System

Abstract

To develop an action plan to support goal achievement of a user, and manage progress.SOLUTION: A system according to an embodiment comprises goal determination unit, a plan determination unit, an acquisition unit, and a feedback unit. The goal determination unit determines a user goal on the basis of interaction with the user. The plan determination unit determines an action plan for achieving the goal determined by the goal determination unit on the basis of interaction with the user. The acquisition unit acquires information indicative of the progress of the user in the action plan determined by the plan determination unit. The feedback unit provides the user with feedback on the basis of the information acquired by the acquisition unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately formulate action plans or manage progress to help users achieve their goals, and there is room for improvement.

[0005] The system according to the embodiment aims to formulate an action plan to assist a user in achieving a goal and to manage the progress. [Means for solving the problem]

[0006] A system according to an embodiment includes a goal determination unit, a plan determination unit, an acquisition unit, and a feedback unit. The goal determination unit determines a goal of a user based on a dialogue with the user. The plan determination unit determines an action plan for achieving the goal determined by the goal determination unit based on the dialogue with the user. The acquisition unit acquires information indicating the user's progress in the action plan determined by the plan determination unit. The feedback unit provides feedback to the user based on the information acquired by the acquisition unit. Effect of the Invention

[0007] The system according to the embodiment can formulate an action plan to assist a user in achieving a goal and manage the progress. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The interactive application AI assistant system for self-development and self-growth according to the embodiment of the present invention is a system that supports the self-development and self-growth of a user. Based on a dialogue with the user, the system clarifies the user's long-term and short-term goals and jointly creates an action plan to achieve them. Next, the system tracks the user's progress toward achieving the goal and provides timely feedback and encouraging messages. In addition, the system provides a space for reflecting on daily experiences and feelings, and supports deepening self-awareness based on them. Furthermore, the system provides questions and quizzes to discover the user's qualities and strengths, and provides advice on self-growth based on them. In addition, the system recommends resources such as books, articles, and videos related to self-growth, and provides opportunities for continuous learning. Finally, the system provides guided meditation sessions and relaxation techniques for mental stability and refreshment. For example, the user sets a goal such as "I want to be able to speak English fluently within one year." This goal is determined by the AI ​​by analyzing the user's behavior history and dialogue content. Next, the system determines an action plan for achieving the determined goal based on the dialogue with the user. For example, a specific plan is created, such as studying English for 30 minutes every day and attending an English conversation class once a week. The AI ​​optimizes this plan based on the user's feedback. In addition, information is acquired to track the user's progress. For example, information is collected about how much time the user spent on a learning app and whether they attended an English conversation class. This information can also be acquired by the AI ​​analyzing social media data. Based on the acquired information, feedback and messages are provided to the user. For example, encouraging messages are provided if the user is progressing smoothly toward their goal, and special motivational messages are provided if progress has stagnated. It can also provide success stories and feedback from other users. In addition, questions and quizzes are provided to discover the user's qualities and strengths. For example, the user can discover their strengths and qualities by answering questions about self-growth. Based on this information, the AI ​​provides self-growth advice. It also recommends self-growth resources to the user.For example, by recommending books, articles, videos, etc. related to English learning, users can continue to learn. Finally, they can be provided with guided meditation sessions and relaxation techniques. For example, by measuring the user's stress level in real time and providing a meditation session based on the results, it is possible to stabilize and refresh the user's mind. In this way, an interactive application AI assistant system for self-development and self-growth can consistently support users from goal setting to progress management and feedback provision.

[0029] An interactive application AI assistant system for self-development and self-growth according to an embodiment includes a goal determination unit, a plan determination unit, an acquisition unit, and a feedback unit. The goal determination unit determines a goal of a user based on a dialogue with the user. For example, the user sets a goal such as "I want to be able to speak English fluently within one year." This goal is determined by an AI analyzing the user's behavior history and dialogue content. The plan determination unit determines an action plan for achieving the goal determined by the goal determination unit based on a dialogue with the user. For example, a specific plan is created such as studying English for 30 minutes every day and attending an English conversation class once a week. This plan is optimized by an AI based on user feedback. The acquisition unit acquires information indicating the user's progress in the action plan determined by the plan determination unit. For example, information such as how much time the user spent on a learning app or attended an English conversation class is collected. This information can also be acquired by an AI analyzing social media data. The feedback unit provides feedback or a message to the user based on the information acquired by the acquisition unit. For example, if the user is making good progress toward a goal, the system can provide an encouraging message, and if the progress is stagnant, a special motivational message can be provided. It can also provide success stories and feedback from other users. In this way, the interactive application AI assistant system for self-development and self-growth according to the embodiment can consistently support the user from goal setting to progress management and feedback provision.

[0030] The goal determination unit determines the user's goal based on a dialogue with the user. For example, the user sets a goal such as "I want to be able to speak English fluently within one year." This goal is determined by the AI ​​by analyzing the user's behavior history and the content of the dialogue. Specifically, the AI ​​grasps the user's interests, past experiences, current skill level, etc. in detail through dialogue with the user. For example, it collects information such as what learning methods the user has tried in the past, how often they study, and what learning materials they use. In addition, the AI ​​analyzes the content of the user's dialogue using natural language processing technology to understand the user's intentions and emotions. For example, if a user says "I want to be able to speak English," additional questions can be asked to delve into the specific motivations and goals behind it. This allows the AI ​​to set the user's goal to be more specific and realistic. In addition, by analyzing the user's behavior history, the AI ​​can propose optimal goals to the user by taking into account past successes and failures. For example, for a user who has tried to learn English in the past but failed, a more realistic and achievable goal can be set. This enables the goal determination unit to set optimal goals according to the individual needs and circumstances of the user, thereby increasing the motivation of the user.

[0031] The plan determination unit determines an action plan for achieving the goal determined by the goal determination unit based on a dialogue with the user. For example, a specific plan is created, such as studying English for 30 minutes every day or attending an English conversation class once a week. This plan is optimized by the AI ​​based on the user's feedback. Specifically, the AI ​​considers the user's life rhythm and learning style and proposes an optimal study schedule. For example, if the user has a busy job, it can propose a short and effective study method. In addition, the AI ​​collects the user's feedback in real time and evaluates the progress and effectiveness of the plan. For example, if the user feels that "studying for 30 minutes every day is difficult," the AI ​​can adjust the study time based on that feedback and propose a more realistic plan. In addition, the AI ​​analyzes the user's learning data and provides advice to maximize the effectiveness of learning. For example, if the user is not good at a particular area, it can propose a study plan that focuses on that area. As a result, the plan determination unit can provide the optimal action plan according to the user's individual needs and situation, and support the achievement of the goal.

[0032] The acquisition unit acquires information indicating the user's progress in the action plan determined by the plan determination unit. For example, the acquisition unit collects information such as how much time the user spent on a learning app or whether the user attended an English conversation class. This information can also be acquired by AI analyzing social media data. Specifically, the acquisition unit collects data directly from the user's device or application. For example, the acquisition unit automatically acquires the usage history of the learning app and the attendance record of the English conversation class and stores them in a central database. The acquisition unit also collects information and feedback about learning posted by the user on social media and uses it to grasp the progress status. Furthermore, the acquisition unit collects the user's feedback in real time and evaluates the progress and effectiveness of the plan. For example, if the user feels that "studying for 30 minutes every day is difficult," the acquisition unit can adjust the study time based on the feedback and propose a more realistic plan. This allows the acquisition unit to accurately grasp the user's progress and provide data to maximize the effectiveness of the plan.

[0033] The feedback unit provides feedback or messages to the user based on the information acquired by the acquisition unit. For example, if the user is progressing smoothly toward the goal, an encouraging message is provided, and if the progress is stagnant, a special motivational message is provided. It can also provide success stories and feedback from other users. Specifically, the feedback unit uses AI to analyze the user's progress and generate appropriate feedback. For example, if the user achieves a certain level of progress in a learning app, the AI ​​generates an encouraging message based on that data and sends it to the user. In addition, if the progress is stagnant, the AI ​​analyzes the cause and provides specific advice and a message to increase motivation. Furthermore, the feedback unit can collect success stories and feedback from other users and share them with the user to increase motivation. For example, it can provide useful information to the user by sharing the specific methods and experiences of how other users with the same goal achieved their goals. As a result, the feedback unit can provide appropriate feedback according to the user's progress, maintain the user's motivation, and support goal achievement.

[0034] The interactive application AI assistant system for self-development and self-growth includes a providing unit that provides questions and quizzes for discovering the qualities and strengths of a user. The providing unit provides questions and quizzes for discovering the qualities and strengths of a user. For example, a user can discover his / her strengths and qualities by answering questions about self-growth. Based on this information, an AI provides advice for self-growth. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit inputs the user's answer data into a generating AI, which can analyze the user's qualities and strengths and provide advice. This makes it possible to discover the user's qualities and strengths and support self-growth.

[0035] An interactive application AI assistant system for self-development and self-growth includes a recommendation unit that recommends resources related to self-growth to a user. The recommendation unit recommends resources related to self-growth to a user. For example, by recommending books, articles, videos, etc. related to English language learning, the user can continue to learn. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit inputs the user's learning history data to the generation AI, which can then recommend the optimal resource. This makes it possible to recommend resources suitable for the user and support continuous learning.

[0036] An interactive application AI assistant system for self-development and self-growth includes a guide unit that provides a user with a meditation session with guidance. The guide unit provides the user with a meditation session with guidance. For example, the user's stress level is measured in real time, and a meditation session is provided according to the result, thereby achieving mental stability and refreshment. A part or all of the above-mentioned processing in the guide unit may be performed, for example, using AI, or may be performed without using AI. For example, the guide unit inputs the user's stress level data to the generation AI, and the generation AI can provide an optimal meditation session. This can support the user's mental stability and refreshment.

[0037] In the interactive application AI assistant system for self-development and self-growth, the guide unit measures the stress level of the user in real time and provides a meditation session based on the measurement result. The guide unit measures the stress level of the user in real time and provides a meditation session based on the measurement result. For example, the heart rate and electrodermal activity of the user are collected by a sensor, and the stress level is estimated by an emotion estimation algorithm. Based on the estimated stress level, a relaxing meditation session is provided. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guide unit may be performed by using, for example, an AI, or may be performed without using an AI. For example, the guide unit inputs data acquired by a sensor to the generation AI, which estimates the stress level and can provide an optimal meditation session. This makes it possible to provide an appropriate meditation session according to the stress level of the user.

[0038] The interactive application AI assistant system for self-development and self-growth includes an awarding unit that awards a user a reward according to the user's progress based on the information acquired by the acquisition unit. The awarding unit awards the user a reward according to the user's progress based on the information acquired by the acquisition unit. For example, points or badges are awarded when the user achieves a goal. A monetary reward can also be provided when the user completes a specific task. Some or all of the above-mentioned processing in the awarding unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the awarding unit inputs the user's progress data into a generating AI, which determines an optimal reward and awards it to the user. This makes it possible to provide a reward according to the user's progress and maintain motivation.

[0039] The goal determination unit can determine a goal suitable for the user based on a dialogue with the user and the user's behavior history. For example, the goal determination unit analyzes the history of goals set by the user in the past and goals achieved by the user, and proposes an optimal goal to the user. A part or all of the above-mentioned processing in the goal determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal determination unit can input the user's behavior history data to the generation AI, which can analyze and propose a goal suitable for the user. This can increase the possibility of goal achievement by setting an optimal goal for the user.

[0040] The feedback unit can provide a motivational message when the user's progress is stagnant. For example, when the user's progress toward a goal is delayed, the user's motivation is improved by introducing an encouraging message or a success story. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's progress data into a generation AI, which generates and provides a motivational message suitable for the user. This makes it possible to improve the user's motivation with a special message when the user's progress is stagnant.

[0041] The feedback unit can provide feedback based on success stories according to the user's progress. For example, when the user is making good progress toward a goal, the user's motivation is maintained by introducing success stories of other users who have achieved the same goal. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's progress data into the generation AI, which can analyze and provide success stories suitable for the user. This makes it possible to provide appropriate feedback according to the user's progress.

[0042] The acquisition unit can analyze the social media data of the user and acquire the progress information. For example, the acquisition unit analyzes the learning situation and activity content shared by the user on social media and acquires the progress information. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the social media data of the user to the generation AI, which can analyze and acquire the progress information. This makes it possible to acquire the progress information by utilizing the social media data.

[0043] The plan determination unit can optimize the contents of the action plan based on the user's feedback. For example, the plan determination unit analyzes the feedback provided by the user for the action plan and adjusts the contents of the plan. A part or all of the above-mentioned processing in the plan determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan determination unit inputs the user's feedback data to the generation AI, and the generation AI can optimize the action plan. This allows the action plan to be optimized by reflecting the user's feedback.

[0044] The goal determination unit can analyze the user's past goal achievement history and select the optimal goal setting method. For example, the pattern of goals that the user has achieved in the past is analyzed and a similar goal setting method is proposed. A part or all of the above-mentioned processing in the goal determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal determination unit can input the user's goal achievement history data to the generation AI, which can analyze and propose the optimal goal setting method. This makes it possible to provide the optimal goal setting method by analyzing the past goal achievement history.

[0045] The goal determination unit can customize the goal based on the user's current living situation and areas of interest when determining the goal. For example, the user sets a realistic goal in consideration of the user's current work and academic situation. Some or all of the above-mentioned processing in the goal determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal determination unit inputs the user's living situation data into the generation AI, and the generation AI can customize an optimal goal. This allows the goal to be customized based on the user's living situation and areas of interest.

[0046] The goal determination unit can set a highly relevant goal in consideration of the geographical location information of the user when determining a goal. For example, a goal related to the area is set in consideration of the characteristics of the area in which the user lives. A part or all of the above-mentioned processing in the goal determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal determination unit can input the geographical location information data of the user to the generation AI, and the generation AI can set a highly relevant goal. This allows the goal to be set in consideration of the geographical location information of the user.

[0047] The goal determination unit can analyze the user's social media activity and set a related goal when determining the goal. For example, the goal is set based on topics in which the user is interested on social media. Some or all of the above-mentioned processing in the goal determination unit can be performed, for example, using AI or can be performed without using AI. For example, the goal determination unit can input the user's social media activity data to the generation AI, which can set a related goal. This allows the user's social media activity to be analyzed and the goal to be set.

[0048] The plan determination unit can analyze the user's past action plan history and select an optimal plan setting method. For example, the plan determination unit analyzes the patterns of action plans that have been successful in the past and proposes a similar plan. A part or all of the above-mentioned processing in the plan determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan determination unit inputs the user's action plan history data to a generation AI, which analyzes and proposes an optimal plan setting method. This makes it possible to provide an optimal plan setting method by analyzing past action plan history.

[0049] When determining an action plan, the plan determination unit can customize the plan based on the user's current living situation and areas of interest. For example, the user sets a realistic plan taking into account the user's current work and academic situation. Some or all of the above-mentioned processing in the plan determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan determination unit inputs the user's living situation data into the generation AI, and the generation AI can customize an optimal plan. This allows the plan to be customized based on the user's living situation and areas of interest.

[0050] When determining an action plan, the plan determination unit can set a highly relevant plan taking into account the geographical location information of the user. For example, a plan related to the area is set taking into account the characteristics of the area in which the user lives. A part or all of the above-mentioned processing in the plan determination unit may be performed using AI, for example, or may be performed without using AI. For example, the plan determination unit can input the geographical location information data of the user to the generation AI, which can set a highly relevant plan. This allows the plan to be set taking into account the geographical location information of the user.

[0051] When determining an action plan, the plan determination unit can analyze the user's social media activity and set a related plan. For example, the plan is set based on topics in which the user is interested on social media. Some or all of the above-mentioned processing in the plan determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan determination unit can input the user's social media activity data to the generation AI, which can set a related plan. This makes it possible to analyze the user's social media activity and set a plan.

[0052] The acquisition unit can analyze the user's past progress information and select the optimal acquisition method. For example, the acquisition unit can analyze a progress information acquisition method that was successful for the user in the past and propose a similar method. A part or all of the above-mentioned processing in the acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the acquisition unit can input the user's progress information data to a generation AI, which can analyze and propose the optimal acquisition method. This makes it possible to provide the optimal acquisition method by analyzing past progress information.

[0053] The acquisition unit can perform filtering based on the user's current living situation and areas of interest when acquiring the progress information. For example, the user acquires relevant progress information taking into account the user's current work or academic situation. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's living situation data to the generation AI, and the generation AI can filter the optimal progress information. This allows the progress information to be filtered based on the user's living situation and areas of interest.

[0054] When acquiring progress information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user. For example, the progress information related to the area is acquired in consideration of the characteristics of the area where the user lives. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit inputs the geographical location information data of the user to the generation AI, and the generation AI can acquire highly relevant progress information. This makes it possible to acquire progress information in consideration of the geographical location information of the user.

[0055] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring the progress information. For example, the progress information is acquired based on topics in which the user is interested on social media. Some or all of the above-mentioned processing in the acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the acquisition unit can input the user's social media activity data to the generation AI, which can acquire the related progress information. This makes it possible to analyze the user's social media activity and acquire the progress information.

[0056] The feedback unit can adjust the level of detail of the feedback based on the importance of the progress information at the time of feedback. For example, detailed feedback is provided for progress information with high importance. A part or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI, or can be performed without using AI. For example, the feedback unit can input the progress information data to a generation AI, and the generation AI can adjust the level of detail of the feedback. This makes it possible to adjust the level of detail of the feedback based on the importance of the progress information.

[0057] The feedback unit can apply different feedback algorithms depending on the category of progress information at the time of feedback. For example, feedback based on a learning algorithm is provided for learning progress. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input progress information data to a generation AI, and the generation AI can apply a feedback algorithm according to the category. This makes it possible to provide optimal feedback according to the category of progress information.

[0058] The feedback unit can determine the priority of the feedback based on the submission time of the progress information at the time of feedback. For example, the feedback unit provides feedback preferentially to the progress information that was submitted most recently. A part or all of the above-mentioned processing in the feedback unit can be performed, for example, using an AI, or can be performed without using an AI. For example, the feedback unit can input the progress information data to a generation AI, and the generation AI can determine the priority of the feedback based on the submission time. This makes it possible to adjust the priority of the feedback based on the submission time of the progress information.

[0059] The feedback unit can adjust the order of feedback based on the relevance of the progress information at the time of feedback. For example, the feedback is provided in order of the most relevant progress information. A part or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI, or can be performed without using AI. For example, the feedback unit can input the progress information data to a generation AI, and the generation AI can adjust the order of feedback based on the relevance. This makes it possible to adjust the order of feedback based on the relevance of the progress information.

[0060] When providing questions or quizzes, the providing unit can select the most appropriate content by referring to the user's past answer history. For example, the difficulty level is adjusted based on questions that the user has answered correctly in the past. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's answer history data to the generating AI, which can select the most appropriate question or quiz. This makes it possible to provide the most appropriate question or quiz by referring to the past answer history.

[0061] The providing unit can provide optimal content when providing questions or quizzes by taking into account the geographical location information of the user. For example, questions related to the area in which the user lives are provided by taking into account the characteristics of the area in which the user lives. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the geographical location information data of the user to a generating AI, which can provide optimal questions or quizzes. This makes it possible to provide questions or quizzes by taking into account the geographical location information of the user.

[0062] When recommending a resource, the recommendation unit can select an optimal resource by referring to the user's past learning history. For example, a related resource is recommended based on the content that the user has learned in the past. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit inputs the user's learning history data to the generation AI, and the generation AI can select an optimal resource. This makes it possible to provide an optimal resource by referring to the past learning history.

[0063] When recommending resources, the recommendation unit can provide optimal resources by taking into account the geographical location information of the user. For example, resources related to the area in which the user lives are recommended by taking into account the characteristics of the area. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit inputs the geographical location information data of the user to the generation AI, which can provide optimal resources. This allows resources to be provided by taking into account the geographical location information of the user.

[0064] When providing a meditation session or a relaxation method, the guide unit can select the optimal content by referring to the user's past stress level history. For example, the guide unit re-provides a meditation session that the user found relaxing in the past. Some or all of the above-mentioned processing in the guide unit may be performed, for example, using AI, or may be performed without using AI. For example, the guide unit inputs the user's stress level history data to the generation AI, which can select the optimal meditation session or relaxation method. This makes it possible to provide the optimal meditation session or relaxation method by referring to the past stress level history.

[0065] When providing a meditation session or a relaxation method, the guide unit can provide optimal content by taking into account the geographical location information of the user. For example, a relaxation method related to the area is provided by taking into account the characteristics of the area in which the user lives. Some or all of the above-mentioned processing in the guide unit may be performed, for example, using AI, or may be performed without using AI. For example, the guide unit inputs the geographical location information data of the user to the generation AI, which can provide an optimal meditation session or relaxation method. This makes it possible to provide a meditation session or relaxation method by taking into account the geographical location information of the user.

[0066] When granting a reward, the granting unit can select an optimal reward by referring to the user's past progress history. For example, the granting unit re-provides a reward that the user was pleased with in the past. Some or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit inputs the user's progress history data into a generation AI, which can select an optimal reward. This allows the optimal reward to be provided by referring to the past progress history.

[0067] The granting unit can provide an optimal reward in consideration of the geographical location information of the user when granting a reward. For example, a reward related to the area is provided in consideration of the characteristics of the area where the user lives. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit inputs the geographical location information data of the user to the generation AI, and the generation AI can provide an optimal reward. This makes it possible to provide a reward in consideration of the geographical location information of the user.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The goal determination unit can analyze the user's past goal achievement history and select the optimal goal setting method. For example, the pattern of goals that the user has achieved in the past is analyzed and a similar goal setting method is proposed. A part or all of the above-mentioned processing in the goal determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal determination unit can input the user's goal achievement history data to the generation AI, which can analyze and propose the optimal goal setting method. This makes it possible to provide the optimal goal setting method by analyzing the past goal achievement history.

[0070] The goal determination unit can analyze the user's past goal achievement history and select the optimal goal setting method. For example, the pattern of goals that the user has achieved in the past is analyzed and a similar goal setting method is proposed. A part or all of the above-mentioned processing in the goal determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal determination unit can input the user's goal achievement history data to the generation AI, which can analyze and propose the optimal goal setting method. This makes it possible to provide the optimal goal setting method by analyzing the past goal achievement history.

[0071] The plan determination unit can analyze the user's past action plan history and select an optimal plan setting method. For example, the plan determination unit analyzes the patterns of action plans that have been successful in the past and proposes a similar plan. A part or all of the above-mentioned processing in the plan determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan determination unit inputs the user's action plan history data to a generation AI, which analyzes and proposes an optimal plan setting method. This makes it possible to provide an optimal plan setting method by analyzing past action plan history.

[0072] The acquisition unit can perform filtering based on the user's current living situation and areas of interest when acquiring the progress information. For example, the user acquires relevant progress information taking into account the user's current work or academic situation. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's living situation data to the generation AI, and the generation AI can filter the optimal progress information. This allows the progress information to be filtered based on the user's living situation and areas of interest.

[0073] The feedback unit can adjust the level of detail of the feedback based on the importance of the progress information at the time of feedback. For example, detailed feedback is provided for progress information with high importance. A part or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI, or can be performed without using AI. For example, the feedback unit can input the progress information data to a generation AI, and the generation AI can adjust the level of detail of the feedback. This makes it possible to adjust the level of detail of the feedback based on the importance of the progress information.

[0074] When recommending a resource, the recommendation unit can select an optimal resource by referring to the user's past learning history. For example, a related resource is recommended based on the content that the user has learned in the past. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit inputs the user's learning history data to the generation AI, and the generation AI can select an optimal resource. This makes it possible to provide an optimal resource by referring to the past learning history.

[0075] The process flow of the first embodiment will be briefly described below.

[0076] Step 1: The goal determination unit determines the user's goal based on the dialogue with the user. For example, the user sets a goal such as "I want to be able to speak English fluently within one year." This goal is determined by the AI ​​by analyzing the user's behavioral history and dialogue content. Step 2: The plan determination unit determines an action plan for achieving the goal determined by the goal determination unit based on a dialogue with the user. For example, a specific plan is created, such as studying English for 30 minutes every day or attending an English conversation class once a week. This plan is optimized by AI based on user feedback. Step 3: The acquisition unit acquires information showing the user's progress in the action plan determined by the plan determination unit. For example, information such as how much time the user spent on a learning app or whether the user attended an English conversation class can be collected. This information can also be acquired by the AI ​​analyzing social media data. Step 4: The feedback unit provides feedback or messages to the user based on the information acquired by the acquisition unit. For example, the feedback unit may provide encouraging messages if the user is making good progress toward a goal, or special motivational messages if progress has stalled. It may also provide success stories and feedback from other users.

[0077] (Example 2) The interactive application AI assistant system for self-development and self-growth according to the embodiment of the present invention is a system that supports the self-development and self-growth of a user. Based on a dialogue with the user, the system clarifies the user's long-term and short-term goals and jointly creates an action plan to achieve them. Next, the system tracks the user's progress toward achieving the goal and provides timely feedback and encouraging messages. In addition, the system provides a space for reflecting on daily experiences and feelings, and supports deepening self-awareness based on them. Furthermore, the system provides questions and quizzes to discover the user's qualities and strengths, and provides advice on self-growth based on them. In addition, the system recommends resources such as books, articles, and videos related to self-growth, and provides opportunities for continuous learning. Finally, the system provides guided meditation sessions and relaxation techniques for mental stability and refreshment. For example, the user sets a goal such as "I want to be able to speak English fluently within one year." This goal is determined by the AI ​​by analyzing the user's behavior history and dialogue content. Next, the system determines an action plan for achieving the determined goal based on the dialogue with the user. For example, a specific plan is created, such as studying English for 30 minutes every day and attending an English conversation class once a week. The AI ​​optimizes this plan based on the user's feedback. In addition, information is acquired to track the user's progress. For example, information is collected about how much time the user spent on a learning app and whether they attended an English conversation class. This information can also be acquired by the AI ​​analyzing social media data. Based on the acquired information, feedback and messages are provided to the user. For example, encouraging messages are provided if the user is progressing smoothly toward their goal, and special motivational messages are provided if progress has stagnated. It can also provide success stories and feedback from other users. In addition, questions and quizzes are provided to discover the user's qualities and strengths. For example, the user can discover their strengths and qualities by answering questions about self-growth. Based on this information, the AI ​​provides self-growth advice. It also recommends self-growth resources to the user.For example, by recommending books, articles, videos, etc. related to English learning, users can continue to learn. Finally, they can be provided with guided meditation sessions and relaxation techniques. For example, by measuring the user's stress level in real time and providing a meditation session based on the results, it is possible to stabilize and refresh the user's mind. In this way, an interactive application AI assistant system for self-development and self-growth can consistently support users from goal setting to progress management and feedback provision.

[0078] An interactive application AI assistant system for self-development and self-growth according to an embodiment includes a goal determination unit, a plan determination unit, an acquisition unit, and a feedback unit. The goal determination unit determines a goal of a user based on a dialogue with the user. For example, the user sets a goal such as "I want to be able to speak English fluently within one year." This goal is determined by an AI analyzing the user's behavior history and dialogue content. The plan determination unit determines an action plan for achieving the goal determined by the goal determination unit based on a dialogue with the user. For example, a specific plan is created such as studying English for 30 minutes every day and attending an English conversation class once a week. This plan is optimized by an AI based on user feedback. The acquisition unit acquires information indicating the user's progress in the action plan determined by the plan determination unit. For example, information such as how much time the user spent on a learning app or attended an English conversation class is collected. This information can also be acquired by an AI analyzing social media data. The feedback unit provides feedback or a message to the user based on the information acquired by the acquisition unit. For example, if the user is making good progress toward a goal, the system can provide an encouraging message, and if the progress is stagnant, a special motivational message can be provided. It can also provide success stories and feedback from other users. In this way, the interactive application AI assistant system for self-development and self-growth according to the embodiment can consistently support the user from goal setting to progress management and feedback provision.

[0079] The goal determination unit determines the user's goal based on a dialogue with the user. For example, the user sets a goal such as "I want to be able to speak English fluently within one year." This goal is determined by the AI ​​by analyzing the user's behavior history and the content of the dialogue. Specifically, the AI ​​grasps the user's interests, past experiences, current skill level, etc. in detail through dialogue with the user. For example, it collects information such as what learning methods the user has tried in the past, how often they study, and what learning materials they use. In addition, the AI ​​analyzes the content of the user's dialogue using natural language processing technology to understand the user's intentions and emotions. For example, if a user says "I want to be able to speak English," additional questions can be asked to delve into the specific motivations and goals behind it. This allows the AI ​​to set the user's goal to be more specific and realistic. In addition, by analyzing the user's behavior history, the AI ​​can propose optimal goals to the user by taking into account past successes and failures. For example, for a user who has tried to learn English in the past but failed, a more realistic and achievable goal can be set. This enables the goal determination unit to set optimal goals according to the individual needs and circumstances of the user, thereby increasing the motivation of the user.

[0080] The plan determination unit determines an action plan for achieving the goal determined by the goal determination unit based on a dialogue with the user. For example, a specific plan is created, such as studying English for 30 minutes every day or attending an English conversation class once a week. This plan is optimized by the AI ​​based on the user's feedback. Specifically, the AI ​​considers the user's life rhythm and learning style and proposes an optimal study schedule. For example, if the user has a busy job, it can propose a short and effective study method. In addition, the AI ​​collects the user's feedback in real time and evaluates the progress and effectiveness of the plan. For example, if the user feels that "studying for 30 minutes every day is difficult," the AI ​​can adjust the study time based on that feedback and propose a more realistic plan. In addition, the AI ​​analyzes the user's learning data and provides advice to maximize the effectiveness of learning. For example, if the user is not good at a particular area, it can propose a study plan that focuses on that area. As a result, the plan determination unit can provide the optimal action plan according to the user's individual needs and situation, and support the achievement of the goal.

[0081] The acquisition unit acquires information indicating the user's progress in the action plan determined by the plan determination unit. For example, the acquisition unit collects information such as how much time the user spent on a learning app or whether the user attended an English conversation class. This information can also be acquired by AI analyzing social media data. Specifically, the acquisition unit collects data directly from the user's device or application. For example, the acquisition unit automatically acquires the usage history of the learning app and the attendance record of the English conversation class and stores them in a central database. The acquisition unit also collects information and feedback about learning posted by the user on social media and uses it to grasp the progress status. Furthermore, the acquisition unit collects the user's feedback in real time and evaluates the progress and effectiveness of the plan. For example, if the user feels that "studying for 30 minutes every day is difficult," the acquisition unit can adjust the study time based on the feedback and propose a more realistic plan. This allows the acquisition unit to accurately grasp the user's progress and provide data to maximize the effectiveness of the plan.

[0082] The feedback unit provides feedback or messages to the user based on the information acquired by the acquisition unit. For example, if the user is progressing smoothly toward the goal, an encouraging message is provided, and if the progress is stagnant, a special motivational message is provided. It can also provide success stories and feedback from other users. Specifically, the feedback unit uses AI to analyze the user's progress and generate appropriate feedback. For example, if the user achieves a certain level of progress in a learning app, the AI ​​generates an encouraging message based on that data and sends it to the user. In addition, if the progress is stagnant, the AI ​​analyzes the cause and provides specific advice and a message to increase motivation. Furthermore, the feedback unit can collect success stories and feedback from other users and share them with the user to increase motivation. For example, it can provide useful information to the user by sharing the specific methods and experiences of how other users with the same goal achieved their goals. As a result, the feedback unit can provide appropriate feedback according to the user's progress, maintain the user's motivation, and support goal achievement.

[0083] The interactive application AI assistant system for self-development and self-growth includes a providing unit that provides questions and quizzes for discovering the qualities and strengths of a user. The providing unit provides questions and quizzes for discovering the qualities and strengths of a user. For example, a user can discover his / her strengths and qualities by answering questions about self-growth. Based on this information, an AI provides advice for self-growth. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit inputs the user's answer data into a generating AI, which can analyze the user's qualities and strengths and provide advice. This makes it possible to discover the user's qualities and strengths and support self-growth.

[0084] An interactive application AI assistant system for self-development and self-growth includes a recommendation unit that recommends resources related to self-growth to a user. The recommendation unit recommends resources related to self-growth to a user. For example, by recommending books, articles, videos, etc. related to English language learning, the user can continue to learn. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit inputs the user's learning history data to the generation AI, which can then recommend the optimal resource. This makes it possible to recommend resources suitable for the user and support continuous learning.

[0085] An interactive application AI assistant system for self-development and self-growth includes a guide unit that provides a user with a meditation session with guidance. The guide unit provides the user with a meditation session with guidance. For example, the user's stress level is measured in real time, and a meditation session is provided according to the result, thereby achieving mental stability and refreshment. A part or all of the above-mentioned processing in the guide unit may be performed, for example, using AI, or may be performed without using AI. For example, the guide unit inputs the user's stress level data to the generation AI, and the generation AI can provide an optimal meditation session. This can support the user's mental stability and refreshment.

[0086] In the interactive application AI assistant system for self-development and self-growth, the guide unit measures the stress level of the user in real time and provides a meditation session based on the measurement result. The guide unit measures the stress level of the user in real time and provides a meditation session based on the measurement result. For example, the heart rate and electrodermal activity of the user are collected by a sensor, and the stress level is estimated by an emotion estimation algorithm. Based on the estimated stress level, a relaxing meditation session is provided. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guide unit may be performed by using, for example, an AI, or may be performed without using an AI. For example, the guide unit inputs data acquired by a sensor to the generation AI, which estimates the stress level and can provide an optimal meditation session. This makes it possible to provide an appropriate meditation session according to the stress level of the user.

[0087] The interactive application AI assistant system for self-development and self-growth includes an awarding unit that awards a user a reward according to the user's progress based on the information acquired by the acquisition unit. The awarding unit awards the user a reward according to the user's progress based on the information acquired by the acquisition unit. For example, points or badges are awarded when the user achieves a goal. A monetary reward can also be provided when the user completes a specific task. Some or all of the above-mentioned processing in the awarding unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the awarding unit inputs the user's progress data into a generating AI, which determines an optimal reward and awards it to the user. This makes it possible to provide a reward according to the user's progress and maintain motivation.

[0088] The goal determination unit can determine a goal suitable for the user based on a dialogue with the user and the user's behavior history. For example, the goal determination unit analyzes the history of goals set by the user in the past and goals achieved by the user, and proposes an optimal goal to the user. A part or all of the above-mentioned processing in the goal determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal determination unit can input the user's behavior history data to the generation AI, which can analyze and propose a goal suitable for the user. This can increase the possibility of goal achievement by setting an optimal goal for the user.

[0089] The feedback unit can provide a motivational message when the user's progress is stagnant. For example, when the user's progress toward a goal is delayed, the user's motivation is improved by introducing an encouraging message or a success story. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's progress data into a generation AI, which generates and provides a motivational message suitable for the user. This makes it possible to improve the user's motivation with a special message when the user's progress is stagnant.

[0090] The feedback unit can provide feedback based on success stories according to the user's progress. For example, when the user is making good progress toward a goal, the user's motivation is maintained by introducing success stories of other users who have achieved the same goal. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's progress data into the generation AI, which can analyze and provide success stories suitable for the user. This makes it possible to provide appropriate feedback according to the user's progress.

[0091] The acquisition unit can analyze the social media data of the user and acquire the progress information. For example, the acquisition unit analyzes the learning situation and activity content shared by the user on social media and acquires the progress information. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input the social media data of the user to the generation AI, which can analyze and acquire the progress information. This makes it possible to acquire the progress information by utilizing the social media data.

[0092] The plan determination unit can optimize the contents of the action plan based on the user's feedback. For example, the plan determination unit analyzes the feedback provided by the user for the action plan and adjusts the contents of the plan. A part or all of the above-mentioned processing in the plan determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan determination unit inputs the user's feedback data to the generation AI, and the generation AI can optimize the action plan. This allows the action plan to be optimized by reflecting the user's feedback.

[0093] The goal determination unit can estimate the user's emotion and determine the priority order of goals based on the estimated user's emotion. The goal determination unit can estimate the user's emotion and determine the priority order of goals based on the estimated user's emotion. For example, when the user is feeling stressed, a short-term goal is set as a priority. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the goal determination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the goal determination unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority order of goals. This makes it possible to adjust the priority order of goals based on the user's emotion.

[0094] The goal determination unit can analyze the user's past goal achievement history and select the optimal goal setting method. For example, the pattern of goals that the user has achieved in the past is analyzed and a similar goal setting method is proposed. A part or all of the above-mentioned processing in the goal determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal determination unit can input the user's goal achievement history data to the generation AI, which can analyze and propose the optimal goal setting method. This makes it possible to provide the optimal goal setting method by analyzing the past goal achievement history.

[0095] The goal determination unit can customize the goal based on the user's current living situation and areas of interest when determining the goal. For example, the user sets a realistic goal in consideration of the user's current work and academic situation. Some or all of the above-mentioned processing in the goal determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal determination unit inputs the user's living situation data into the generation AI, and the generation AI can customize an optimal goal. This allows the goal to be customized based on the user's living situation and areas of interest.

[0096] The goal determination unit can estimate the user's emotion and adjust the difficulty of the goal based on the estimated user's emotion. The goal determination unit can estimate the user's emotion and adjust the difficulty of the goal based on the estimated user's emotion. For example, if the user is feeling stressed, a goal with a low difficulty is set. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the goal determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the goal determination unit can input the user's emotion data to the generation AI, and the generation AI can adjust the difficulty of the goal. This makes it possible to adjust the difficulty of the goal based on the user's emotion.

[0097] The goal determination unit can set a highly relevant goal in consideration of the geographical location information of the user when determining a goal. For example, a goal related to the area is set in consideration of the characteristics of the area in which the user lives. A part or all of the above-mentioned processing in the goal determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal determination unit can input the geographical location information data of the user to the generation AI, and the generation AI can set a highly relevant goal. This allows the goal to be set in consideration of the geographical location information of the user.

[0098] The goal determination unit can analyze the user's social media activity and set a related goal when determining the goal. For example, the goal is set based on topics in which the user is interested on social media. Some or all of the above-mentioned processing in the goal determination unit can be performed, for example, using AI or can be performed without using AI. For example, the goal determination unit can input the user's social media activity data to the generation AI, which can set a related goal. This allows the user's social media activity to be analyzed and the goal to be set.

[0099] The plan determination unit can estimate the user's emotion and determine the priority of the action plans based on the estimated user's emotion. The plan determination unit can estimate the user's emotion and determine the priority of the action plans based on the estimated user's emotion. For example, if the user is stressed, a simple action plan is prioritized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the plan determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the plan determination unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of the action plans. This makes it possible to adjust the priority of the action plans based on the user's emotion.

[0100] The plan determination unit can analyze the user's past action plan history and select an optimal plan setting method. For example, the plan determination unit analyzes the patterns of action plans that have been successful in the past and proposes a similar plan. A part or all of the above-mentioned processing in the plan determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan determination unit inputs the user's action plan history data to a generation AI, which analyzes and proposes an optimal plan setting method. This makes it possible to provide an optimal plan setting method by analyzing past action plan history.

[0101] When determining an action plan, the plan determination unit can customize the plan based on the user's current living situation and areas of interest. For example, the user sets a realistic plan taking into account the user's current work and academic situation. Some or all of the above-mentioned processing in the plan determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan determination unit inputs the user's living situation data into the generation AI, and the generation AI can customize an optimal plan. This allows the plan to be customized based on the user's living situation and areas of interest.

[0102] When determining an action plan, the plan determination unit can set a highly relevant plan taking into account the geographical location information of the user. For example, a plan related to the area is set taking into account the characteristics of the area in which the user lives. A part or all of the above-mentioned processing in the plan determination unit may be performed using AI, for example, or may be performed without using AI. For example, the plan determination unit can input the geographical location information data of the user to the generation AI, which can set a highly relevant plan. This allows the plan to be set taking into account the geographical location information of the user.

[0103] When determining an action plan, the plan determination unit can analyze the user's social media activity and set a related plan. For example, the plan is set based on topics in which the user is interested on social media. Some or all of the above-mentioned processing in the plan determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan determination unit can input the user's social media activity data to the generation AI, which can set a related plan. This makes it possible to analyze the user's social media activity and set a plan.

[0104] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the progress information based on the estimated user's emotion. The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the progress information based on the estimated user's emotion. For example, if the user is feeling stressed, the frequency of acquiring the progress information is reduced. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the generation AI, and the generation AI can adjust the timing of acquiring the progress information. This makes it possible to adjust the timing of acquiring the progress information based on the user's emotion.

[0105] The acquisition unit can analyze the user's past progress information and select the optimal acquisition method. For example, the acquisition unit can analyze a progress information acquisition method that was successful for the user in the past and propose a similar method. A part or all of the above-mentioned processing in the acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the acquisition unit can input the user's progress information data to a generation AI, which can analyze and propose the optimal acquisition method. This makes it possible to provide the optimal acquisition method by analyzing past progress information.

[0106] The acquisition unit can perform filtering based on the user's current living situation and areas of interest when acquiring the progress information. For example, the user acquires relevant progress information taking into account the user's current work or academic situation. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's living situation data to the generation AI, and the generation AI can filter the optimal progress information. This allows the progress information to be filtered based on the user's living situation and areas of interest.

[0107] The acquisition unit can estimate the user's emotion and determine the priority order of the progress information to be acquired based on the estimated user's emotion. The acquisition unit can estimate the user's emotion and determine the priority order of the progress information to be acquired based on the estimated user's emotion. For example, if the user is stressed, the progress information with a lower importance is prioritized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority order of the progress information. This makes it possible to adjust the priority order of the progress information based on the user's emotion.

[0108] When acquiring progress information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user. For example, the progress information related to the area is acquired in consideration of the characteristics of the area where the user lives. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit inputs the geographical location information data of the user to the generation AI, and the generation AI can acquire highly relevant progress information. This makes it possible to acquire progress information in consideration of the geographical location information of the user.

[0109] The acquisition unit can analyze the user's social media activity and acquire related information when acquiring the progress information. For example, the progress information is acquired based on topics in which the user is interested on social media. Some or all of the above-mentioned processing in the acquisition unit can be performed, for example, using AI, or can be performed without using AI. For example, the acquisition unit can input the user's social media activity data to the generation AI, which can acquire the related progress information. This makes it possible to analyze the user's social media activity and acquire the progress information.

[0110] The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. For example, if the user is feeling stressed, the feedback is provided in gentle words. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit inputs the user's emotion data into the generation AI, and the generation AI can adjust the feedback expression method. This makes it possible to adjust the feedback expression method based on the user's emotion.

[0111] The feedback unit can adjust the level of detail of the feedback based on the importance of the progress information at the time of feedback. For example, detailed feedback is provided for progress information with high importance. A part or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI, or can be performed without using AI. For example, the feedback unit can input the progress information data to a generation AI, and the generation AI can adjust the level of detail of the feedback. This makes it possible to adjust the level of detail of the feedback based on the importance of the progress information.

[0112] The feedback unit can apply different feedback algorithms depending on the category of progress information at the time of feedback. For example, feedback based on a learning algorithm is provided for learning progress. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input progress information data to a generation AI, and the generation AI can apply a feedback algorithm according to the category. This makes it possible to provide optimal feedback according to the category of progress information.

[0113] The feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. The feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. For example, if the user is stressed, a short feedback is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data to the generation AI, and the generation AI can adjust the length of the feedback. This makes it possible to adjust the length of the feedback based on the user's emotion.

[0114] The feedback unit can determine the priority of the feedback based on the submission time of the progress information at the time of feedback. For example, the feedback unit provides feedback preferentially to the progress information that was submitted most recently. A part or all of the above-mentioned processing in the feedback unit can be performed, for example, using an AI, or can be performed without using an AI. For example, the feedback unit can input the progress information data to a generation AI, and the generation AI can determine the priority of the feedback based on the submission time. This makes it possible to adjust the priority of the feedback based on the submission time of the progress information.

[0115] The feedback unit can adjust the order of feedback based on the relevance of the progress information at the time of feedback. For example, the feedback is provided in order of the most relevant progress information. A part or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI, or can be performed without using AI. For example, the feedback unit can input the progress information data to a generation AI, and the generation AI can adjust the order of feedback based on the relevance. This makes it possible to adjust the order of feedback based on the relevance of the progress information.

[0116] The providing unit can estimate the user's emotions and adjust the content of the questions and quizzes based on the estimated user's emotions. The providing unit can estimate the user's emotions and adjust the content of the questions and quizzes based on the estimated user's emotions. For example, when the user is feeling stressed, a simple question or quiz is provided. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, and the generation AI can adjust the content of the questions and quizzes. This allows the content of the questions and quizzes to be adjusted based on the user's emotions.

[0117] When providing questions or quizzes, the providing unit can select the most appropriate content by referring to the user's past answer history. For example, the difficulty level is adjusted based on questions that the user has answered correctly in the past. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's answer history data to the generating AI, which can select the most appropriate question or quiz. This makes it possible to provide the most appropriate question or quiz by referring to the past answer history.

[0118] The providing unit can estimate the user's emotions and adjust the order of questions and quizzes based on the estimated user's emotions. The providing unit can estimate the user's emotions and adjust the order of questions and quizzes based on the estimated user's emotions. For example, if the user is feeling stressed, start with simple questions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, and the generation AI can adjust the order of questions and quizzes. This makes it possible to adjust the order of questions and quizzes based on the user's emotions.

[0119] The providing unit can provide optimal content when providing questions or quizzes by taking into account the geographical location information of the user. For example, questions related to the area in which the user lives are provided by taking into account the characteristics of the area in which the user lives. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the geographical location information data of the user to a generating AI, which can provide optimal questions or quizzes. This makes it possible to provide questions or quizzes by taking into account the geographical location information of the user.

[0120] The recommendation unit can estimate the user's emotion and adjust the resource recommendation method based on the estimated user's emotion. The recommendation unit can estimate the user's emotion and adjust the resource recommendation method based on the estimated user's emotion. For example, if the user is stressed, a resource that can relax is recommended. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's emotion data to the generation AI, and the generation AI can adjust the resource recommendation method. This makes it possible to adjust the resource recommendation method based on the user's emotion.

[0121] When recommending a resource, the recommendation unit can select an optimal resource by referring to the user's past learning history. For example, a related resource is recommended based on the content that the user has learned in the past. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit inputs the user's learning history data to the generation AI, and the generation AI can select an optimal resource. This makes it possible to provide an optimal resource by referring to the past learning history.

[0122] The recommendation unit can estimate the user's emotion and determine the priority order of resources based on the estimated user's emotion. The recommendation unit can estimate the user's emotion and determine the priority order of resources based on the estimated user's emotion. For example, if the user is stressed, a resource that allows relaxation is prioritized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority order of resources. This makes it possible to determine the priority order of resources based on the user's emotion.

[0123] When recommending resources, the recommendation unit can provide optimal resources by taking into account the geographical location information of the user. For example, resources related to the area in which the user lives are recommended by taking into account the characteristics of the area. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit inputs the geographical location information data of the user to the generation AI, which can provide optimal resources. This allows resources to be provided by taking into account the geographical location information of the user.

[0124] The guide unit can estimate the user's emotion and adjust the meditation session or relaxation method based on the estimated user's emotion. The guide unit can estimate the user's emotion and adjust the meditation session or relaxation method based on the estimated user's emotion. For example, if the user is feeling stressed, a relaxing meditation session is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guide unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guide unit inputs the user's emotion data into the generation AI, and the generation AI can adjust the meditation session or relaxation method. This makes it possible to adjust the meditation session or relaxation method based on the user's emotion.

[0125] When providing a meditation session or a relaxation method, the guide unit can select the optimal content by referring to the user's past stress level history. For example, the guide unit re-provides a meditation session that the user found relaxing in the past. Some or all of the above-mentioned processing in the guide unit may be performed, for example, using AI, or may be performed without using AI. For example, the guide unit inputs the user's stress level history data to the generation AI, which can select the optimal meditation session or relaxation method. This makes it possible to provide the optimal meditation session or relaxation method by referring to the past stress level history.

[0126] The guide unit can estimate the user's emotion and adjust the order of the meditation sessions and relaxation methods based on the estimated user's emotion. The guide unit can estimate the user's emotion and adjust the order of the meditation sessions and relaxation methods based on the estimated user's emotion. For example, if the user is feeling stressed, the user starts with a session that allows them to relax. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guide unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the guide unit can input the user's emotion data into the generation AI, and the generation AI can adjust the order of the meditation sessions and relaxation methods. This makes it possible to adjust the order of the meditation sessions and relaxation methods based on the user's emotion.

[0127] When providing a meditation session or a relaxation method, the guide unit can provide optimal content by taking into account the geographical location information of the user. For example, a relaxation method related to the area is provided by taking into account the characteristics of the area in which the user lives. Some or all of the above-mentioned processing in the guide unit may be performed, for example, using AI, or may be performed without using AI. For example, the guide unit inputs the geographical location information data of the user to the generation AI, which can provide an optimal meditation session or relaxation method. This makes it possible to provide a meditation session or relaxation method by taking into account the geographical location information of the user.

[0128] The granting unit can estimate the user's emotion and adjust the content of the reward based on the estimated user's emotion. The granting unit can estimate the user's emotion and adjust the content of the reward based on the estimated user's emotion. For example, if the user is feeling stressed, a reward that allows the user to relax is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the granting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the granting unit can input the user's emotion data to the generation AI, and the generation AI can adjust the content of the reward. This makes it possible to adjust the content of the reward based on the user's emotion.

[0129] When granting a reward, the granting unit can select an optimal reward by referring to the user's past progress history. For example, the granting unit re-provides a reward that the user was pleased with in the past. Some or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit inputs the user's progress history data into a generation AI, which can select an optimal reward. This allows the optimal reward to be provided by referring to the past progress history.

[0130] The assigning unit can estimate the user's emotion and determine the priority order of rewards based on the estimated user's emotion. The assigning unit can estimate the user's emotion and determine the priority order of rewards based on the estimated user's emotion. For example, if the user is stressed, a reward that allows relaxation is prioritized. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the assigning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the assigning unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority order of rewards. This makes it possible to determine the priority order of rewards based on the user's emotion.

[0131] The granting unit can provide an optimal reward in consideration of the geographical location information of the user when granting a reward. For example, a reward related to the area is provided in consideration of the characteristics of the area where the user lives. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit inputs the geographical location information data of the user to the generation AI, and the generation AI can provide an optimal reward. This makes it possible to provide a reward in consideration of the geographical location information of the user.

[0132] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0133] The goal determination unit can analyze the user's past goal achievement history and select the optimal goal setting method. For example, the pattern of goals that the user has achieved in the past is analyzed and a similar goal setting method is proposed. A part or all of the above-mentioned processing in the goal determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal determination unit can input the user's goal achievement history data to the generation AI, which can analyze and propose the optimal goal setting method. This makes it possible to provide the optimal goal setting method by analyzing the past goal achievement history.

[0134] The plan determination unit can estimate the user's emotion and determine the priority of the action plan based on the estimated user's emotion. For example, if the user is stressed, a simple action plan is prioritized. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the plan determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the plan determination unit inputs the user's emotion data into the generation AI, and the generation AI can determine the priority of the action plan. This makes it possible to adjust the priority of the action plan based on the user's emotion.

[0135] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the progress information based on the estimated emotion of the user. For example, if the user is feeling stressed, the frequency of acquiring the progress information is reduced. The estimation of the emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit inputs the user's emotion data to the generation AI, and the generation AI can adjust the timing of acquiring the progress information. This makes it possible to adjust the timing of acquiring the progress information based on the user's emotion.

[0136] The feedback unit can estimate the user's emotion and adjust the way of expressing the feedback based on the estimated user's emotion. For example, if the user is feeling stressed, the feedback is provided in gentle words. The estimation of the emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the feedback unit inputs the user's emotion data into the generation AI, and the generation AI can adjust the way of expressing the feedback. This makes it possible to adjust the way of expressing the feedback based on the user's emotion.

[0137] The providing unit can estimate the user's emotions and adjust the content of questions and quizzes based on the estimated user's emotions. For example, when the user is feeling stressed, a simple question or quiz is provided. The estimation of emotions is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit inputs the user's emotion data into the generation AI, and the generation AI can adjust the content of the questions and quizzes. This allows the content of the questions and quizzes to be adjusted based on the user's emotions.

[0138] The goal determination unit can analyze the user's past goal achievement history and select the optimal goal setting method. For example, the pattern of goals that the user has achieved in the past is analyzed and a similar goal setting method is proposed. A part or all of the above-mentioned processing in the goal determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the goal determination unit can input the user's goal achievement history data to the generation AI, which can analyze and propose the optimal goal setting method. This makes it possible to provide the optimal goal setting method by analyzing the past goal achievement history.

[0139] The plan determination unit can analyze the user's past action plan history and select an optimal plan setting method. For example, the plan determination unit analyzes the patterns of action plans that have been successful in the past and proposes a similar plan. A part or all of the above-mentioned processing in the plan determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the plan determination unit inputs the user's action plan history data to a generation AI, which analyzes and proposes an optimal plan setting method. This makes it possible to provide an optimal plan setting method by analyzing past action plan history.

[0140] The acquisition unit can perform filtering based on the user's current living situation and areas of interest when acquiring the progress information. For example, the user acquires relevant progress information taking into account the user's current work or academic situation. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's living situation data to the generation AI, and the generation AI can filter the optimal progress information. This allows the progress information to be filtered based on the user's living situation and areas of interest.

[0141] The feedback unit can adjust the level of detail of the feedback based on the importance of the progress information at the time of feedback. For example, detailed feedback is provided for progress information with high importance. A part or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI, or can be performed without using AI. For example, the feedback unit can input the progress information data to a generation AI, and the generation AI can adjust the level of detail of the feedback. This makes it possible to adjust the level of detail of the feedback based on the importance of the progress information.

[0142] When recommending a resource, the recommendation unit can select an optimal resource by referring to the user's past learning history. For example, a related resource is recommended based on the content that the user has learned in the past. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit inputs the user's learning history data to the generation AI, and the generation AI can select an optimal resource. This makes it possible to provide an optimal resource by referring to the past learning history.

[0143] The process flow of the second embodiment will be briefly described below.

[0144] Step 1: The goal determination unit determines the user's goal based on the dialogue with the user. For example, the user sets a goal such as "I want to be able to speak English fluently within one year." This goal is determined by the AI ​​by analyzing the user's behavioral history and dialogue content. Step 2: The plan determination unit determines an action plan for achieving the goal determined by the goal determination unit based on a dialogue with the user. For example, a specific plan is created, such as studying English for 30 minutes every day or attending an English conversation class once a week. This plan is optimized by AI based on user feedback. Step 3: The acquisition unit acquires information showing the user's progress in the action plan determined by the plan determination unit. For example, information such as how much time the user spent on a learning app or whether the user attended an English conversation class can be collected. This information can also be acquired by the AI ​​analyzing social media data. Step 4: The feedback unit provides feedback or messages to the user based on the information acquired by the acquisition unit. For example, the feedback unit may provide encouraging messages if the user is making good progress toward a goal, or special motivational messages if progress has stalled. It may also provide success stories and feedback from other users.

[0145] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0146] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0147] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] For example, each of the multiple elements including the goal determination unit, the plan determination unit, the acquisition unit, the feedback unit, the provision unit, the recommendation unit, the guide unit, and the assignment unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the goal determination unit is realized by the control unit 46A of the smart device 14 and determines a goal based on a dialogue with the user. The plan determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines an action plan for achieving the goal. The acquisition unit acquires progress information of the user using the camera 42 or the microphone 38B of the smart device 14. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback based on the acquired information. The provision unit is realized by the control unit 46A of the smart device 14 and provides questions and quizzes to discover the user's qualities and strengths. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends resources related to self-growth. The guide unit is realized by the control unit 46A of the smart device 14 and provides a guided meditation session. The granting unit is realized by the specific processing unit 290 of the data processing device 12, and grants a reward according to the user's progress. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0149] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0150] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0151] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0164] For example, each of the multiple elements including the goal determination unit, the plan determination unit, the acquisition unit, the feedback unit, the provision unit, the recommendation unit, the guide unit, and the assignment unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the goal determination unit is realized by the control unit 46A of the smart glasses 214 and determines a goal based on a dialogue with the user. The plan determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines an action plan for achieving the goal. The acquisition unit acquires progress information of the user using the camera 42 and the microphone 238 of the smart glasses 214. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback based on the acquired information. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides questions and quizzes to discover the user's qualities and strengths. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends resources related to self-growth. The guide unit is realized by the control unit 46A of the smart glasses 214 and provides a guided meditation session. The granting unit is realized by the specific processing unit 290 of the data processing device 12, and grants a reward according to the user's progress. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0165] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0166] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0167] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0172] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0175] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0176] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0177] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0178] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0179] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0180] For example, each of the multiple elements including the goal determination unit, the plan determination unit, the acquisition unit, the feedback unit, the provision unit, the recommendation unit, the guide unit, and the assignment unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the goal determination unit is realized by the control unit 46A of the headset type terminal 314, and determines a goal based on a dialogue with the user. The plan determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines an action plan for achieving the goal. The acquisition unit acquires progress information of the user using the camera 42 and the microphone 238 of the headset type terminal 314. The feedback unit is realized by the specific processing unit 290 of the data processing device 12, and provides feedback based on the acquired information. The provision unit is realized by the control unit 46A of the headset type terminal 314, and provides questions and quizzes to discover the user's qualities and strengths. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12, and recommends resources related to self-growth. The guide unit is realized by the control unit 46A of the headset terminal 314, and provides a guided meditation session. The granting unit is realized by the specific processing unit 290 of the data processing device 12, and grants a reward according to the user's progress. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0181] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0182] 7, a 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.

[0183] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0184] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0185] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0186] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0187] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0188] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0189] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0190] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0191] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0192] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0193] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0194] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0195] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0196] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0197] For example, each of the multiple elements including the goal determination unit, the plan determination unit, the acquisition unit, the feedback unit, the provision unit, the recommendation unit, the guide unit, and the assignment unit is realized by at least one of the robot 414 and the data processing device 12. For example, the goal determination unit is realized by the control unit 46A of the robot 414 and determines a goal based on a dialogue with the user. The plan determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines an action plan for achieving the goal. The acquisition unit acquires progress information of the user using the camera 42 and the microphone 238 of the robot 414. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback based on the acquired information. The provision unit is realized by the control unit 46A of the robot 414 and provides questions and quizzes for discovering the user's qualities and strengths. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends resources related to self-growth. The guide unit is realized by the control unit 46A of the robot 414 and provides a guided meditation session. The granting unit is realized by the specific processing unit 290 of the data processing device 12, and grants a reward according to the user's progress. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0198] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0199] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0200] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0201] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0202] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0203] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0204] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0205] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0206] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0207] In addition, 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 upon request from the data processing device 12.

[0208] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0209] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0210] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0211] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0212] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0213] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0214] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0215] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0216] (Appendix 1) A goal determination unit that determines a goal of the user based on a dialogue with the user; a plan determination unit that determines an action plan for achieving the goal determined by the goal determination unit based on a dialogue with the user; an acquisition unit that acquires information indicating the user's progress in the action plan determined by the plan determination unit; a feedback unit that provides feedback to the user based on the information acquired by the acquisition unit; Equipped A system characterized by: (Appendix 2) A provision section is provided that provides questions and quizzes to discover the user's qualities and strengths. 2. The system of claim 1, (Appendix 3) A recommendation unit is provided for recommending resources related to self-development to the user. 2. The system of claim 1, (Appendix 4) A guide unit that provides a guided meditation session to the user. 2. The system of claim 1, (Appendix 5) The guide portion is Measuring the user's stress level in real time and providing a meditation session based on the measured results. 5. The system of claim 4. (Appendix 6) Based on the information acquired by the acquisition unit, A reward unit that rewards the user according to the progress of the user. 2. The system of claim 1, (Appendix 7) The goal determination unit, Determine a goal appropriate for the user based on a dialogue with the user and a behavioral history of the user. 2. The system of claim 1, (Appendix 8) The feedback unit is Providing motivational messages when the user's progress stagnates 2. The system of claim 1, (Appendix 9) The feedback unit is Providing best practice feedback as the user progresses 2. The system of claim 1, (Appendix 10) The acquisition unit is Analyzing social media data of said users to obtain progress information. 2. The system of claim 1, (Appendix 11) The plan determination unit, Optimizing the content of the action plan based on the user's feedback. 2. The system of claim 1, (Appendix 12) The goal determination unit, Estimating user emotions and prioritizing goals based on the estimated user emotions 2. The system of claim 1, (Appendix 13) The goal determination unit, Analyze the user's past goal achievement history and select the optimal goal setting method 2. The system of claim 1, (Appendix 14) The goal determination unit, Customize goals based on the user's current life situation and interests as they are defined 2. The system of claim 1, (Appendix 15) The goal determination unit, Estimating a user's emotion and adjusting the difficulty of the goal based on the estimated user's emotion 2. The system of claim 1, (Appendix 16) The goal determination unit, When determining goals, take into account the user's geographic location to set more relevant goals. 2. The system of claim 1, (Appendix 17) The goal determination unit, When determining goals, analyze users' social media activity and set relevant goals. 2. The system of claim 1, (Appendix 18) The plan determination unit, Infer user emotions and prioritize action plans based on the inferred user emotions 2. The system of claim 1, (Appendix 19) The plan determination unit, Analyze the user's past action plan history and select the optimal plan setting method 2. The system of claim 1, (Appendix 20) The plan determination unit, When deciding on an action plan, customize it based on the user's current life situation and interests 2. The system of claim 1, (Appendix 21) The plan determination unit, When determining action plans, consider the user's geographic location to set more relevant plans 2. The system of claim 1, (Appendix 22) The plan determination unit, When deciding on an action plan, analyze users' social media activity and set up a relevant plan 2. The system of claim 1, (Appendix 23) The acquisition unit is To estimate a user's emotion and adjust a timing of acquiring progress information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 24) The acquisition unit is Analyze the user's past progress information and select the optimal acquisition method 2. The system of claim 1, (Appendix 25) The acquisition unit is Filter progress information based on the user's current life situation and interests 2. The system of claim 1, (Appendix 26) The acquisition unit is A user's emotion is estimated, and a priority order of progress information to be acquired is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 27) The acquisition unit is When obtaining progress information, the system takes into account the user's geographic location information and prioritizes obtaining information that is more relevant to the user. 2. The system of claim 1, (Appendix 28) The acquisition unit is When obtaining progress information, analyze the user's social media activity to obtain related information. 2. The system of claim 1, (Appendix 29) The feedback unit is Infer user emotions and adjust feedback presentation based on the estimated user emotions 2. The system of claim 1, (Appendix 30) The feedback unit is When providing feedback, adjust the level of detail based on the importance of the progress information 2. The system of claim 1, (Appendix 31) The feedback unit is When providing feedback, apply different feedback algorithms depending on the category of progress information. 2. The system of claim 1, (Appendix 32) The feedback unit is Estimate the user's emotions and adjust the length of the feedback based on the estimated user emotions. 2. The system of claim 1, (Appendix 33) The feedback unit is When feedback is given, it is prioritized based on when progress information is provided 2. The system of claim 1, (Appendix 34) The feedback unit is When giving feedback, adjust the order of feedback based on the relevance of progress information 2. The system of claim 1, (Appendix 35) The providing unit is Infer user sentiment and adjust questions and quizzes based on the inferred sentiment 3. The system of claim 2, (Appendix 36) The providing unit is When providing questions or quizzes, select the most appropriate content by referring to the user's past answer history 3. The system of claim 2, (Appendix 37) The providing unit is Infer user sentiment and adjust the order of questions and quizzes based on the inferred user sentiment 3. The system of claim 2, (Appendix 38) The providing unit is When providing questions or quizzes, provide the most appropriate content by taking into account the user's geographic location 3. The system of claim 2, (Appendix 39) The recommendation unit, To estimate a user's emotion and adjust a method of recommending resources based on the estimated user's emotion. 4. The system of claim 3. (Appendix 40) The recommendation unit, When recommending resources, the most appropriate resource is selected by referring to the user's past learning history. 4. The system of claim 3. (Appendix 41) The recommendation unit, Inferring user emotions and prioritizing resources based on the inferred user emotions 4. The system of claim 3. (Appendix 42) The recommendation unit, When recommending resources, provide optimal resources by considering the user's geographic location information. 4. The system of claim 3. (Appendix 43) The guide portion is Estimating a user's emotions and adjusting a meditation session or relaxation method based on the estimated user's emotions 5. The system of claim 4. (Appendix 44) The guide portion is When providing meditation sessions or relaxation methods, the system will select the most appropriate content by referring to the user's past stress level history. 5. The system of claim 4. (Appendix 45) The guide portion is Inferring a user's emotions and adjusting the sequence of meditation sessions or relaxation techniques based on the inferred user emotions 5. The system of claim 4. (Appendix 46) The guide portion is When providing meditation sessions or relaxation techniques, the app takes into account the user's geographic location to provide the most appropriate content. 5. The system of claim 4. (Appendix 47) The applying unit is Estimate the user's emotions and adjust the rewards based on the estimated user emotions. 6. The system of claim 5, (Appendix 48) The applying unit is When awarding rewards, the system selects the optimal reward by referring to the user's past progress history. 6. The system of claim 5, (Appendix 49) The applying unit is Estimate the user's emotions and prioritize rewards based on the estimated user emotions. 6. The system of claim 5, (Appendix 50) The applying unit is When providing rewards, the system considers the user's geographic location information to provide optimal rewards. 6. The system of claim 5, [Explanation of symbols]

[0217] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A goal determination unit that determines a goal of the user based on a dialogue with the user; a plan determination unit that determines an action plan for achieving the goal determined by the goal determination unit based on a dialogue with the user; an acquisition unit that acquires information indicating the user's progress in the action plan determined by the plan determination unit; a feedback unit that provides feedback to the user based on the information acquired by the acquisition unit; Equipped A system characterized by:

2. A provision section is provided that provides questions and quizzes to discover the user's qualities and strengths.

2. The system of claim 1 .

3. A recommendation unit is provided for recommending resources related to self-development to the user.

2. The system of claim 1 .

4. A guide unit that provides a guided meditation session to the user.

2. The system of claim 1 .

5. The guide portion is Measuring the user's stress level in real time and providing a meditation session based on the measured results.

5. The system of claim 4.

6. Based on the information acquired by the acquisition unit, A reward unit that rewards the user according to the progress of the user.

2. The system of claim 1 .

7. The goal determination unit, Determine a goal appropriate for the user based on a dialogue with the user and a behavioral history of the user.

2. The system of claim 1 .

8. The feedback unit is Providing motivational messages when the user's progress stagnates 2. The system of claim 1 .

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