system

Abstract

The system according to this embodiment aims to create concrete action plans for an individual's dreams and goals and to effectively manage their progress. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a visualization unit, and a dialogue unit. The analysis unit analyzes the current situation. The generation unit creates an action plan based on the information analyzed by the analysis unit. The visualization unit visualizes the progress of the action plan created by the generation unit. The dialogue unit performs in-depth analysis and adjustments in a dialogue format based on the progress visualized by the visualization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to create a specific action plan for an individual's dreams and goals and effectively manage the progress thereof.

[0005] The system according to the embodiment aims to create a specific action plan for an individual's dreams and goals and effectively manage the progress thereof.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a visualization unit, and a dialogue unit. The analysis unit analyzes the current situation. The generation unit creates an action plan based on the information analyzed by the analysis unit. The visualization unit visualizes the progress of the action plan created by the generation unit. The dialogue unit performs in-depth analysis and adjustments in a dialogue format based on the progress visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can create concrete action plans for an individual's dreams and goals and effectively manage their progress. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 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 action plan and coaching service system according to an embodiment of the present invention is a system that provides action plans and coaching services using AI for people who have dreams or things they want to do. This system starts with the user inputting basic information such as their current situation, goals, and daily routine. Next, the AI ​​analyzes this information and creates a specific action plan tailored to the individual. This action plan is reminded on a calendar, and the overall progress rate is visualized. The user takes action based on the plan, and if there are any issues with the completion rate of the to-do list, things they have noticed, or things they want to discuss, they can deepen their understanding and make adjustments while interacting with the AI. By repeating this process, the user can get closer to self-realization. This service is expected to not only bring users closer to realizing their dreams and things they want to do, but also to improve their confidence and motivation through successful experiences. In particular, it is a very beneficial service for working adults who are tired from work and unable to do what they want to do. For example, if a user has the goal of "becoming fluent in English within a year," the AI ​​analyzes the user's current English ability and daily schedule and creates a specific learning plan. Daily learning content and progress are reminded on a calendar, and the user proceeds with learning based on the plan. If users encounter difficulties or need advice along the way, they can find solutions by interacting with the AI. In this way, users can progress towards their goals at their own pace and ultimately move closer to self-realization. Furthermore, accumulating successful experiences will improve their confidence and motivation, and increase their desire to take on further challenges. This service can be offered to a wide range of users by adopting business models such as a freemium model, affiliate income, and B2B development. In particular, it targets working adults in their 20s to 50s, and will be a very beneficial service for people who are tired from work and unable to do what they want. In addition, by utilizing generative AI, users can quickly gather information in a conversational format, organize, process, and delve deeper into it, allowing them to design their own lives and live more fulfilling lives. Socially, many people are interested in "AI x ○○" and this service has the potential to make an impact on society.Through this service, we aim to prove that what you want to do can be achieved (or brought closer to achieving it) by breaking it down and tackling the challenges, and to help many people achieve self-realization. This will allow our action plan and coaching service system to support users in realizing their dreams and aspirations, and to improve their confidence and motivation through successful experiences.

[0029] The action plan and coaching service system according to this embodiment comprises an analysis unit, a generation unit, a visualization unit, and a dialogue unit. The analysis unit analyzes the user's current situation. The analysis unit analyzes basic information such as the user's current situation, goals, and daily routine, for example, entered by the user. The analysis unit can analyze the current situation in detail based on the user's lifestyle patterns and behavioral history, for example. The analysis unit can propose an optimal action plan, for example, taking into account the user's lifestyle patterns. The generation unit creates an action plan based on the information analyzed by the analysis unit. The generation unit creates a specific action plan tailored to the user's goals, for example. The generation unit can set specific daily tasks based on the user's lifestyle patterns and goals, for example. The generation unit can create an effective action plan, for example, taking into account the user's past successes. The visualization unit visualizes the progress of the action plan created by the generation unit. The visualization unit visually displays the progress of the action plan using, for example, a calendar or graph. The visualization unit can display, for example, the completion rate of a to-do list or a progress bar, making it easier for the user to check the progress. The visualization unit can adjust the level of detail displayed according to the user's progress toward achieving their goals. The dialogue unit can deepen the conversation and make adjustments based on the progress visualized by the visualization unit. The dialogue unit can resolve issues the user wants to discuss or problems they are facing through dialogue with the AI. The dialogue unit can estimate the user's emotions and adjust the content of the conversation based on those emotions. The dialogue unit can refer to the user's past conversation history and provide the most appropriate conversation content. As a result, the action plan and coaching service system according to this embodiment can analyze the user's current situation, create a personalized action plan, visualize progress, and deepen the conversation and make adjustments in a dialogue format.

[0030] The analytics department analyzes the user's current situation. For example, it analyzes basic information entered by the user, such as their current situation, goals, and daily routine. Specifically, the data provided by the user includes daily schedules, diet, exercise frequency, sleep duration, and stress levels. This data may be entered manually by the user or automatically collected from smartphones or wearable devices. The analytics department integrates this data to analyze the user's lifestyle patterns and behavioral history in detail. For example, it identifies when the user is most active each day, what kind of diet contributes to their health, and how much exercise is effective. Furthermore, the analytics department evaluates how the current state has changed by comparing it with the user's past data. This allows the analytics department to clarify the user's strengths and areas for improvement and provide foundational data for proposing an optimal action plan. The analytics department can process data in real time using AI and analyze the current situation in detail based on the user's lifestyle patterns and behavioral history. For example, it can use machine learning algorithms to cluster the user's behavioral patterns and refer to the success stories of other users with similar patterns. This allows the analytics department to provide specific advice tailored to the user's individual needs.

[0031] The generation unit creates an action plan based on the information analyzed by the analysis unit. For example, the generation unit creates a specific action plan tailored to the user's goals. Specifically, if the user's goal is weight loss, it provides a detailed plan that takes into account factors such as diet, exercise frequency, and sleep duration. The generation unit can set specific daily tasks based on the user's lifestyle and goals. For example, it can suggest specific daily meal menus, exercise programs, and times for relaxation. Furthermore, the generation unit can create an effective action plan by considering the user's past successes. For example, by incorporating previously successful diet methods and stress management techniques, it increases the user's chances of success again. The generation unit uses AI to analyze this data and generate an optimal action plan. For example, it uses natural language processing technology to understand the user's goals and current situation and generates a specific action plan based on that. This allows the generation unit to provide a specific action plan tailored to the user's individual needs. Furthermore, the generation unit can continuously improve the action plan based on user feedback. For example, it evaluates the results of the user's actions according to the plan and modifies the plan as needed. This allows the generation unit to provide an optimal action plan to support the user in achieving their goals.

[0032] The visualization unit visualizes the progress of the action plan created by the generation unit. The visualization unit visually displays the progress of the action plan using, for example, calendars and graphs. Specifically, it allows users to see at a glance the completion status of tasks they have set and their progress toward their goals. For example, the calendar displays daily tasks in different colors, and completed tasks are marked with a checkmark. Graphs visually show the user's progress, allowing them to see at a glance things like weight changes, exercise frequency, and dietary balance. The visualization unit can also display, for example, the completion rate and progress bar of a to-do list, making it easier for users to check their progress. This allows users to understand their progress in real time and maintain their motivation. Furthermore, the visualization unit can adjust the level of detail displayed according to the user's goal achievement. For example, as the user approaches their goal, it provides more detailed feedback and advice. This allows the visualization unit to help users concretely understand their progress and take specific actions to move to the next step. Additionally, the visualization unit can customize the displayed content based on user feedback. For example, if a user values ​​certain data, that data will be highlighted. This allows the visualization unit to provide flexible displays tailored to user needs, enabling users to effectively manage their progress.

[0033] The dialogue unit deepens and adjusts the conversation in a dialogue format based on the progress visualized by the visualization unit. For example, the dialogue unit allows users to resolve issues they want to discuss or problems they are facing through dialogue with the AI. Specifically, the AI ​​provides real-time advice on problems and questions that users encounter while acting according to their action plan. For example, if a user fails to complete a particular task, the dialogue unit analyzes the cause and suggests ways to improve next time. The dialogue unit can also estimate the user's emotions and adjust the content of the conversation based on those emotions. For example, if a user is feeling stressed, it provides advice and words of encouragement to help them relax. Furthermore, the dialogue unit can refer to the user's past dialogue history to provide optimal dialogue content. For example, it can conduct more effective conversations based on past successful advice and information that the user found particularly helpful. This allows the dialogue unit to provide specific advice tailored to the user's individual needs and support the execution of their action plan. In addition, the dialogue unit can continuously improve the content of the conversation based on user feedback. For example, it evaluates whether the user is satisfied with the content of the conversation and modifies the dialogue approach as needed. This allows the dialogue unit to provide optimal dialogue to support the user in achieving their goals.

[0034] The reminder unit can provide reminders via a calendar. For example, the reminder unit can register the user's action plan in the calendar and send a notification at the appropriate time. For example, the reminder unit can set the optimal reminder timing according to the user's schedule. For example, the reminder unit can estimate the user's emotions and adjust the reminder timing based on those emotions. This ensures that users remember to carry out their action plans by being reminded via the calendar. Some or all of the above processes in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's schedule data into a generating AI and have the generating AI optimize the reminder timing.

[0035] The display unit can display the completion rate of the to-do list. For example, the display unit can display the user's to-do list completion rate using a graph or progress bar. For example, the display unit can estimate the user's emotions and adjust the display content based on those emotions. For example, the display unit can refer to the user's past viewing history and provide the optimal display method. This makes it easier for the user to check their progress by displaying the to-do list completion rate. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's to-do list data into a generating AI and have the generating AI execute the method for displaying the completion rate.

[0036] The reception desk can receive inquiries. The reception desk can, for example, provide an interface for users to input the content they wish to discuss. The reception desk can, for example, estimate the user's emotions and adjust the content of the inquiry based on those emotions. The reception desk can, for example, refer to the user's past inquiry history and provide the most appropriate method of inquiry. This ensures that users receive support when they are in trouble by accepting their inquiries. Some or all of the above-described processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's inquiry into a generating AI and have the generating AI execute the most appropriate response.

[0037] The analysis unit can analyze the user's past behavioral history to improve the accuracy of the analysis. For example, the analysis unit can analyze the goals and processes that the user has achieved in the past and extract successful patterns. For example, the analysis unit can analyze tasks that the user has failed at in the past and identify the causes of failure. For example, the analysis unit can identify the most effective behavioral patterns from the user's past behavioral history and reflect them in the analysis. In this way, the accuracy of the analysis is improved by analyzing past behavioral history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's behavioral history data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0038] The analysis unit can customize the analysis method during analysis by taking into account the user's lifestyle patterns. For example, the analysis unit can consider whether the user is a morning person or a night owl and propose an optimal action plan. For example, the analysis unit can consider how the user spends their weekends and propose tasks suitable for the weekend. For example, the analysis unit can consider the user's eating and exercise patterns and propose an action plan that supports a healthy lifestyle. In this way, a more appropriate analysis method can be provided by taking into account the user's lifestyle patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's lifestyle pattern data into a generating AI and have the generating AI perform the customization of the analysis method.

[0039] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user lives in an urban area, the analysis unit can propose an action plan specific to urban areas. For example, if the user lives in a rural area, the analysis unit can propose an action plan that utilizes the natural environment. For example, if the user is traveling, the analysis unit can propose an action plan for their travel destination. By considering the user's geographical location information, the analysis unit can provide more appropriate analysis results. 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 user's geographical location information data into a generating AI and have the generating AI perform the analysis.

[0040] The analysis unit can analyze the user's social media activity during analysis and reflect relevant information in the analysis. For example, the analysis unit can reflect goals shared by the user on social media in the analysis. For example, the analysis unit can reflect the activities of influencers followed by the user on social media in the analysis. For example, the analysis unit can reflect feedback received by the user on social media in the analysis. This allows for the provision of more relevant analysis results by analyzing social media activity. 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 user's social media data into a generating AI and have the generating AI perform the analysis.

[0041] The generation unit can create an action plan by considering the user's past successes during the generation process. For example, the generation unit can create an action plan that includes similar tasks by referring to tasks the user has successfully completed in the past. For example, the generation unit can set new goals based on goals the user has achieved in the past. For example, the generation unit can analyze the user's successes and create an action plan that incorporates the factors of those successes. This allows for the provision of a more effective action plan by considering past successes. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's success data into a generation AI and have the generation AI create the action plan.

[0042] The generation unit can adjust the difficulty level of the action plan according to the user's progress toward achieving their goals during the generation process. For example, if the user is approaching their goal, the generation unit can create an action plan that includes tasks of high difficulty. For example, if the user is far from their goal, the generation unit can create an action plan that includes tasks of easy difficulty. For example, the generation unit can analyze the user's progress toward achieving their goal and create an action plan that includes tasks of appropriate difficulty. This allows for the provision of a more appropriate action plan by adjusting the difficulty level of the action plan according to the user's progress toward achieving their goal. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's progress toward achieving their goal into a generation AI and have the generation AI adjust the difficulty level of the action plan.

[0043] The generation unit can create an action plan that takes into account the user's geographical location information during generation. For example, if the user lives in an urban area, the generation unit can create an action plan that includes urban-specific tasks. For example, if the user lives in a rural area, the generation unit can create an action plan that includes tasks that utilize the natural environment. For example, if the user is traveling, the generation unit can create an action plan that includes tasks at the travel destination. This allows for the provision of a more appropriate action plan by considering geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information data into a generation AI and have the generation AI create the action plan.

[0044] The generation unit can analyze the user's social media activity during generation and propose relevant action plans. For example, the generation unit can reflect goals shared by the user on social media in the action plan. For example, the generation unit can reflect the activities of influencers followed by the user on social media in the action plan. For example, the generation unit can reflect feedback received by the user on social media in the action plan. By analyzing social media activity, it is possible to provide more relevant action plans. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI execute the action plan proposal.

[0045] The visualization unit can optimize the displayed content by referring to the user's past progress data during visualization. For example, the visualization unit can display goals the user has achieved in the past and their progress. For example, the visualization unit can display tasks the user has failed at in the past and the reasons for those failures. For example, the visualization unit can analyze the user's past progress data and provide the most effective display method. This allows for more effective display content by referring to past progress data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's progress data into a generating AI and have the generating AI perform the optimization of the displayed content.

[0046] The visualization unit can adjust the level of detail displayed according to the user's progress toward achieving their goals during visualization. For example, if the user is approaching their goal, the visualization unit can display detailed progress information. For example, if the user is moving further away from their goal, the visualization unit can display concise progress information. For example, the visualization unit can analyze the user's progress toward their goal and display progress information with an appropriate level of detail. This allows for more appropriate display by adjusting the level of detail according to the user's progress toward their goal. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's progress toward their goal into a generating AI and have the generating AI adjust the level of detail of the display.

[0047] The visualization unit can display progress while considering the user's geographical location information. For example, if the user lives in an urban area, the visualization unit can display urban-specific progress information. For example, if the user lives in a rural area, the visualization unit can display progress information that utilizes the natural environment. For example, if the user is traveling, the visualization unit can display progress information at the travel destination. This makes it possible to display progress more appropriately by considering geographical location information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's geographical location information data into a generating AI and have the generating AI perform the display of progress.

[0048] The visualization unit can analyze the user's social media activity and display relevant progress information during visualization. For example, the visualization unit can display the progress of goals shared by the user on social media. For example, the visualization unit can display progress information of influencers followed by the user on social media. For example, the visualization unit can reflect feedback received by the user on social media in the progress information. This allows for the provision of more relevant progress information by analyzing social media activity. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's social media data into a generating AI and have the generating AI display the progress information.

[0049] The dialogue unit can provide optimal dialogue content by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can provide relevant dialogue content based on what the user has consulted about in the past. For example, the dialogue unit can provide ongoing support by referring to advice the user has received in the past. For example, the dialogue unit can provide the most effective dialogue content by analyzing the user's past dialogue history. This allows for the provision of more effective dialogue content by referring to past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's dialogue history data into a generating AI and have the generating AI perform the optimization of the dialogue content.

[0050] The dialogue unit can adjust the level of detail in the dialogue according to the user's progress toward achieving their goals. For example, if the user is approaching their goal, the dialogue unit can provide detailed advice. If the user is moving further away from their goal, the dialogue unit can provide concise advice. For example, the dialogue unit can analyze the user's progress toward their goal and provide dialogue content with the appropriate level of detail. By adjusting the level of detail in the dialogue according to the user's progress toward their goal, more appropriate dialogue becomes possible. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's progress toward their goal into a generating AI and have the generating AI adjust the level of detail in the dialogue content.

[0051] The dialogue unit can provide dialogue content while considering the user's geographical location information. For example, if the user lives in an urban area, the dialogue unit can provide urban-specific dialogue content. For example, if the user lives in a rural area, the dialogue unit can provide dialogue content that utilizes the natural environment. For example, if the user is traveling, the dialogue unit can provide dialogue content relevant to their travel destination. By considering geographical location information, more appropriate dialogue content can be provided. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing dialogue content.

[0052] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant dialogue content. For example, the dialogue unit can provide dialogue content related to goals the user has shared on social media. For example, the dialogue unit can provide dialogue content related to the activities of influencers the user follows on social media. For example, the dialogue unit can provide dialogue content based on feedback the user has received on social media. By analyzing social media activity, it is possible to provide more relevant dialogue content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's social media data into a generating AI and have the generating AI provide the dialogue content.

[0053] The reminder unit can provide the optimal reminder method by referring to the user's past reminder history when sending a reminder. For example, the reminder unit can prioritize providing reminder methods that have been effective for the user in the past. For example, the reminder unit can improve and provide reminder methods that the user has ignored in the past. For example, the reminder unit can analyze the user's past reminder history and provide the most effective reminder method. This allows for the provision of more effective reminder methods by referring to past reminder history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's reminder history data into a generating AI and have the generating AI perform the optimization of the reminder method.

[0054] The reminder unit can provide reminder content that takes into account the user's geographical location information. For example, if the user lives in an urban area, the reminder unit can provide urban-specific reminder content. If the user lives in a rural area, the reminder unit can provide reminder content that utilizes the natural environment. If the user is traveling, the reminder unit can provide reminder content relevant to their travel destination. By considering geographical location information, the system can provide more appropriate reminder content. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing the reminder content.

[0055] The display unit can provide the optimal display method by referring to the user's past viewing history when displaying information. For example, the display unit can prioritize providing display methods that have been effective for the user in the past. For example, the display unit can improve and provide display methods that the user has ignored in the past. For example, the display unit can analyze the user's past viewing history and provide the most effective display method. This allows for the provision of more effective display methods by referring to past viewing history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's viewing history data into a generating AI and have the generating AI perform the optimization of the display method.

[0056] The display unit can provide display content while considering the user's geographical location information. For example, if the user lives in an urban area, the display unit can provide urban-specific display content. For example, if the user lives in a rural area, the display unit can provide display content that utilizes the natural environment. For example, if the user is traveling, the display unit can provide display content relevant to the travel destination. By considering geographical location information, more appropriate display content can be provided. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing the display content.

[0057] The reception unit can provide the optimal reception method by referring to the user's past reception history at the time of reception. For example, the reception unit can prioritize providing reception methods that have been effective for the user in the past. For example, the reception unit can improve and provide reception methods that the user has ignored in the past. For example, the reception unit can analyze the user's past reception history and provide the most effective reception method. This allows for the provision of more effective reception methods by referring to past reception history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's reception history data into a generating AI and have the generating AI perform the optimization of the reception method.

[0058] The reception desk can provide reception details while considering the user's geographical location information at the time of reception. For example, if the user lives in an urban area, the reception desk can provide urban-specific reception details. For example, if the user lives in a rural area, the reception desk can provide reception details that utilize the natural environment. For example, if the user is traveling, the reception desk can provide reception details for the travel destination. In this way, more appropriate reception details can be provided by considering geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information data into a generating AI and have the generating AI perform the provision of reception details.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The analysis unit can analyze the user's past behavioral history to improve the accuracy of the analysis. For example, the analysis unit can analyze the goals and processes that the user has achieved in the past and extract successful patterns. For example, the analysis unit can analyze tasks that the user has failed at in the past and identify the causes of failure. For example, the analysis unit can identify the most effective behavioral patterns from the user's past behavioral history and reflect them in the analysis. In this way, the accuracy of the analysis is improved by analyzing past behavioral history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's behavioral history data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0061] The analysis unit can customize the analysis method during analysis by taking into account the user's lifestyle patterns. For example, the analysis unit can consider whether the user is a morning person or a night owl and propose an optimal action plan. For example, the analysis unit can consider how the user spends their weekends and propose tasks suitable for the weekend. For example, the analysis unit can consider the user's eating and exercise patterns and propose an action plan that supports a healthy lifestyle. In this way, a more appropriate analysis method can be provided by taking into account the user's lifestyle patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's lifestyle pattern data into a generating AI and have the generating AI perform the customization of the analysis method.

[0062] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user lives in an urban area, the analysis unit can propose an action plan specific to urban areas. For example, if the user lives in a rural area, the analysis unit can propose an action plan that utilizes the natural environment. For example, if the user is traveling, the analysis unit can propose an action plan for their travel destination. By considering the user's geographical location information, the analysis unit can provide more appropriate analysis results. 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 user's geographical location information data into a generating AI and have the generating AI perform the analysis.

[0063] The generation unit can create an action plan by considering the user's past successes during the generation process. For example, the generation unit can create an action plan that includes similar tasks by referring to tasks the user has successfully completed in the past. For example, the generation unit can set new goals based on goals the user has achieved in the past. For example, the generation unit can analyze the user's successes and create an action plan that incorporates the factors of those successes. This allows for the provision of a more effective action plan by considering past successes. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's success data into a generation AI and have the generation AI create the action plan.

[0064] The visualization unit can optimize the displayed content by referring to the user's past progress data during visualization. For example, the visualization unit can display goals the user has achieved in the past and their progress. For example, the visualization unit can display tasks the user has failed at in the past and the reasons for those failures. For example, the visualization unit can analyze the user's past progress data and provide the most effective display method. This allows for more effective display content by referring to past progress data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's progress data into a generating AI and have the generating AI perform the optimization of the displayed content.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The analysis unit analyzes the user's current situation. For example, the analysis unit analyzes basic information entered by the user, such as their current situation, goals, and daily routine. Based on the user's lifestyle patterns and behavioral history, the analysis unit can perform a detailed analysis of the current situation. Step 2: The generation unit creates an action plan based on the information analyzed by the analysis unit. For example, the generation unit creates a specific action plan tailored to the user's goals. The generation unit can set specific daily tasks based on the user's lifestyle patterns and goals. Step 3: The visualization unit visualizes the progress of the action plan created by the generation unit. The visualization unit visually displays the progress of the action plan, for example, using a calendar or graph. The visualization unit can display the completion rate and progress bar of the to-do list, making it easier for users to check the progress. Step 4: The dialogue unit deepens and adjusts the conversation in a dialogue format based on the progress visualized by the visualization unit. For example, the dialogue unit can resolve issues that the user wants to discuss or problems they are facing through dialogue with the AI. The dialogue unit can estimate the user's emotions and adjust the content of the conversation based on those emotions.

[0067] (Example of form 2)The action plan and coaching service system according to an embodiment of the present invention is a system that provides action plans and coaching services using AI for people who have dreams or things they want to do. This system starts with the user inputting basic information such as their current situation, goals, and daily routine. Next, the AI ​​analyzes this information and creates a specific action plan tailored to the individual. This action plan is reminded on a calendar, and the overall progress rate is visualized. The user takes action based on the plan, and if there are any issues with the completion rate of the to-do list, things they have noticed, or things they want to discuss, they can deepen their understanding and make adjustments while interacting with the AI. By repeating this process, the user can get closer to self-realization. This service is expected to not only bring users closer to realizing their dreams and things they want to do, but also to improve their confidence and motivation through successful experiences. In particular, it is a very beneficial service for working adults who are tired from work and unable to do what they want to do. For example, if a user has the goal of "becoming fluent in English within a year," the AI ​​analyzes the user's current English ability and daily schedule and creates a specific learning plan. Daily learning content and progress are reminded on a calendar, and the user proceeds with learning based on the plan. If users encounter difficulties or need advice along the way, they can find solutions by interacting with the AI. In this way, users can progress towards their goals at their own pace and ultimately move closer to self-realization. Furthermore, accumulating successful experiences will improve their confidence and motivation, and increase their desire to take on further challenges. This service can be offered to a wide range of users by adopting business models such as a freemium model, affiliate income, and B2B development. In particular, it targets working adults in their 20s to 50s, and will be a very beneficial service for people who are tired from work and unable to do what they want. In addition, by utilizing generative AI, users can quickly gather information in a conversational format, organize, process, and delve deeper into it, allowing them to design their own lives and live more fulfilling lives. Socially, many people are interested in "AI x ○○" and this service has the potential to make an impact on society.Through this service, we aim to prove that what you want to do can be achieved (or brought closer to achieving it) by breaking it down and tackling the challenges, and to help many people achieve self-realization. This will allow our action plan and coaching service system to support users in realizing their dreams and aspirations, and to improve their confidence and motivation through successful experiences.

[0068] The action plan and coaching service system according to this embodiment comprises an analysis unit, a generation unit, a visualization unit, and a dialogue unit. The analysis unit analyzes the user's current situation. The analysis unit analyzes basic information such as the user's current situation, goals, and daily routine, for example, entered by the user. The analysis unit can analyze the current situation in detail based on the user's lifestyle patterns and behavioral history, for example. The analysis unit can propose an optimal action plan, for example, taking into account the user's lifestyle patterns. The generation unit creates an action plan based on the information analyzed by the analysis unit. The generation unit creates a specific action plan tailored to the user's goals, for example. The generation unit can set specific daily tasks based on the user's lifestyle patterns and goals, for example. The generation unit can create an effective action plan, for example, taking into account the user's past successes. The visualization unit visualizes the progress of the action plan created by the generation unit. The visualization unit visually displays the progress of the action plan using, for example, a calendar or graph. The visualization unit can display, for example, the completion rate of a to-do list or a progress bar, making it easier for the user to check the progress. The visualization unit can adjust the level of detail displayed according to the user's progress toward achieving their goals. The dialogue unit can deepen the conversation and make adjustments based on the progress visualized by the visualization unit. The dialogue unit can resolve issues the user wants to discuss or problems they are facing through dialogue with the AI. The dialogue unit can estimate the user's emotions and adjust the content of the conversation based on those emotions. The dialogue unit can refer to the user's past conversation history and provide the most appropriate conversation content. As a result, the action plan and coaching service system according to this embodiment can analyze the user's current situation, create a personalized action plan, visualize progress, and deepen the conversation and make adjustments in a dialogue format.

[0069] The analytics department analyzes the user's current situation. For example, it analyzes basic information entered by the user, such as their current situation, goals, and daily routine. Specifically, the data provided by the user includes daily schedules, diet, exercise frequency, sleep duration, and stress levels. This data may be entered manually by the user or automatically collected from smartphones or wearable devices. The analytics department integrates this data to analyze the user's lifestyle patterns and behavioral history in detail. For example, it identifies when the user is most active each day, what kind of diet contributes to their health, and how much exercise is effective. Furthermore, the analytics department evaluates how the current state has changed by comparing it with the user's past data. This allows the analytics department to clarify the user's strengths and areas for improvement and provide foundational data for proposing an optimal action plan. The analytics department can process data in real time using AI and analyze the current situation in detail based on the user's lifestyle patterns and behavioral history. For example, it can use machine learning algorithms to cluster the user's behavioral patterns and refer to the success stories of other users with similar patterns. This allows the analytics department to provide specific advice tailored to the user's individual needs.

[0070] The generation unit creates an action plan based on the information analyzed by the analysis unit. For example, the generation unit creates a specific action plan tailored to the user's goals. Specifically, if the user's goal is weight loss, it provides a detailed plan that takes into account factors such as diet, exercise frequency, and sleep duration. The generation unit can set specific daily tasks based on the user's lifestyle and goals. For example, it can suggest specific daily meal menus, exercise programs, and times for relaxation. Furthermore, the generation unit can create an effective action plan by considering the user's past successes. For example, by incorporating previously successful diet methods and stress management techniques, it increases the user's chances of success again. The generation unit uses AI to analyze this data and generate an optimal action plan. For example, it uses natural language processing technology to understand the user's goals and current situation and generates a specific action plan based on that. This allows the generation unit to provide a specific action plan tailored to the user's individual needs. Furthermore, the generation unit can continuously improve the action plan based on user feedback. For example, it evaluates the results of the user's actions according to the plan and modifies the plan as needed. This allows the generation unit to provide an optimal action plan to support the user in achieving their goals.

[0071] The visualization unit visualizes the progress of the action plan created by the generation unit. The visualization unit visually displays the progress of the action plan using, for example, calendars and graphs. Specifically, it allows users to see at a glance the completion status of tasks they have set and their progress toward their goals. For example, the calendar displays daily tasks in different colors, and completed tasks are marked with a checkmark. Graphs visually show the user's progress, allowing them to see at a glance things like weight changes, exercise frequency, and dietary balance. The visualization unit can also display, for example, the completion rate and progress bar of a to-do list, making it easier for users to check their progress. This allows users to understand their progress in real time and maintain their motivation. Furthermore, the visualization unit can adjust the level of detail displayed according to the user's goal achievement. For example, as the user approaches their goal, it provides more detailed feedback and advice. This allows the visualization unit to help users concretely understand their progress and take specific actions to move to the next step. Additionally, the visualization unit can customize the displayed content based on user feedback. For example, if a user values ​​certain data, that data will be highlighted. This allows the visualization unit to provide flexible displays tailored to user needs, enabling users to effectively manage their progress.

[0072] The dialogue unit deepens and adjusts the conversation in a dialogue format based on the progress visualized by the visualization unit. For example, the dialogue unit allows users to resolve issues they want to discuss or problems they are facing through dialogue with the AI. Specifically, the AI ​​provides real-time advice on problems and questions that users encounter while acting according to their action plan. For example, if a user fails to complete a particular task, the dialogue unit analyzes the cause and suggests ways to improve next time. The dialogue unit can also estimate the user's emotions and adjust the content of the conversation based on those emotions. For example, if a user is feeling stressed, it provides advice and words of encouragement to help them relax. Furthermore, the dialogue unit can refer to the user's past dialogue history to provide optimal dialogue content. For example, it can conduct more effective conversations based on past successful advice and information that the user found particularly helpful. This allows the dialogue unit to provide specific advice tailored to the user's individual needs and support the execution of their action plan. In addition, the dialogue unit can continuously improve the content of the conversation based on user feedback. For example, it evaluates whether the user is satisfied with the content of the conversation and modifies the dialogue approach as needed. This allows the dialogue unit to provide optimal dialogue to support the user in achieving their goals.

[0073] The reminder unit can provide reminders via a calendar. For example, the reminder unit can register the user's action plan in the calendar and send a notification at the appropriate time. For example, the reminder unit can set the optimal reminder timing according to the user's schedule. For example, the reminder unit can estimate the user's emotions and adjust the reminder timing based on those emotions. This ensures that users remember to carry out their action plans by being reminded via the calendar. Some or all of the above processes in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's schedule data into a generating AI and have the generating AI optimize the reminder timing.

[0074] The display unit can display the completion rate of the to-do list. For example, the display unit can display the user's to-do list completion rate using a graph or progress bar. For example, the display unit can estimate the user's emotions and adjust the display content based on those emotions. For example, the display unit can refer to the user's past viewing history and provide the optimal display method. This makes it easier for the user to check their progress by displaying the to-do list completion rate. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's to-do list data into a generating AI and have the generating AI execute the method for displaying the completion rate.

[0075] The reception desk can receive inquiries. The reception desk can, for example, provide an interface for users to input the content they wish to discuss. The reception desk can, for example, estimate the user's emotions and adjust the content of the inquiry based on those emotions. The reception desk can, for example, refer to the user's past inquiry history and provide the most appropriate method of inquiry. This ensures that users receive support when they are in trouble by accepting their inquiries. Some or all of the above-described processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's inquiry into a generating AI and have the generating AI execute the most appropriate response.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize providing analysis results that promote relaxation. For example, if the user is highly motivated, the analysis unit can prioritize analyses related to challenging goals. For example, if the user is tired, the analysis unit can prioritize analyses related to easily achievable tasks. By adjusting the analysis priority according to the user's emotions, more appropriate analysis results can be provided. 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 above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis priority.

[0077] The analysis unit can analyze the user's past behavioral history to improve the accuracy of the analysis. For example, the analysis unit can analyze the goals and processes that the user has achieved in the past and extract successful patterns. For example, the analysis unit can analyze tasks that the user has failed at in the past and identify the causes of failure. For example, the analysis unit can identify the most effective behavioral patterns from the user's past behavioral history and reflect them in the analysis. In this way, the accuracy of the analysis is improved by analyzing past behavioral history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's behavioral history data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0078] The analysis unit can customize the analysis method during analysis by taking into account the user's lifestyle patterns. For example, the analysis unit can consider whether the user is a morning person or a night owl and propose an optimal action plan. For example, the analysis unit can consider how the user spends their weekends and propose tasks suitable for the weekend. For example, the analysis unit can consider the user's eating and exercise patterns and propose an action plan that supports a healthy lifestyle. In this way, a more appropriate analysis method can be provided by taking into account the user's lifestyle patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's lifestyle pattern data into a generating AI and have the generating AI perform the customization of the analysis method.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0080] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user lives in an urban area, the analysis unit can propose an action plan specific to urban areas. For example, if the user lives in a rural area, the analysis unit can propose an action plan that utilizes the natural environment. For example, if the user is traveling, the analysis unit can propose an action plan for their travel destination. By considering the user's geographical location information, the analysis unit can provide more appropriate analysis results. 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 user's geographical location information data into a generating AI and have the generating AI perform the analysis.

[0081] The analysis unit can analyze the user's social media activity during analysis and reflect relevant information in the analysis. For example, the analysis unit can reflect goals shared by the user on social media in the analysis. For example, the analysis unit can reflect the activities of influencers followed by the user on social media in the analysis. For example, the analysis unit can reflect feedback received by the user on social media in the analysis. This allows for the provision of more relevant analysis results by analyzing social media activity. 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 user's social media data into a generating AI and have the generating AI perform the analysis.

[0082] The generation unit can estimate the user's emotions and adjust the content of the action plan based on the estimated emotions. For example, if the user is stressed, the generation unit can create an action plan that includes relaxing tasks. For example, if the user is highly motivated, the generation unit can create an action plan that includes challenging tasks. For example, if the user is tired, the generation unit can create an action plan that includes easily achievable tasks. In this way, by adjusting the content of the action plan according to the user's emotions, a more appropriate action plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the content of the action plan.

[0083] The generation unit can create an action plan by considering the user's past successes during the generation process. For example, the generation unit can create an action plan that includes similar tasks by referring to tasks the user has successfully completed in the past. For example, the generation unit can set new goals based on goals the user has achieved in the past. For example, the generation unit can analyze the user's successes and create an action plan that incorporates the factors of those successes. This allows for the provision of a more effective action plan by considering past successes. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's success data into a generation AI and have the generation AI create the action plan.

[0084] The generation unit can adjust the difficulty level of the action plan according to the user's progress toward achieving their goals during the generation process. For example, if the user is approaching their goal, the generation unit can create an action plan that includes tasks of high difficulty. For example, if the user is far from their goal, the generation unit can create an action plan that includes tasks of easy difficulty. For example, the generation unit can analyze the user's progress toward achieving their goal and create an action plan that includes tasks of appropriate difficulty. This allows for the provision of a more appropriate action plan by adjusting the difficulty level of the action plan according to the user's progress toward achieving their goal. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's progress toward achieving their goal into a generation AI and have the generation AI adjust the difficulty level of the action plan.

[0085] The generation unit can estimate the user's emotions and determine the priority of the action plan based on the estimated emotions. For example, if the user is stressed, the generation unit may prioritize relaxing tasks. For example, if the user is highly motivated, the generation unit may prioritize challenging tasks. For example, if the user is tired, the generation unit may prioritize easily achievable tasks. This allows for the provision of a more appropriate action plan by determining the priority of the action plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI determine the priority of the action plan.

[0086] The generation unit can create an action plan that takes into account the user's geographical location information during generation. For example, if the user lives in an urban area, the generation unit can create an action plan that includes urban-specific tasks. For example, if the user lives in a rural area, the generation unit can create an action plan that includes tasks that utilize the natural environment. For example, if the user is traveling, the generation unit can create an action plan that includes tasks at the travel destination. This allows for the provision of a more appropriate action plan by considering geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information data into a generation AI and have the generation AI create the action plan.

[0087] The generation unit can analyze the user's social media activity during generation and propose relevant action plans. For example, the generation unit can reflect goals shared by the user on social media in the action plan. For example, the generation unit can reflect the activities of influencers followed by the user on social media in the action plan. For example, the generation unit can reflect feedback received by the user on social media in the action plan. By analyzing social media activity, it is possible to provide more relevant action plans. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI execute the action plan proposal.

[0088] The visualization unit can estimate the user's emotions and adjust the progress display method based on the estimated user emotions. For example, if the user is nervous, the visualization unit can provide a simple and highly visible display method. For example, if the user is relaxed, the visualization unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the visualization unit can provide a display method that gets straight to the point. This allows for a more appropriate display by adjusting the progress display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0089] The visualization unit can optimize the displayed content by referring to the user's past progress data during visualization. For example, the visualization unit can display goals the user has achieved in the past and their progress. For example, the visualization unit can display tasks the user has failed at in the past and the reasons for those failures. For example, the visualization unit can analyze the user's past progress data and provide the most effective display method. This allows for more effective display content by referring to past progress data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's progress data into a generating AI and have the generating AI perform the optimization of the displayed content.

[0090] The visualization unit can adjust the level of detail displayed according to the user's progress toward achieving their goals during visualization. For example, if the user is approaching their goal, the visualization unit can display detailed progress information. For example, if the user is moving further away from their goal, the visualization unit can display concise progress information. For example, the visualization unit can analyze the user's progress toward their goal and display progress information with an appropriate level of detail. This allows for more appropriate display by adjusting the level of detail according to the user's progress toward their goal. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's progress toward their goal into a generating AI and have the generating AI adjust the level of detail of the display.

[0091] The visualization unit can estimate the user's emotions and adjust the display order of progress based on the estimated emotions. For example, if the user is nervous, the visualization unit can display important information first. For example, if the user is relaxed, the visualization unit can display detailed information first. For example, if the user is in a hurry, the visualization unit can display concise information first. This allows for a more appropriate display by adjusting the display order of progress according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, or not using AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display order.

[0092] The visualization unit can display progress while considering the user's geographical location information. For example, if the user lives in an urban area, the visualization unit can display urban-specific progress information. For example, if the user lives in a rural area, the visualization unit can display progress information that utilizes the natural environment. For example, if the user is traveling, the visualization unit can display progress information at the travel destination. This makes it possible to display progress more appropriately by considering geographical location information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's geographical location information data into a generating AI and have the generating AI perform the display of progress.

[0093] The visualization unit can analyze the user's social media activity and display relevant progress information during visualization. For example, the visualization unit can display the progress of goals shared by the user on social media. For example, the visualization unit can display progress information of influencers followed by the user on social media. For example, the visualization unit can reflect feedback received by the user on social media in the progress information. This allows for the provision of more relevant progress information by analyzing social media activity. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's social media data into a generating AI and have the generating AI display the progress information.

[0094] The dialogue unit can estimate the user's emotions and adjust the content of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit can provide relaxing dialogue. For example, if the user is highly motivated, the dialogue unit can provide challenging dialogue. For example, if the user is tired, the dialogue unit can provide dialogue about easily achievable tasks. By adjusting the content of the dialogue according to the user's emotions, more appropriate dialogue becomes possible. 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 above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the dialogue content.

[0095] The dialogue unit can provide optimal dialogue content by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can provide relevant dialogue content based on what the user has consulted about in the past. For example, the dialogue unit can provide ongoing support by referring to advice the user has received in the past. For example, the dialogue unit can provide the most effective dialogue content by analyzing the user's past dialogue history. This allows for the provision of more effective dialogue content by referring to past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's dialogue history data into a generating AI and have the generating AI perform the optimization of the dialogue content.

[0096] The dialogue unit can adjust the level of detail in the dialogue according to the user's progress toward achieving their goals. For example, if the user is approaching their goal, the dialogue unit can provide detailed advice. If the user is moving further away from their goal, the dialogue unit can provide concise advice. For example, the dialogue unit can analyze the user's progress toward their goal and provide dialogue content with the appropriate level of detail. By adjusting the level of detail in the dialogue according to the user's progress toward their goal, more appropriate dialogue becomes possible. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's progress toward their goal into a generating AI and have the generating AI adjust the level of detail in the dialogue content.

[0097] The dialogue unit can estimate the user's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit may prioritize relaxing dialogue. For example, if the user is highly motivated, the dialogue unit may prioritize challenging dialogue. For example, if the user is tired, the dialogue unit may prioritize dialogue about easily achievable tasks. This allows for more appropriate dialogue by determining the priority of dialogue according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI determine the priority of the dialogue.

[0098] The dialogue unit can provide dialogue content while considering the user's geographical location information. For example, if the user lives in an urban area, the dialogue unit can provide urban-specific dialogue content. For example, if the user lives in a rural area, the dialogue unit can provide dialogue content that utilizes the natural environment. For example, if the user is traveling, the dialogue unit can provide dialogue content relevant to their travel destination. By considering geographical location information, more appropriate dialogue content can be provided. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing dialogue content.

[0099] The dialogue unit can analyze the user's social media activity during a conversation and provide relevant dialogue content. For example, the dialogue unit can provide dialogue content related to goals the user has shared on social media. For example, the dialogue unit can provide dialogue content related to the activities of influencers the user follows on social media. For example, the dialogue unit can provide dialogue content based on feedback the user has received on social media. By analyzing social media activity, it is possible to provide more relevant dialogue content. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's social media data into a generating AI and have the generating AI provide the dialogue content.

[0100] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, if the user is stressed, the reminder unit can send reminders at a time when the user can relax. For example, if the user is highly motivated, the reminder unit can prioritize reminders for challenging tasks. For example, if the user is tired, the reminder unit can prioritize reminders for easily achievable tasks. By adjusting the timing of reminders according to the user's emotions, more appropriate reminders become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into the generative AI and have the generative AI adjust the timing of reminders.

[0101] The reminder unit can provide the optimal reminder method by referring to the user's past reminder history when sending a reminder. For example, the reminder unit can prioritize providing reminder methods that have been effective for the user in the past. For example, the reminder unit can improve and provide reminder methods that the user has ignored in the past. For example, the reminder unit can analyze the user's past reminder history and provide the most effective reminder method. This allows for the provision of more effective reminder methods by referring to past reminder history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's reminder history data into a generating AI and have the generating AI perform the optimization of the reminder method.

[0102] The reminder unit can estimate the user's emotions and determine the priority of reminders based on those emotions. For example, if the user is stressed, the reminder unit may prioritize reminders for relaxing tasks. If the user is highly motivated, the reminder unit may prioritize reminders for challenging tasks. If the user is tired, the reminder unit may prioritize reminders for easily achievable tasks. By determining the priority of reminders according to the user's emotions, more appropriate reminders become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into a generative AI and have the generative AI determine the priority of reminders.

[0103] The reminder unit can provide reminder content that takes into account the user's geographical location information. For example, if the user lives in an urban area, the reminder unit can provide urban-specific reminder content. If the user lives in a rural area, the reminder unit can provide reminder content that utilizes the natural environment. If the user is traveling, the reminder unit can provide reminder content relevant to their travel destination. By considering geographical location information, the system can provide more appropriate reminder content. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing the reminder content.

[0104] The display unit can estimate the user's emotions and adjust the displayed content based on the estimated emotions. For example, if the user is stressed, the display unit can provide relaxing content. For example, if the user is highly motivated, the display unit can provide challenging content. For example, if the user is tired, the display unit can provide content related to easily achievable tasks. By adjusting the displayed content according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the displayed content.

[0105] The display unit can provide the optimal display method by referring to the user's past viewing history when displaying information. For example, the display unit can prioritize providing display methods that have been effective for the user in the past. For example, the display unit can improve and provide display methods that the user has ignored in the past. For example, the display unit can analyze the user's past viewing history and provide the most effective display method. This allows for the provision of more effective display methods by referring to past viewing history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's viewing history data into a generating AI and have the generating AI perform the optimization of the display method.

[0106] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is stressed, the display unit may prioritize displaying relaxing tasks. For example, if the user is highly motivated, the display unit may prioritize displaying challenging tasks. For example, if the user is tired, the display unit may prioritize displaying easily achievable tasks. This allows for more appropriate displays by determining the display priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, or not using AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI determine the display priority.

[0107] The display unit can provide display content while considering the user's geographical location information. For example, if the user lives in an urban area, the display unit can provide urban-specific display content. For example, if the user lives in a rural area, the display unit can provide display content that utilizes the natural environment. For example, if the user is traveling, the display unit can provide display content relevant to the travel destination. By considering geographical location information, more appropriate display content can be provided. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing the display content.

[0108] The reception desk can estimate the user's emotions and adjust the reception content based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of the reception content. This allows for more appropriate reception by adjusting the reception content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform the adjustment of the reception content.

[0109] The reception unit can provide the optimal reception method by referring to the user's past reception history at the time of reception. For example, the reception unit can prioritize providing reception methods that have been effective for the user in the past. For example, the reception unit can improve and provide reception methods that the user has ignored in the past. For example, the reception unit can analyze the user's past reception history and provide the most effective reception method. This allows for the provision of more effective reception methods by referring to past reception history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's reception history data into a generating AI and have the generating AI perform the optimization of the reception method.

[0110] The reception unit can estimate the user's emotions and determine the priority of requests based on those emotions. For example, if the user is stressed, the reception unit may prioritize requests for relaxing tasks. If the user is highly motivated, the reception unit may prioritize requests for challenging tasks. If the user is tired, the reception unit may prioritize requests for easily achievable tasks. This allows for more appropriate requests by determining the priority of requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI determine the priority of requests.

[0111] The reception desk can provide reception details while considering the user's geographical location information at the time of reception. For example, if the user lives in an urban area, the reception desk can provide urban-specific reception details. For example, if the user lives in a rural area, the reception desk can provide reception details that utilize the natural environment. For example, if the user is traveling, the reception desk can provide reception details for the travel destination. In this way, more appropriate reception details can be provided by considering geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information data into a generating AI and have the generating AI perform the provision of reception details.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize providing analysis results that promote relaxation. For example, if the user is highly motivated, the analysis unit can prioritize analyses related to challenging goals. For example, if the user is tired, the analysis unit can prioritize analyses related to easily achievable tasks. By adjusting the analysis priority according to the user's emotions, more appropriate analysis results can be provided. 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 above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis priority.

[0114] The generation unit can estimate the user's emotions and adjust the content of the action plan based on the estimated emotions. For example, if the user is stressed, the generation unit can create an action plan that includes relaxing tasks. For example, if the user is highly motivated, the generation unit can create an action plan that includes challenging tasks. For example, if the user is tired, the generation unit can create an action plan that includes easily achievable tasks. In this way, by adjusting the content of the action plan according to the user's emotions, a more appropriate action plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the content of the action plan.

[0115] The visualization unit can estimate the user's emotions and adjust the progress display method based on the estimated user emotions. For example, if the user is nervous, the visualization unit can provide a simple and highly visible display method. For example, if the user is relaxed, the visualization unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the visualization unit can provide a display method that gets straight to the point. This allows for a more appropriate display by adjusting the progress display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0116] The dialogue unit can estimate the user's emotions and adjust the content of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit can provide relaxing dialogue. For example, if the user is highly motivated, the dialogue unit can provide challenging dialogue. For example, if the user is tired, the dialogue unit can provide dialogue about easily achievable tasks. By adjusting the content of the dialogue according to the user's emotions, more appropriate dialogue becomes possible. 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 above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the dialogue content.

[0117] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, if the user is stressed, the reminder unit can send reminders at a time when the user can relax. For example, if the user is highly motivated, the reminder unit can prioritize reminders for challenging tasks. For example, if the user is tired, the reminder unit can prioritize reminders for easily achievable tasks. By adjusting the timing of reminders according to the user's emotions, more appropriate reminders become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into the generative AI and have the generative AI adjust the timing of reminders.

[0118] The analysis unit can analyze the user's past behavioral history to improve the accuracy of the analysis. For example, the analysis unit can analyze the goals and processes that the user has achieved in the past and extract successful patterns. For example, the analysis unit can analyze tasks that the user has failed at in the past and identify the causes of failure. For example, the analysis unit can identify the most effective behavioral patterns from the user's past behavioral history and reflect them in the analysis. In this way, the accuracy of the analysis is improved by analyzing past behavioral history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's behavioral history data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0119] The analysis unit can customize the analysis method during analysis by taking into account the user's lifestyle patterns. For example, the analysis unit can consider whether the user is a morning person or a night owl and propose an optimal action plan. For example, the analysis unit can consider how the user spends their weekends and propose tasks suitable for the weekend. For example, the analysis unit can consider the user's eating and exercise patterns and propose an action plan that supports a healthy lifestyle. In this way, a more appropriate analysis method can be provided by taking into account the user's lifestyle patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's lifestyle pattern data into a generating AI and have the generating AI perform the customization of the analysis method.

[0120] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user lives in an urban area, the analysis unit can propose an action plan specific to urban areas. For example, if the user lives in a rural area, the analysis unit can propose an action plan that utilizes the natural environment. For example, if the user is traveling, the analysis unit can propose an action plan for their travel destination. By considering the user's geographical location information, the analysis unit can provide more appropriate analysis results. 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 user's geographical location information data into a generating AI and have the generating AI perform the analysis.

[0121] The generation unit can create an action plan by considering the user's past successes during the generation process. For example, the generation unit can create an action plan that includes similar tasks by referring to tasks the user has successfully completed in the past. For example, the generation unit can set new goals based on goals the user has achieved in the past. For example, the generation unit can analyze the user's successes and create an action plan that incorporates the factors of those successes. This allows for the provision of a more effective action plan by considering past successes. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's success data into a generation AI and have the generation AI create the action plan.

[0122] The visualization unit can optimize the displayed content by referring to the user's past progress data during visualization. For example, the visualization unit can display goals the user has achieved in the past and their progress. For example, the visualization unit can display tasks the user has failed at in the past and the reasons for those failures. For example, the visualization unit can analyze the user's past progress data and provide the most effective display method. This allows for more effective display content by referring to past progress data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the user's progress data into a generating AI and have the generating AI perform the optimization of the displayed content.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The analysis unit analyzes the user's current situation. For example, the analysis unit analyzes basic information entered by the user, such as their current situation, goals, and daily routine. Based on the user's lifestyle patterns and behavioral history, the analysis unit can perform a detailed analysis of the current situation. Step 2: The generation unit creates an action plan based on the information analyzed by the analysis unit. For example, the generation unit creates a specific action plan tailored to the user's goals. The generation unit can set specific daily tasks based on the user's lifestyle patterns and goals. Step 3: The visualization unit visualizes the progress of the action plan created by the generation unit. The visualization unit visually displays the progress of the action plan, for example, using a calendar or graph. The visualization unit can display the completion rate and progress bar of the to-do list, making it easier for users to check the progress. Step 4: The dialogue unit deepens and adjusts the conversation in a dialogue format based on the progress visualized by the visualization unit. For example, the dialogue unit can resolve issues that the user wants to discuss or problems they are facing through dialogue with the AI. The dialogue unit can estimate the user's emotions and adjust the content of the conversation based on those emotions.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the analysis unit, generation unit, visualization unit, dialogue unit, reminder unit, display unit, and reception unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The visualization unit is implemented by the display 40A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The dialogue unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The reminder unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The display unit is implemented by the display 40A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented, for example, by the reception device 38 of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the analysis unit, generation unit, visualization unit, dialogue unit, reminder unit, display unit, and reception unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The visualization unit is implemented, for example, by the display of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The dialogue unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The reminder unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The display unit is implemented, for example, by the display of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented, for example, by the microphone 238 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the analysis unit, generation unit, visualization unit, dialogue unit, reminder unit, display unit, and reception unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The visualization unit is implemented by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The dialogue unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The reminder unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The display unit is implemented by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented, for example, by the microphone 238 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the analysis unit, generation unit, visualization unit, dialogue unit, reminder unit, display unit, and reception unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The visualization unit is implemented, for example, by the display of the robot 414 or the specific processing unit 290 of the data processing unit 12. The dialogue unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The reminder unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The display unit is implemented, for example, by the display of the robot 414 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented, for example, by the microphone 238 of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The analysis department analyzes the current situation, A generation unit creates an action plan based on the information analyzed by the analysis unit, A visualization unit that visualizes the progress of the action plan created by the generation unit, The system includes a dialogue unit that performs in-depth analysis and adjustments in a dialogue format based on the progress visualized by the aforementioned visualization unit. A system characterized by the following features. (Note 2) It has a reminder section that uses a calendar to set reminders. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a display unit that shows the completion rate of the to-do list. The system described in Appendix 1, characterized by the features described herein. (Note 4) The facility includes a reception desk to receive inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze the user's past behavior history and improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, During analysis, the analysis method is customized to take into account the user's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, the system analyzes users' social media activity and incorporates relevant information into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is The system estimates the user's emotions and adjusts the content of the action plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, an action plan is created that takes into account the user's past successes. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the difficulty level of the action plan is adjusted according to the user's progress toward achieving their goals. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and prioritizes the action plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating the plan, the user's geographical location information is taken into consideration to create the action plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the system analyzes the user's social media activity and proposes relevant action plans. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned visualization unit, It estimates the user's emotions and adjusts how progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned visualization unit, When visualizing data, the display content is optimized by referencing the user's past progress data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, When visualization is performed, the level of detail displayed is adjusted according to the user's progress toward achieving their goals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display order of progress based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, When visualizing progress, the user's geographical location is taken into consideration when displaying the progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, During visualization, the system analyzes the user's social media activity and displays relevant progress information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the content of the conversation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned dialogue unit, During conversations, the system provides optimal dialogue content by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, During the conversation, adjust the level of detail in the dialogue according to the user's progress toward achieving their goals. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, During conversations, the system provides dialogue content that takes the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and provides relevant conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reminder unit, It estimates the user's emotions and adjusts the timing of reminders based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned reminder unit, When sending a reminder, the system refers to the user's past reminder history to provide the most suitable reminder method. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned reminder unit, It estimates the user's emotions and determines the priority of reminders based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned reminder unit, When sending reminders, the content of the reminders will take into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned display unit is It estimates the user's emotions and adjusts the displayed content based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned display unit is When displaying content, the system provides the optimal display method by referring to the user's past viewing history. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned display unit is When displaying content, the content should be provided taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned reception unit is The system estimates the user's emotions and adjusts the request based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned reception unit is During registration, the system refers to the user's past registration history to provide the most suitable registration method. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the reception process based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned reception unit is When registering, the registration details will be provided taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes the current situation, A generation unit creates an action plan based on the information analyzed by the analysis unit, A visualization unit that visualizes the progress of the action plan created by the generation unit, The system includes a dialogue unit that performs in-depth analysis and adjustments in a dialogue format based on the progress visualized by the aforementioned visualization unit. A system characterized by the following features.

2. It has a reminder section that uses a calendar to set reminders. The system according to feature 1.

3. It features a display unit that shows the completion rate of the to-do list. The system according to feature 1.

4. The facility includes a reception desk to receive inquiries. The system according to feature 1.

5. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system according to feature 1.

6. The aforementioned analysis unit, Analyze the user's past behavior history and improve the accuracy of the analysis. The system according to feature 1.

7. The aforementioned analysis unit, During analysis, the analysis method is customized to take into account the user's lifestyle patterns. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, the system analyzes users' social media activity and incorporates relevant information into the analysis. The system according to feature 1.

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