysis unit can record the user's voice and estimate the emotions using a speech analysis algorithm. The analysis unit can also estimate emotions using the user's biometric data. For example, the analysis unit can collect the user's heart rate and skin electrical activity with sensors and estimate the emotions using an emotion estimation algorithm. This allows the analysis unit to provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. Conversely, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can select an analysis algorithm according to the data category. For example, the analysis unit can apply a health-related analysis algorithm to health data. Similarly, the analysis unit can apply a work-related analysis algorithm to work data. For example, the analysis unit can apply a hobby-related analysis algorithm to hobby data. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select the analysis algorithm.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using speech analysis technology. For example, it can record the user's voice and estimate the emotions using a speech analysis algorithm. Furthermore, the analysis unit can estimate emotions using the user's biometric data. For example, it can collect the user's heart rate and skin electrical activity with sensors and estimate the emotions using an emotion estimation algorithm. This allows the analysis unit to provide the user with an analysis result of an appropriate length by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0092] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the latest data. The analysis unit can also analyze current data based on past data. For example, the analysis unit can adjust the priority of analysis according to the data collection period. This allows the analysis unit to prioritize the analysis of the latest data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can adjust the order of analysis according to the relevance of the data. In this way, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0094] The simulation unit can estimate the user's emotions and adjust the simulation criteria based on the estimated emotions. For example, the simulation unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Alternatively, the simulation unit can estimate the user's emotions using voice analysis technology. For example, it can record the user's voice and estimate the emotions using a voice analysis algorithm. Furthermore, the simulation unit can estimate emotions using the user's biometric data. For example, it can collect the user's heart rate and skin electrical activity with sensors and estimate the emotions using an emotion estimation algorithm. This allows the simulation unit to provide an appropriate simulation for the user by adjusting the simulation criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0095] The simulation unit can improve the accuracy of the simulation by considering the interrelationships between data during the simulation. For example, the simulation unit can perform a simulation by considering the interrelationships between data. For example, the simulation unit can perform a simulation by considering the interrelationships between health data and exercise data. The simulation unit can also perform a simulation by considering the interrelationships between work data and lifestyle data. For example, the simulation unit can perform a simulation by considering the interrelationships between hobby data and social media data. In this way, the simulation unit can improve the accuracy of the simulation by considering the interrelationships between data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the interrelationships between data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0096] The simulation unit can perform simulations while considering the user's attribute information. For example, the simulation unit can perform simulations while considering the user's attribute information. For example, the simulation unit can perform simulations while considering the user's age and gender. The simulation unit can also perform simulations while considering the user's occupation and lifestyle. For example, the simulation unit can perform simulations while considering the user's health condition and exercise habits. In this way, the simulation unit can provide more personalized simulations by considering the user's attribute information. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the user's attribute information into a generating AI and have the generating AI perform the simulation.

[0097] The simulation unit can estimate the user's emotions and adjust the order in which the simulation results are displayed based on the estimated emotions. For example, the simulation unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Alternatively, the simulation unit can estimate the user's emotions using speech analysis technology. For example, it can record the user's voice and estimate the emotions using a speech analysis algorithm. Furthermore, the simulation unit can estimate emotions using the user's biometric data. For example, it can collect the user's heart rate and skin electrical activity with sensors and estimate the emotions using an emotion estimation algorithm. This allows the simulation unit to provide results that are easy for the user to understand by adjusting the order in which the simulation results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0098] The simulation unit can perform simulations while considering the geographical distribution of the data. For example, the simulation unit can perform simulations while considering the geographical distribution of the data. For example, if the user is in a specific region, the simulation unit can perform simulations based on data related to that region. The simulation unit can also perform simulations while considering geographically relevant data based on the user's travel history. For example, if the user is traveling, the simulation unit can perform simulations based on data related to the travel destination. In this way, the simulation unit can provide more realistic simulations by considering the geographical distribution of the data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the simulation.

[0099] The simulation unit can improve the accuracy of simulations by referring to relevant literature during the simulation process. For example, the simulation unit performs simulations by referring to relevant literature. For example, in health-related simulations, the simulation unit can refer to the latest medical literature. In work-related simulations, the simulation unit can also refer to the latest industry trends. For example, in hobby-related simulations, the simulation unit can refer to relevant research papers. In this way, the simulation unit can improve the accuracy of simulations by referring to relevant literature. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input relevant literature into a generating AI and have the generating AI perform the simulation.

[0100] The service provider can estimate the user's emotions and adjust the way messages and advice are expressed based on the estimated emotions. For example, the service provider can use facial recognition technology to estimate the user's emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The service provider can also estimate the user's emotions using voice analysis technology. For example, the service provider can record the user's voice and estimate the emotions using a voice analysis algorithm. The service provider can also estimate emotions using the user's biometric data. For example, the service provider can collect the user's heart rate and skin electrical activity with sensors and estimate the emotions using an emotion estimation algorithm. As a result, the service provider can provide messages and advice that are easy for the user to understand by adjusting the way messages and advice are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0101] The information delivery unit can adjust the level of detail provided based on the importance of the message or advice at the time of delivery. For example, the information delivery unit can adjust the level of detail based on the importance of the message or advice. For example, the information delivery unit can provide detailed explanations for high-importance messages or advice. Conversely, the information delivery unit can provide concise explanations for low-importance messages or advice. For example, the information delivery unit can determine the priority of delivery according to the importance of the message or advice. This allows the information delivery unit to provide information efficiently by adjusting the level of detail based on the importance of the message or advice. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the importance of the message or advice into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0102] The delivery unit can apply different delivery algorithms depending on the category of the message or advice at the time of delivery. For example, the delivery unit can select a delivery algorithm according to the category of the message or advice. For example, the delivery unit can apply a health-related delivery algorithm to health-related messages or advice. It can also apply a work-related delivery algorithm to work-related messages or advice. For example, the delivery unit can apply a hobby-related delivery algorithm to hobby-related messages or advice. In this way, the delivery unit can provide more appropriate information by applying different delivery algorithms depending on the category of the message or advice. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the category of the message or advice into a generating AI and have the generating AI select a delivery algorithm.

[0103] The service provider can estimate the user's emotions and adjust the length of messages and advice based on the estimated emotions. For example, the service provider can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Alternatively, the service provider can estimate the user's emotions using voice analysis technology. For example, it can record the user's voice and estimate the emotions using a voice analysis algorithm. Furthermore, the service provider can estimate emotions using the user's biometric data. For example, it can collect the user's heart rate and skin electrical activity with sensors and estimate the emotions using an emotion estimation algorithm. This allows the service provider to provide information of an appropriate length to the user by adjusting the length of messages and advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0104] The service provider can determine the priority of service delivery based on the generation time of messages and advice. For example, the service provider can prioritize the delivery of the latest messages and advice. The service provider can also provide current messages and advice based on past messages and advice. For example, the service provider can adjust the priority of service delivery according to the generation time of messages and advice. This allows the service provider to prioritize the delivery of the latest information by determining the priority of service delivery based on the generation time of messages and advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generation time of messages and advice into a generation AI and have the generation AI perform the determination of the service delivery priority.

[0105] The delivery unit can adjust the order of delivery based on the relevance of messages and advice. For example, the delivery unit can prioritize the delivery of highly relevant messages and advice. It can also postpone less relevant messages and advice. For example, the delivery unit can adjust the order of delivery according to the relevance of messages and advice. This allows the delivery unit to prioritize the delivery of information that is important to the user by adjusting the order of delivery based on the relevance of messages and advice. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the relevance of messages and advice into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0106] The anonymization unit can estimate the user's emotions and adjust the anonymization method based on the estimated emotions. For example, the anonymization unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The anonymization unit can also estimate the user's emotions using speech analysis technology. For example, it can record the user's voice and estimate the emotions using a speech analysis algorithm. Furthermore, the anonymization unit can estimate emotions using the user's biometric data. For example, it can collect the user's heart rate and skin electrical activity with sensors and estimate the emotions using an emotion estimation algorithm. This allows the anonymization unit to better protect user privacy by adjusting the anonymization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input the user's facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0107] The anonymization unit can adjust the level of detail of anonymization based on the importance of the data during the anonymization process. For example, the anonymization unit can perform detailed anonymization on high-importance data. It can also perform simplified anonymization on low-importance data. For example, the anonymization unit can determine the priority of anonymization according to the importance of the data. This allows the anonymization unit to perform efficient anonymization by adjusting the level of detail of anonymization based on the importance of the data. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of anonymization.

[0108] The anonymization unit can estimate the user's emotions and determine the anonymization priority based on the estimated emotions. For example, the anonymization unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The anonymization unit can also estimate the user's emotions using voice analysis technology. For example, it can record the user's voice and estimate the emotions using a voice analysis algorithm. Furthermore, the anonymization unit can estimate emotions using the user's biometric data. For example, it can collect the user's heart rate and skin electrical activity with sensors and estimate the emotions using an emotion estimation algorithm. This allows the anonymization unit to better protect user privacy by determining the anonymization priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input the user's facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0109] The anonymization unit can weight the anonymization process based on the data collection timing. For example, the anonymization unit can prioritize anonymizing the most recent data. It can also anonymize current data based on past data. For example, the anonymization unit can adjust the anonymization weighting according to the data collection timing. This allows the anonymization unit to perform efficient anonymization by weighting the anonymization process based on the data collection timing. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input the data collection timing into a generating AI and have the generating AI perform the anonymization weighting.

[0110] The security unit can estimate the user's emotions and adjust security measures based on those estimated emotions. For example, the security unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The security unit can also estimate the user's emotions using voice analysis technology. For example, it can record the user's voice and estimate their emotions using a voice analysis algorithm. Furthermore, the security unit can estimate emotions using the user's biometric data. For example, it can collect the user's heart rate and skin electrical activity with sensors and estimate their emotions using an emotion estimation algorithm. This allows the security unit to better protect user data by adjusting security measures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0111] The security department can adjust the level of detail of security measures based on the importance of the data. For example, the security department can implement detailed security measures for high-importance data and simplified security measures for low-importance data. For example, the security department can prioritize security measures according to the importance of the data. This allows the security department to implement efficient security measures by adjusting the level of detail of security measures based on the importance of the data. Some or all of the above processes in the security department may be performed using AI, for example, or without AI. For example, the security department can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the security measures.

[0112] The security department can estimate the user's emotions and prioritize security measures based on those estimated emotions. For example, the security department can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The security department can also estimate the user's emotions using voice analysis technology. For example, it can record the user's voice and estimate their emotions using a voice analysis algorithm. Furthermore, the security department can estimate emotions using the user's biometric data. For example, it can collect the user's heart rate and skin electrical activity with sensors and estimate their emotions using an emotion estimation algorithm. This allows the security department to better protect user data by prioritizing security measures based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0113] The security department can weight security measures based on the data collection timing. For example, the security department can prioritize security measures on the most recent data. The security department can also implement security measures on current data based on past data. For example, the security department can adjust the weighting of security measures according to the data collection timing. This allows the security department to implement efficient security measures by weighting security measures based on the data collection timing. Some or all of the above processing in the security department may be performed using AI, for example, or without AI. For example, the security department can input the data collection timing into a generating AI and have the generating AI perform the weighting of security measures.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The future dialogue system can further estimate the user's emotions and adjust the content of messages and advice from their future self based on those estimated emotions. For example, if the user is feeling stressed, the system can offer advice on how to relax. If the user wants to increase their motivation, the system can offer encouraging messages. Furthermore, if the user is feeling anxious, the system can offer reassuring messages. In this way, the future dialogue system can provide appropriate messages and advice tailored to the user's emotions.

[0116] The future dialogue system can further analyze the user's past behavioral data and generate messages and advice from their future self based on past successes and failures. For example, it can provide advice on maintaining health in the future based on past successful diet methods. It can also provide advice on future careers based on lessons learned from past failed projects. Furthermore, it can suggest future hobbies based on past interests. In this way, the future dialogue system can provide specific advice that leverages the user's past experiences.

[0117] The future dialogue system can further estimate the user's emotions and adjust the way messages and advice from their future self are expressed based on those estimated emotions. For example, if the user is feeling down, the system can offer encouraging messages in gentle words. If the user is excited, the system can offer calm advice. Furthermore, if the user is tired, the system can offer advice on how to relax. In this way, the future dialogue system can deliver messages and advice in an appropriate manner according to the user's emotions.

[0118] The future dialogue system can further customize messages and advice from the user's future self based on the user's current life situation and areas of interest. For example, if the user is currently interested in health, the system can provide specific advice on health. If the user is interested in career, the system can provide specific advice on career. Furthermore, if the user is interested in hobbies, the system can provide specific advice on hobbies. In this way, the future dialogue system can provide specific advice tailored to the user's areas of interest.

[0119] The future dialogue system can further estimate the user's emotions and adjust the timing of messages and advice from their future self based on those estimated emotions. For example, if the user is feeling stressed, the system can immediately provide advice on how to relax. If the user wants to increase their motivation, the system can provide encouraging messages at the appropriate time. Furthermore, if the user is feeling anxious, the system can provide reassuring messages at the appropriate time. In this way, the future dialogue system can provide messages and advice at the right time in accordance with the user's emotions.

[0120] The future dialogue system can also provide messages and advice from your future self, taking into account the user's geographical location. For example, if the user is in a specific region, it can provide health and career information relevant to that region. If the user is traveling, it can provide health and career advice related to their destination. Furthermore, if the user is on the move, it can suggest hobbies related to their destination. In this way, the future dialogue system can provide specific advice based on the user's geographical location.

[0121] The future dialogue system can further estimate the user's emotions and adjust the length of messages and advice from their future self based on those estimated emotions. For example, if the user is tired, the system can provide short, concise advice. If the user is relaxed, the system can provide detailed advice. Furthermore, if the user is excited, the system can provide calm advice. In this way, the future dialogue system can provide messages and advice of appropriate length according to the user's emotions.

[0122] The future dialogue system can further analyze the user's social media activity and provide messages and advice from their future self based on their interests and trends on social media. For example, if a user frequently posts about health on social media, the system can provide specific health advice. Similarly, if a user frequently posts about their career, the system can provide specific career advice. Furthermore, if a user frequently posts about their hobbies, the system can provide specific advice on their hobbies. In this way, the future dialogue system can provide specific advice based on the user's social media activity.

[0123] The future dialogue system can further estimate the user's emotions and prioritize messages and advice from their future self based on those estimated emotions. For example, if the user is feeling stressed, the system can prioritize providing advice to help them relax. If the user wants to increase their motivation, the system can prioritize providing encouraging messages. Furthermore, if the user is feeling anxious, the system can prioritize providing messages that offer reassurance. In this way, the future dialogue system can provide messages and advice with appropriate priorities according to the user's emotions.

[0124] The future dialogue system can further predict future risks based on the user's current data and propose countermeasures against those risks. For example, it can predict potential health risks that may arise if the current lifestyle continues and propose specific exercise and diet plans to mitigate those risks. It can also predict potential career risks that may arise if the current career path continues and provide advice on skill development or job changes to avoid those risks. Furthermore, it can predict potential hobby-related risks that may arise if the current hobbies and lifestyle continue and provide specific suggestions to mitigate those risks. In this way, the future dialogue system can support users in predicting future risks and taking concrete countermeasures.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The data collection unit collects the user's current behavioral data and lifestyle. For example, it collects data such as the user's daily habits, work patterns, hobbies, travel history, and sleep patterns. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it predicts future health status and career prospects if current lifestyle habits are continued. Step 3: The simulation unit simulates a dialogue with your future self based on the results analyzed by the analysis unit. For example, it generates messages and advice from your future self. Step 4: The delivery unit provides messages and advice generated by the simulation unit. For example, it provides these to the user as text messages or voice advice to support concrete action plans.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, simulation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user behavior data and lifestyle data using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and predicts future states. The simulation unit simulates a conversation with one's future self using the specific processing unit 290 of the data processing unit 12 and generates messages and advice. The provision unit provides the user with the messages and advice generated by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, simulation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user behavior data and lifestyle data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and predicts future states. The simulation unit simulates a conversation with one's future self using the specific processing unit 290 of the data processing unit 12 and generates messages and advice. The provision unit provides the user with the messages and advice generated by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, simulation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user behavior data and lifestyle data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and predicts future states. The simulation unit simulates a conversation with one's future self using the specific processing unit 290 of the data processing unit 12 and generates messages and advice. The provision unit provides the user with the messages and advice generated by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the collection unit, analysis unit, simulation unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects user behavior data and lifestyle data using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and predicts future states. The simulation unit simulates a conversation with one's future self using the specific processing unit 290 of the data processing unit 12 and generates messages and advice. The provision unit provides the user with the messages and advice generated by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) A data collection unit that collects the user's current behavioral data and lifestyle, An analysis unit analyzes the data collected by the aforementioned collection unit, A simulation unit simulates a dialogue with one's future self based on the results analyzed by the aforementioned analysis unit, The system includes a providing unit that provides messages and advice generated by the simulation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on users' lifestyles, work patterns, hobbies, and more. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, predict the user's future state. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned simulation unit, Generate messages and advice from your future self. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides users with generated messages and advice. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We support users in reaffirming their future goals and direction, and in creating concrete action plans. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It includes an anonymization unit that anonymizes user data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It includes a security unit to ensure data security. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze users' past behavioral data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, During simulation, consider the interrelationships between data to improve the accuracy of the simulation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, During the simulation, the user's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, It estimates the user's emotions and adjusts the order in which the simulation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, During the simulation, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned simulation unit, During simulations, we refer to relevant literature to improve the accuracy of the simulations. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way messages and advice are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, adjust the level of detail based on the importance of the message or advice. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing content, different delivery algorithms are applied depending on the category of the message or advice. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of messages and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing content, we prioritize its delivery based on when the message or advice was generated. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When delivering messages and advice, the order of delivery will be adjusted based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 33) The anonymization unit is, The system estimates the user's emotions and adjusts the anonymization method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The anonymization unit is, During anonymization, adjust the level of anonymization based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The anonymization unit is, The system estimates the user's emotions and determines the priority of anonymization based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The anonymization unit is, During anonymization, the weighting of the anonymization process is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned security unit is It estimates user sentiment and adjusts security measures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned security unit is When implementing security measures, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned security unit is It estimates user sentiment and prioritizes security measures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned security unit is When implementing security measures, weight the security measures based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects the user's current behavioral data and lifestyle, An analysis unit analyzes the data collected by the aforementioned collection unit, A simulation unit simulates a dialogue with one's future self based on the results analyzed by the aforementioned analysis unit, The system includes a providing unit that provides messages and advice generated by the simulation unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on users' lifestyles, work patterns, hobbies, and more. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, predict the user's future state. The system according to feature 1.

4. The aforementioned simulation unit, Generate messages and advice from your future self. The system according to feature 1.

5. The aforementioned supply unit is, Provides users with generated messages and advice. The system according to feature 1.

6. The aforementioned supply unit is, We support users in reaffirming their future goals and direction, and in creating concrete action plans. The system according to feature 1.

7. The aforementioned collection unit is It includes an anonymization unit that anonymizes user data. The system according to feature 1.

8. The aforementioned collection unit is It includes a security unit to ensure data security. The system according to feature 1.

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