System

Abstract

To provide a customized approach for promoting individual growth and organization success.SOLUTION: A system according to an embodiment comprises an acceptance unit, a feedback unit, a coating unit, and a monitoring unit. The acceptance unit accepts information for setting a goal. The feedback unit provides customized feedback on the basis of the information accepted by the acceptance unit. The coating unit provides coating on the basis of the feedback provided by the feedback unit. The monitoring unit monitors the progress of a user on the basis of the coating provided by the coating unit, and adjusts an action plan.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] Existing technologies lack the ability to provide a customized approach to drive individual growth and organizational success, leaving room for improvement.

[0005] The system according to the embodiment aims to provide a customized approach to promoting individual growth and organizational success. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a feedback unit, a coaching unit, and a monitoring unit. The reception unit receives information for setting a goal. The feedback unit provides customized feedback based on the information received by the reception unit. The coaching unit provides coaching based on the feedback provided by the feedback unit. The monitoring unit monitors the user's progress based on the coaching provided by the coaching unit and adjusts the action plan. Effect of the Invention

[0007] An embodiment of the system can provide a customized approach to promoting individual growth and organizational success. [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 growth promotion system according to the embodiment of the present invention is a customized approach to promoting personal growth and organizational success using a generation AI. This growth promotion system is a mechanism for achieving sustainable results through goal setting, feedback, and coaching. Specifically, first, the user sets goals related to personal growth and organizational success. Next, the generation AI provides customized feedback based on the goals. Furthermore, the generation AI provides coaching and creates a specific action plan for the user to achieve the goals. This mechanism realizes sustainable personal growth and organizational success. For example, the user sets goals related to personal growth and organizational success. At this time, the user inputs a specific goal. For example, the user sets goals such as "improve project management skills" or "improve team communication." This information is input to the generation AI. Next, the generation AI provides customized feedback based on the input goal. The generation AI generates appropriate feedback for the user's goal. For example, for the goal "improve project management skills," the generation AI provides specific advice such as "how to clarify task priorities" and "how to utilize progress management tools." Furthermore, the generation AI provides coaching and creates a specific action plan for the user to achieve the goal. The generation AI monitors the user's progress and adjusts the action plan as necessary. For example, for the goal "improve team communication," the generative AI suggests specific actions such as "set up regular meetings" and "improve feedback methods." This mechanism ensures that individual growth and organizational success are sustained. With the support of the generative AI, users can set specific goals, receive customized feedback, and implement action plans to achieve the goals through coaching. This improves individual skills and capabilities and improves the performance of the entire organization. In this way, the growth promotion system can ensure that individual growth and organizational success are sustained.

[0029] The growth promotion system according to the embodiment includes a reception unit, a feedback unit, a coaching unit, and a monitoring unit. The reception unit receives a user's input of goals related to personal growth and organizational success. The goals input by the user include, but are not limited to, skill improvement, sales goals, and project achievement. The reception unit receives, for example, a specific goal input by the user, such as "improve project management skills" or "improve team communication." The feedback unit uses a generation AI to provide customized feedback based on the information received by the reception unit. The feedback includes, but is not limited to, specific advice for the user's goals, such as "how to clarify task priorities" and "how to utilize progress management tools." For example, the generation AI generates appropriate feedback for the user's goals. The generation AI customizes the feedback in consideration of the user's past performance, current situation, type of goal, and the like. The coaching unit uses the generation AI to provide coaching based on the feedback provided by the feedback unit. Coaching includes, but is not limited to, creating a specific action plan for the user to achieve the goal. For example, the generation AI monitors the user's progress and adjusts the action plan as necessary. The monitoring unit uses the generation AI to monitor the progress of the user based on the coaching performed by the coaching unit, and adjusts the action plan. Monitoring includes, for example, evaluating the progress of the user and adjusting the action plan as necessary, but is not limited to such an example. For example, the generation AI monitors the progress of the user in real time, and adjusts the action plan taking into account the progress evaluation criteria, the monitoring frequency, the tools used, etc. In this way, the growth promotion system according to the embodiment can achieve the growth of individuals and the success of organizations in a sustainable manner. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI.For example, the monitoring unit can input the user's progress to the generation AI and have the generation AI evaluate the progress and adjust the action plan.

[0030] The reception unit receives input of a user's goals related to personal growth and organizational success. Examples of goals input by the user include, but are not limited to, skill improvement, sales goals, and project achievement. Specifically, the reception unit receives input of specific goals such as "improve project management skills" and "improve team communication" by the user. The reception unit provides an interface for the user to input goals, allowing the user to easily set goals. For example, the user can input goals through a web form or a mobile app. Furthermore, the reception unit can provide guidelines and hints for evaluating the specificity and achievability of the goals when the user inputs the goals. This allows the user to set more specific and achievable goals. The reception unit stores the goals input by the user in a database and makes them available for subsequent processing. For example, the goals input by the user are stored in a database so that they can be accessed by the feedback unit and coaching unit. This allows the reception unit to support the process of the user setting goals and improve the effectiveness of the entire system.

[0031] The feedback unit uses the generation AI to provide customized feedback based on the information received by the reception unit. The feedback includes, for example, specific advice such as "how to clarify task priorities" and "how to utilize progress management tools" for the user's goals, but is not limited to such examples. The generation AI customizes the feedback taking into consideration the user's past performance, current situation, type of goal, etc. Specifically, the generation AI analyzes the user's past data, evaluates the current situation, and generates feedback that is optimal for the goal. For example, the generation AI analyzes goals that the user has achieved in the past and goals that he or she has failed to achieve, and identifies the factors of success and the causes of failure. This allows the generation AI to suggest specific actions that the user needs to take to achieve the goal. Furthermore, the generation AI can evaluate the user's current situation in real time and continuously update the feedback. For example, if the user adds a new task or the progress status changes, the generation AI immediately updates the feedback to respond to the latest situation. This allows the feedback unit to always provide the user with the latest and most optimal feedback and support goal achievement.

[0032] The coaching unit uses the generation AI to provide coaching based on the feedback provided by the feedback unit. Coaching includes, for example, but is not limited to, creating a specific action plan for the user to achieve the goal. The generation AI monitors the user's progress and adjusts the action plan as necessary. Specifically, the generation AI proposes an optimal action plan for the user's goal and supports the user to act according to the plan. For example, the generation AI creates weekly tasks and daily checklists based on the goal set by the user, allowing the user to move toward the goal in a planned manner. Furthermore, the generation AI monitors the user's progress in real time and adjusts the action plan as necessary. For example, if the user is behind schedule, the generation AI reevaluates the priority of tasks and proposes a new plan to efficiently achieve the goal. In addition, the generation AI can collect user feedback and continuously improve the content of coaching. This allows the coaching unit to provide effective coaching to the user and support goal achievement.

[0033] The monitoring unit uses the generation AI to monitor the user's progress based on the coaching performed by the coaching unit and adjust the action plan. Monitoring includes, for example, but is not limited to, evaluating the user's progress and adjusting the action plan as necessary. The generation AI monitors the user's progress in real time and adjusts the action plan taking into consideration the evaluation criteria for progress, the frequency of monitoring, the tools used, and the like. Specifically, the generation AI periodically evaluates the progress toward the goal set by the user, and when progress is delayed or the approach toward the goal is ineffective, reevaluates the action plan and makes necessary adjustments. For example, the generation AI analyzes the completion status and progress report of the task set by the user, and when progress is delayed, reevaluates the priority of the task and proposes a new plan to efficiently achieve the goal. In addition, the generation AI can collect user feedback and continuously improve the content of the monitoring. This allows the monitoring unit to accurately grasp the user's progress and adjust the action plan at an appropriate time. Furthermore, the monitoring unit visualizes the user's progress so that the user can grasp his or her progress at a glance. For example, the progress is displayed in a graph or chart so that the user can intuitively understand his or her progress. This enables the monitoring unit to effectively monitor the user's progress and support goal achievement.

[0034] The reception unit allows the user to input goals related to personal growth and organizational success. The reception unit, for example, allows the user to input goals related to personal growth and organizational success. The goals input by the user include, for example, skill improvement, sales goals, project achievement, etc., but are not limited to such examples. For example, the reception unit accepts the user to input specific goals such as "improve project management skills" or "improve team communication." This allows the user to set specific goals. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the goals input by the user to a generation AI and analyze the contents of the goals.

[0035] The feedback unit can generate appropriate feedback for the user's goal. The feedback unit uses the generation AI to generate appropriate feedback for the user's goal. The feedback includes, for example, specific advice for the user's goal, such as "how to clarify task priorities" and "how to utilize progress management tools," but is not limited to such examples. For example, the generation AI generates appropriate feedback for the user's goal. The generation AI customizes the feedback taking into account the user's past performance, current situation, type of goal, etc. This makes it possible to provide specific advice for the user's 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 goal into the generation AI and have the generation AI generate the content of the feedback.

[0036] The coaching unit can create a specific action plan for the user to achieve the goal. The coaching unit uses the generation AI to create a specific action plan for the user to achieve the goal. The action plan includes, for example, a step-by-step guide, necessary resources, a timeline, and the like, but is not limited to such examples. For example, the generation AI monitors the user's progress and adjusts the action plan as necessary. This allows the user to have a specific action plan for achieving the goal. Some or all of the above-mentioned processing in the coaching unit may be performed, for example, using AI or may be performed without using AI. For example, the coaching unit can input the user's progress to the generation AI and cause the generation AI to create and adjust the action plan.

[0037] The monitoring unit can monitor the progress of the user and adjust the action plan as necessary. The monitoring unit uses the generation AI to monitor the progress of the user and adjust the action plan as necessary. Monitoring includes, for example, evaluating the progress of the user and adjusting the action plan as necessary, but is not limited to such an example. For example, the generation AI monitors the progress of the user in real time and adjusts the action plan taking into consideration the evaluation criteria for the progress, the frequency of monitoring, the tools used, etc. This allows for flexible response according to the progress of the user. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the progress of the user to the generation AI and cause the generation AI to evaluate the progress and adjust the action plan.

[0038] The feedback unit can provide specific advice such as "how to clarify task priorities" or "how to utilize progress management tools" for the user's goals. The feedback unit uses the generation AI to provide specific advice such as "how to clarify task priorities" or "how to utilize progress management tools" for the user's goals. For example, the generation AI generates appropriate feedback for the user's goals. The generation AI customizes the feedback taking into account the user's past performance, current situation, type of goal, etc. As a result, the user receives specific advice to help achieve the 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 goals into the generation AI and have the generation AI generate the content of the feedback.

[0039] The reception unit can analyze the user's past goal setting history and select the optimal goal setting method. The reception unit uses the generation AI to analyze the user's past goal setting history and select the optimal goal setting method. For example, the reception unit preferentially suggests a goal setting method that the user has been successful in the past. It can also suggest that the user avoid a goal setting method that the user has failed in the past. Furthermore, it can also suggest realistic goal setting based on the user's past goal achievement rate. This makes it possible to provide an optimal goal setting method based on the user's past history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past goal setting history to the generation AI and cause the generation AI to select the optimal goal setting method.

[0040] The reception unit can perform filtering based on the user's current project and field of interest when setting a goal. The reception unit uses the generation AI to perform filtering based on the user's current project and field of interest when setting a goal. For example, goals related to the project the user is currently working on are preferentially proposed. Also, it is possible to propose interesting goals based on the user's field of interest. Furthermore, it is possible to filter and propose goals related to the user's current work content. This makes it possible to set goals according to the user's current situation. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input information on the user's current project and field of interest to the generation AI and cause the generation AI to execute filtering of goals.

[0041] The reception unit can prioritize highly relevant goals based on the geographical location information of the user when setting a goal. The reception unit uses the generation AI to prioritize highly relevant goals based on the geographical location information of the user when setting a goal. For example, when the user is in a specific area, a goal related to the area is proposed. In addition, when the user is on a business trip, a goal that can be achieved at the business trip destination can be proposed. Furthermore, when the user is at home, a goal that can be achieved at home can be proposed. This enables goal setting according to the geographical location of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute setting of a highly relevant goal.

[0042] The reception unit can analyze the user's social media activities and set related goals when setting goals. The reception unit uses the generation AI to analyze the user's social media activities and set related goals when setting goals. For example, the reception unit can suggest goals related to themes that the user frequently mentions on social media. In addition, a goal that increases influence can be set based on the number of the user's social media followers. Furthermore, the content of the user's social media posts can be analyzed and goals that attract interest can be suggested. This makes it possible to set goals based on the user's social media activities. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activities into the generation AI and cause the generation AI to set goals.

[0043] The feedback unit can adjust the level of detail of the feedback based on the importance of the goal when providing the feedback. The feedback unit adjusts the level of detail of the feedback based on the importance of the goal when providing the feedback using the generation AI. For example, the feedback unit provides detailed feedback for an important goal. Also, for a less important goal, brief feedback can be provided. Furthermore, the level of detail of the feedback can be adjusted according to the degree of achievement of the goal. This makes it possible to provide feedback according to the importance of the goal. 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 data on the importance of the goal to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0044] The feedback unit can apply different feedback algorithms depending on the category of the goal when providing feedback. The feedback unit uses the generation AI to apply different feedback algorithms depending on the category of the goal when providing feedback. For example, the feedback unit applies a task management feedback algorithm to a goal related to project management. Also, a human skills feedback algorithm can be applied to a goal related to communication. Furthermore, a technical feedback algorithm can be applied to a goal related to technical skills. This makes it possible to provide feedback according to the category of the goal. Some 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 data of the goal category to the generation AI and cause the generation AI to apply the feedback algorithm.

[0045] The feedback unit can determine the priority of feedback based on the time when the goal was set when providing feedback. The feedback unit uses the generation AI to determine the priority of feedback based on the time when the goal was set when providing feedback. For example, the feedback unit provides feedback preferentially for a recently set goal. Also, detailed feedback can be provided for a goal that has not been achieved for a long time. Furthermore, the priority of feedback can be adjusted according to the time when the goal was set. This makes it possible to provide the priority of feedback according to the time when the goal was set. 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 data on the time when the goal was set to the generation AI and cause the generation AI to execute the determination of the priority of feedback.

[0046] The feedback unit can adjust the order of feedback based on the relevance of the goal when providing feedback. The feedback unit adjusts the order of feedback based on the relevance of the goal using the generation AI when providing feedback. For example, the feedback unit provides feedback first for an important goal. Also, feedback can be provided later for a goal with low relevance. Furthermore, the order of feedback can be adjusted according to the relevance of the goal. This makes it possible to provide the order of feedback according to the relevance of the goal. 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 data on the relevance of the goal to the generation AI and cause the generation AI to adjust the order of feedback.

[0047] The coaching unit can select the optimal coaching method by analyzing the user's past behavior history during coaching. The coaching unit uses the generation AI to analyze the user's past behavior history during coaching to select the optimal coaching method. For example, the coaching unit preferentially suggests coaching methods that the user has been successful in the past. It can also suggest coaching methods that the user has failed in the past to be avoided. Furthermore, it can also suggest realistic coaching methods based on the user's past behavior history. This makes it possible to provide an optimal coaching method based on the user's past behavior history. Some or all of the above-mentioned processing in the coaching unit may be performed, for example, using AI, or may be performed without using AI. For example, the coaching unit can input data of the user's past behavior history into the generation AI and cause the generation AI to select the optimal coaching method.

[0048] The coaching unit can customize the coaching means based on the user's current living situation during coaching. The coaching unit uses the generation AI to customize the coaching means based on the user's current living situation during coaching. For example, if the user is busy, the coaching unit can suggest a short and effective coaching method. Also, if the user is relaxed, a detailed coaching method can be suggested. Furthermore, the coaching means can be customized according to the user's living situation. This makes it possible to provide coaching means according to the user's current living situation. Some or all of the above-mentioned processing in the coaching unit may be performed, for example, using AI, or may be performed without using AI. For example, the coaching unit can input data on the user's living situation into the generation AI and cause the generation AI to customize the coaching means.

[0049] The coaching unit can select the optimal coaching method in consideration of the geographical location information of the user during coaching. The coaching unit uses the generation AI to select the optimal coaching method in consideration of the geographical location information of the user during coaching. For example, when the user is in a specific area, coaching related to the area is provided. In addition, when the user is on a business trip, coaching that can be performed at the business trip destination can be provided. Furthermore, when the user is at home, coaching that can be performed at home can be provided. This makes it possible to provide the optimal coaching method according to the geographical location of the user. A part or all of the above-mentioned processing in the coaching unit may be performed, for example, using AI, or may be performed without using AI. For example, the coaching unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the optimal coaching method.

[0050] The coaching unit can analyze the user's social media activities and propose coaching means during coaching. The coaching unit uses the generation AI to analyze the user's social media activities and propose coaching means during coaching. For example, coaching related to a theme that the user frequently mentions on social media can be provided. Coaching that increases influence can also be provided based on the number of the user's social media followers. Furthermore, interesting coaching can be provided by analyzing the content of the user's social media posts. This makes it possible to provide coaching means based on the user's social media activities. Some or all of the above-mentioned processing in the coaching unit may be performed, for example, using AI or may be performed without using AI. For example, the coaching unit can input data on the user's social media activities into the generation AI and cause the generation AI to execute the proposal of coaching means.

[0051] The monitoring unit can select the optimal monitoring method by referring to the user's past progress data during monitoring. The monitoring unit uses the generation AI to select the optimal monitoring method by referring to the user's past progress data during monitoring. For example, the monitoring unit preferentially suggests monitoring methods that have been successful for the user in the past. It can also suggest to avoid monitoring methods that have failed for the user in the past. Furthermore, it can also suggest a realistic monitoring method based on the user's past progress data. This makes it possible to provide an optimal monitoring method based on the user's past progress data. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's past progress data to the generation AI and cause the generation AI to select the optimal monitoring method.

[0052] The monitoring unit can customize the monitoring means based on the user's current project status at the time of monitoring. The monitoring unit uses the generation AI to customize the monitoring means based on the user's current project status at the time of monitoring. For example, a monitoring method related to the project that the user is currently working on is provided. The frequency of monitoring can also be adjusted according to the progress of the user's project. Furthermore, the level of detail of monitoring can also be adjusted according to the importance of the user's project. This makes it possible to provide a monitoring means according to the user's current project status. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input data on the user's project status to the generation AI and cause the generation AI to customize the monitoring means.

[0053] The monitoring unit can select the optimal monitoring method in consideration of the geographical location information of the user at the time of monitoring. The monitoring unit uses the generation AI to select the optimal monitoring method in consideration of the geographical location information of the user at the time of monitoring. For example, when the user is in a specific area, a monitoring method related to the area is provided. In addition, when the user is on a business trip, a monitoring method that can be performed at the business trip destination can be provided. Furthermore, when the user is at home, a monitoring method that can be performed at home can be provided. This makes it possible to provide the optimal monitoring method according to the geographical location of the user. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the optimal monitoring method.

[0054] The monitoring unit can analyze the user's social media activities during monitoring and suggest monitoring means. The monitoring unit uses the generation AI to analyze the user's social media activities during monitoring and suggest monitoring means. For example, a monitoring method related to a theme that the user frequently mentions on social media can be provided. Also, a monitoring method that increases influence based on the number of the user's social media followers can be provided. Furthermore, a monitoring method that analyzes the content of the user's social media posts and attracts interest can be provided. This makes it possible to provide a monitoring means based on the user's social media activities. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input data on the user's social media activities into the generation AI and cause the generation AI to execute a suggestion of a monitoring means.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can analyze the user's past goal achievement history and adjust the difficulty of the goal based on the degree of achievement. For example, if the user has shown a high achievement rate in the past, the reception unit can suggest a more challenging goal. Also, if the user has shown a low achievement rate in the past, the reception unit can suggest an easy-to-achieve goal. Furthermore, realistic goal setting can be suggested based on the user's past goal achievement history. This enables optimal goal setting based on the user's past history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past goal achievement history into the generation AI and cause the generation AI to adjust the difficulty of the goal.

[0057] The feedback unit can provide achievable steps for the user's goal in a step-by-step manner. For example, if the user sets a goal of "improving project management skills," the feedback unit provides specific steps such as "clarifying task priorities," "utilizing progress management tools," and "strengthening communication with team members." In addition, the feedback unit can provide criteria for evaluating the degree of achievement for each step. In addition, the feedback unit can suggest resources and tools for the user to achieve each step. This allows the user to have a specific action plan, making it easier to check progress toward achieving the 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 goal into the generation AI and have the generation AI generate steps and set evaluation criteria.

[0058] The coaching unit can customize the coaching method based on the user's learning style. For example, if the user is a visual learner, the coaching unit can provide coaching that makes extensive use of visual aids and graphs. If the user is an auditory learner, the coaching unit can provide coaching in the form of audio guides or podcasts. If the user is a practical learner, the coaching unit can also suggest hands-on workshops and practical tasks. This makes it possible to provide an optimal coaching method according to the user's learning style. Some or all of the above-mentioned processing in the coaching unit may be performed, for example, using AI or may be performed without using AI. For example, the coaching unit can input data on the user's learning style into the generation AI and cause the generation AI to customize the coaching method.

[0059] The monitoring unit can visualize the user's progress in real time and provide it in the form of a dashboard. For example, the progress toward the goals set by the user can be displayed in graphs and charts. The degree of achievement of each goal and the remaining tasks can also be displayed in a list. Furthermore, a customizable widget can be provided so that the user can grasp the progress at a glance. This allows the user to intuitively understand his / her own progress and take necessary actions quickly. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input the user's progress data to the generation AI and cause the generation AI to generate a dashboard.

[0060] The reception unit can evaluate the user's current skill level and suggest appropriate goals. For example, if the user is a beginner, the reception unit can suggest a basic goal. If the user is an intermediate player, the reception unit can suggest a more advanced goal. If the user is an advanced player, the reception unit can suggest a specialized goal. This enables optimal goal setting according to the user's skill level. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input data on the user's skill level into the generation AI and cause the generation AI to execute the goal suggestion.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception section allows the user to input goals related to personal growth and organizational success. Goals input by the user include skill improvement, sales targets, project completion, etc. For example, the reception section accepts the user to input specific goals such as "improve project management skills" or "improve team communication." Step 2: The feedback unit uses the generation AI to provide customized feedback based on the information received by the reception unit. The feedback includes specific advice for the user's goals, such as "how to clarify task priorities" and "how to utilize progress management tools." The generation AI customizes the feedback by taking into account the user's past performance, current situation, type of goal, etc. Step 3: The coaching unit uses the generating AI to provide coaching based on the feedback provided by the feedback unit. Coaching includes creating a specific action plan for the user to achieve the goal. The generating AI monitors the user's progress and adjusts the action plan as necessary. Step 4: The monitoring unit uses the generating AI to monitor the user's progress based on the coaching provided by the coaching unit and adjust the action plan. Monitoring includes evaluating the user's progress and adjusting the action plan as necessary. The generating AI monitors the user's progress in real time and adjusts the action plan taking into account the progress evaluation criteria, monitoring frequency, tools used, etc.

[0063] (Example 2) The growth promotion system according to the embodiment of the present invention is a customized approach to promoting personal growth and organizational success using a generation AI. This growth promotion system is a mechanism for achieving sustainable results through goal setting, feedback, and coaching. Specifically, first, the user sets goals related to personal growth and organizational success. Next, the generation AI provides customized feedback based on the goals. Furthermore, the generation AI provides coaching and creates a specific action plan for the user to achieve the goals. This mechanism realizes sustainable personal growth and organizational success. For example, the user sets goals related to personal growth and organizational success. At this time, the user inputs a specific goal. For example, the user sets goals such as "improve project management skills" or "improve team communication." This information is input to the generation AI. Next, the generation AI provides customized feedback based on the input goal. The generation AI generates appropriate feedback for the user's goal. For example, for the goal "improve project management skills," the generation AI provides specific advice such as "how to clarify task priorities" and "how to utilize progress management tools." Furthermore, the generation AI provides coaching and creates a specific action plan for the user to achieve the goal. The generation AI monitors the user's progress and adjusts the action plan as necessary. For example, for the goal "improve team communication," the generative AI suggests specific actions such as "set up regular meetings" and "improve feedback methods." This mechanism ensures that individual growth and organizational success are sustained. With the support of the generative AI, users can set specific goals, receive customized feedback, and implement action plans to achieve the goals through coaching. This improves individual skills and capabilities and improves the performance of the entire organization. In this way, the growth promotion system can ensure that individual growth and organizational success are sustained.

[0064] The growth promotion system according to the embodiment includes a reception unit, a feedback unit, a coaching unit, and a monitoring unit. The reception unit receives a user's input of goals related to personal growth and organizational success. The goals input by the user include, but are not limited to, skill improvement, sales goals, and project achievement. The reception unit receives, for example, a specific goal input by the user, such as "improve project management skills" or "improve team communication." The feedback unit uses a generation AI to provide customized feedback based on the information received by the reception unit. The feedback includes, but is not limited to, specific advice for the user's goals, such as "how to clarify task priorities" and "how to utilize progress management tools." For example, the generation AI generates appropriate feedback for the user's goals. The generation AI customizes the feedback in consideration of the user's past performance, current situation, type of goal, and the like. The coaching unit uses the generation AI to provide coaching based on the feedback provided by the feedback unit. Coaching includes, but is not limited to, creating a specific action plan for the user to achieve the goal. For example, the generation AI monitors the user's progress and adjusts the action plan as necessary. The monitoring unit uses the generation AI to monitor the progress of the user based on the coaching performed by the coaching unit, and adjusts the action plan. Monitoring includes, for example, evaluating the progress of the user and adjusting the action plan as necessary, but is not limited to such an example. For example, the generation AI monitors the progress of the user in real time, and adjusts the action plan taking into account the progress evaluation criteria, the monitoring frequency, the tools used, etc. In this way, the growth promotion system according to the embodiment can achieve the growth of individuals and the success of organizations in a sustainable manner. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI.For example, the monitoring unit can input the user's progress to the generation AI and have the generation AI evaluate the progress and adjust the action plan.

[0065] The reception unit receives input of a user's goals related to personal growth and organizational success. Examples of goals input by the user include, but are not limited to, skill improvement, sales goals, and project achievement. Specifically, the reception unit receives input of specific goals such as "improve project management skills" and "improve team communication" by the user. The reception unit provides an interface for the user to input goals, allowing the user to easily set goals. For example, the user can input goals through a web form or a mobile app. Furthermore, the reception unit can provide guidelines and hints for evaluating the specificity and achievability of the goals when the user inputs the goals. This allows the user to set more specific and achievable goals. The reception unit stores the goals input by the user in a database and makes them available for subsequent processing. For example, the goals input by the user are stored in a database so that they can be accessed by the feedback unit and coaching unit. This allows the reception unit to support the process of the user setting goals and improve the effectiveness of the entire system.

[0066] The feedback unit uses the generation AI to provide customized feedback based on the information received by the reception unit. The feedback includes, for example, specific advice such as "how to clarify task priorities" and "how to utilize progress management tools" for the user's goals, but is not limited to such examples. The generation AI customizes the feedback taking into consideration the user's past performance, current situation, type of goal, etc. Specifically, the generation AI analyzes the user's past data, evaluates the current situation, and generates feedback that is optimal for the goal. For example, the generation AI analyzes goals that the user has achieved in the past and goals that he or she has failed to achieve, and identifies the factors of success and the causes of failure. This allows the generation AI to suggest specific actions that the user needs to take to achieve the goal. Furthermore, the generation AI can evaluate the user's current situation in real time and continuously update the feedback. For example, if the user adds a new task or the progress status changes, the generation AI immediately updates the feedback to respond to the latest situation. This allows the feedback unit to always provide the user with the latest and most optimal feedback and support goal achievement.

[0067] The coaching unit uses the generation AI to provide coaching based on the feedback provided by the feedback unit. Coaching includes, for example, but is not limited to, creating a specific action plan for the user to achieve the goal. The generation AI monitors the user's progress and adjusts the action plan as necessary. Specifically, the generation AI proposes an optimal action plan for the user's goal and supports the user to act according to the plan. For example, the generation AI creates weekly tasks and daily checklists based on the goal set by the user, allowing the user to move toward the goal in a planned manner. Furthermore, the generation AI monitors the user's progress in real time and adjusts the action plan as necessary. For example, if the user is behind schedule, the generation AI reevaluates the priority of tasks and proposes a new plan to efficiently achieve the goal. In addition, the generation AI can collect user feedback and continuously improve the content of coaching. This allows the coaching unit to provide effective coaching to the user and support goal achievement.

[0068] The monitoring unit uses the generation AI to monitor the user's progress based on the coaching performed by the coaching unit and adjust the action plan. Monitoring includes, for example, but is not limited to, evaluating the user's progress and adjusting the action plan as necessary. The generation AI monitors the user's progress in real time and adjusts the action plan taking into consideration the evaluation criteria for progress, the frequency of monitoring, the tools used, and the like. Specifically, the generation AI periodically evaluates the progress toward the goal set by the user, and when progress is delayed or the approach toward the goal is ineffective, reevaluates the action plan and makes necessary adjustments. For example, the generation AI analyzes the completion status and progress report of the task set by the user, and when progress is delayed, reevaluates the priority of the task and proposes a new plan to efficiently achieve the goal. In addition, the generation AI can collect user feedback and continuously improve the content of the monitoring. This allows the monitoring unit to accurately grasp the user's progress and adjust the action plan at an appropriate time. Furthermore, the monitoring unit visualizes the user's progress so that the user can grasp his or her progress at a glance. For example, the progress is displayed in a graph or chart so that the user can intuitively understand his or her progress. This enables the monitoring unit to effectively monitor the user's progress and support goal achievement.

[0069] The reception unit allows the user to input goals related to personal growth and organizational success. The reception unit, for example, allows the user to input goals related to personal growth and organizational success. The goals input by the user include, for example, skill improvement, sales goals, project achievement, etc., but are not limited to such examples. For example, the reception unit accepts the user to input specific goals such as "improve project management skills" or "improve team communication." This allows the user to set specific goals. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the goals input by the user to a generation AI and analyze the contents of the goals.

[0070] The feedback unit can generate appropriate feedback for the user's goal. The feedback unit uses the generation AI to generate appropriate feedback for the user's goal. The feedback includes, for example, specific advice for the user's goal, such as "how to clarify task priorities" and "how to utilize progress management tools," but is not limited to such examples. For example, the generation AI generates appropriate feedback for the user's goal. The generation AI customizes the feedback taking into account the user's past performance, current situation, type of goal, etc. This makes it possible to provide specific advice for the user's 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 goal into the generation AI and have the generation AI generate the content of the feedback.

[0071] The coaching unit can create a specific action plan for the user to achieve the goal. The coaching unit uses the generation AI to create a specific action plan for the user to achieve the goal. The action plan includes, for example, a step-by-step guide, necessary resources, a timeline, and the like, but is not limited to such examples. For example, the generation AI monitors the user's progress and adjusts the action plan as necessary. This allows the user to have a specific action plan for achieving the goal. Some or all of the above-mentioned processing in the coaching unit may be performed, for example, using AI or may be performed without using AI. For example, the coaching unit can input the user's progress to the generation AI and cause the generation AI to create and adjust the action plan.

[0072] The monitoring unit can monitor the progress of the user and adjust the action plan as necessary. The monitoring unit uses the generation AI to monitor the progress of the user and adjust the action plan as necessary. Monitoring includes, for example, evaluating the progress of the user and adjusting the action plan as necessary, but is not limited to such an example. For example, the generation AI monitors the progress of the user in real time and adjusts the action plan taking into consideration the evaluation criteria for the progress, the frequency of monitoring, the tools used, etc. This allows for flexible response according to the progress of the user. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the progress of the user to the generation AI and cause the generation AI to evaluate the progress and adjust the action plan.

[0073] The feedback unit can provide specific advice such as "how to clarify task priorities" or "how to utilize progress management tools" for the user's goals. The feedback unit uses the generation AI to provide specific advice such as "how to clarify task priorities" or "how to utilize progress management tools" for the user's goals. For example, the generation AI generates appropriate feedback for the user's goals. The generation AI customizes the feedback taking into account the user's past performance, current situation, type of goal, etc. As a result, the user receives specific advice to help achieve the 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 goals into the generation AI and have the generation AI generate the content of the feedback.

[0074] The reception unit can estimate the user's emotion and adjust the timing of goal setting based on the estimated user's emotion. The reception unit uses the generation AI to estimate the user's emotion and adjust the timing of goal setting based on the estimated user's emotion. For example, if the user is stressed, the reception unit encourages the user to set goals during a time period when the user can relax. In addition, if the user is highly motivated, the reception unit can encourage the user to set goals on the spot. Furthermore, if the user is tired, the reception unit suggests that the user set goals after resting. This allows the user to set goals at the optimal timing according to the user's emotion. The emotion estimation is realized 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion and adjust the timing of goal setting.

[0075] The reception unit can analyze the user's past goal setting history and select the optimal goal setting method. The reception unit uses the generation AI to analyze the user's past goal setting history and select the optimal goal setting method. For example, the reception unit preferentially suggests a goal setting method that the user has been successful in the past. It can also suggest that the user avoid a goal setting method that the user has failed in the past. Furthermore, it can also suggest realistic goal setting based on the user's past goal achievement rate. This makes it possible to provide an optimal goal setting method based on the user's past history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past goal setting history to the generation AI and cause the generation AI to select the optimal goal setting method.

[0076] The reception unit can perform filtering based on the user's current project and field of interest when setting a goal. The reception unit uses the generation AI to perform filtering based on the user's current project and field of interest when setting a goal. For example, goals related to the project the user is currently working on are preferentially proposed. Also, it is possible to propose interesting goals based on the user's field of interest. Furthermore, it is possible to filter and propose goals related to the user's current work content. This makes it possible to set goals according to the user's current situation. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input information on the user's current project and field of interest to the generation AI and cause the generation AI to execute filtering of goals.

[0077] The reception unit can estimate the user's emotion and determine the priority of the goals to be set based on the estimated user's emotion. The reception unit uses the generation AI to estimate the user's emotion and determine the priority of the goals to be set based on the estimated user's emotion. For example, when the user is highly motivated, the reception unit prioritizes goals with high difficulty. Also, when the user is stressed, it is also possible to prioritize easy goals. Furthermore, when the user is relaxed, it is also possible to set balanced goals. This makes it possible to set optimal goal priorities according to the user's emotion. The emotion estimation is realized 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the goals.

[0078] The reception unit can prioritize highly relevant goals based on the geographical location information of the user when setting a goal. The reception unit uses the generation AI to prioritize highly relevant goals based on the geographical location information of the user when setting a goal. For example, when the user is in a specific area, a goal related to the area is proposed. In addition, when the user is on a business trip, a goal that can be achieved at the business trip destination can be proposed. Furthermore, when the user is at home, a goal that can be achieved at home can be proposed. This enables goal setting according to the geographical location of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute setting of a highly relevant goal.

[0079] The reception unit can analyze the user's social media activities and set related goals when setting goals. The reception unit uses the generation AI to analyze the user's social media activities and set related goals when setting goals. For example, the reception unit can suggest goals related to themes that the user frequently mentions on social media. In addition, a goal that increases influence can be set based on the number of the user's social media followers. Furthermore, the content of the user's social media posts can be analyzed and goals that attract interest can be suggested. This makes it possible to set goals based on the user's social media activities. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activities into the generation AI and cause the generation AI to set goals.

[0080] 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 uses the generation AI to estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. For example, if the user is nervous, the feedback unit provides feedback in gentle words. Also, if the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, brief feedback can be provided. This makes it possible to provide feedback according to the user's emotion. The emotion estimation is realized 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 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 cause the generation AI to adjust the feedback expression method.

[0081] The feedback unit can adjust the level of detail of the feedback based on the importance of the goal when providing the feedback. The feedback unit adjusts the level of detail of the feedback based on the importance of the goal when providing the feedback using the generation AI. For example, the feedback unit provides detailed feedback for an important goal. Also, for a less important goal, brief feedback can be provided. Furthermore, the level of detail of the feedback can be adjusted according to the degree of achievement of the goal. This makes it possible to provide feedback according to the importance of the goal. 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 can input data on the importance of the goal to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0082] The feedback unit can apply different feedback algorithms depending on the category of the goal when providing feedback. The feedback unit uses the generation AI to apply different feedback algorithms depending on the category of the goal when providing feedback. For example, the feedback unit applies a task management feedback algorithm to a goal related to project management. Also, a human skills feedback algorithm can be applied to a goal related to communication. Furthermore, a technical feedback algorithm can be applied to a goal related to technical skills. This makes it possible to provide feedback according to the category of the goal. Some 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 data of the goal category to the generation AI and cause the generation AI to apply the feedback algorithm.

[0083] 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 uses the generation AI to 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 in a hurry, the feedback unit provides short and to the point feedback. Also, if the user is relaxed, detailed feedback can be provided. Furthermore, if the user is excited, visually stimulating feedback can be provided. This makes it possible to provide the length of the feedback according to the user's emotion. The emotion estimation is realized 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 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 cause the generation AI to adjust the length of the feedback.

[0084] The feedback unit can determine the priority of feedback based on the time when the goal was set when providing feedback. The feedback unit uses the generation AI to determine the priority of feedback based on the time when the goal was set when providing feedback. For example, the feedback unit provides feedback preferentially for a recently set goal. Also, detailed feedback can be provided for a goal that has not been achieved for a long time. Furthermore, the priority of feedback can be adjusted according to the time when the goal was set. This makes it possible to provide the priority of feedback according to the time when the goal was set. 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 data on the time when the goal was set to the generation AI and cause the generation AI to execute the determination of the priority of feedback.

[0085] The feedback unit can adjust the order of feedback based on the relevance of the goal when providing feedback. The feedback unit adjusts the order of feedback based on the relevance of the goal using the generation AI when providing feedback. For example, the feedback unit provides feedback first for an important goal. Also, feedback can be provided later for a goal with low relevance. Furthermore, the order of feedback can be adjusted according to the relevance of the goal. This makes it possible to provide the order of feedback according to the relevance of the goal. 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 data on the relevance of the goal to the generation AI and cause the generation AI to adjust the order of feedback.

[0086] The coaching unit can estimate the user's emotion and adjust the coaching method based on the estimated user's emotion. The coaching unit uses the generation AI to estimate the user's emotion and adjust the coaching method based on the estimated user's emotion. For example, if the user is nervous, the coaching unit provides a coaching method that allows the user to relax. If the user is relaxed, detailed coaching can be provided. Furthermore, if the user is in a hurry, brief coaching can be provided. This makes it possible to provide a coaching method according to the user's emotion. The emotion estimation is realized 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 coaching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the coaching unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the coaching method.

[0087] The coaching unit can select the optimal coaching method by analyzing the user's past behavior history during coaching. The coaching unit uses the generation AI to analyze the user's past behavior history during coaching to select the optimal coaching method. For example, the coaching unit preferentially suggests coaching methods that the user has been successful in the past. It can also suggest coaching methods that the user has failed in the past to be avoided. Furthermore, it can also suggest realistic coaching methods based on the user's past behavior history. This makes it possible to provide an optimal coaching method based on the user's past behavior history. Some or all of the above-mentioned processing in the coaching unit may be performed, for example, using AI, or may be performed without using AI. For example, the coaching unit can input data of the user's past behavior history into the generation AI and cause the generation AI to select the optimal coaching method.

[0088] The coaching unit can customize the coaching means based on the user's current living situation during coaching. The coaching unit uses the generation AI to customize the coaching means based on the user's current living situation during coaching. For example, if the user is busy, the coaching unit can suggest a short and effective coaching method. Also, if the user is relaxed, a detailed coaching method can be suggested. Furthermore, the coaching means can be customized according to the user's living situation. This makes it possible to provide coaching means according to the user's current living situation. Some or all of the above-mentioned processing in the coaching unit may be performed, for example, using AI, or may be performed without using AI. For example, the coaching unit can input data on the user's living situation into the generation AI and cause the generation AI to customize the coaching means.

[0089] The coaching unit can estimate the user's emotion and determine the priority of coaching based on the estimated user's emotion. The coaching unit uses the generation AI to estimate the user's emotion and determine the priority of coaching based on the estimated user's emotion. For example, when the user is highly motivated, the coaching unit can provide coaching with a high level of difficulty preferentially. Also, when the user is stressed, it is also possible to provide easy coaching with a high level of difficulty preferentially. Furthermore, when the user is relaxed, it is also possible to provide balanced coaching. This makes it possible to provide a priority of coaching according to the user's emotion. 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, 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 coaching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the coaching unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of coaching.

[0090] The coaching unit can select the optimal coaching method in consideration of the geographical location information of the user during coaching. The coaching unit uses the generation AI to select the optimal coaching method in consideration of the geographical location information of the user during coaching. For example, when the user is in a specific area, coaching related to the area is provided. In addition, when the user is on a business trip, coaching that can be performed at the business trip destination can be provided. Furthermore, when the user is at home, coaching that can be performed at home can be provided. This makes it possible to provide the optimal coaching method according to the geographical location of the user. A part or all of the above-mentioned processing in the coaching unit may be performed, for example, using AI, or may be performed without using AI. For example, the coaching unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the optimal coaching method.

[0091] The coaching unit can analyze the user's social media activities and propose coaching means during coaching. The coaching unit uses the generation AI to analyze the user's social media activities and propose coaching means during coaching. For example, coaching related to a theme that the user frequently mentions on social media can be provided. Coaching that increases influence can also be provided based on the number of the user's social media followers. Furthermore, interesting coaching can be provided by analyzing the content of the user's social media posts. This makes it possible to provide coaching means based on the user's social media activities. Some or all of the above-mentioned processing in the coaching unit may be performed, for example, using AI or may be performed without using AI. For example, the coaching unit can input data on the user's social media activities into the generation AI and cause the generation AI to execute the proposal of coaching means.

[0092] The monitoring unit can estimate the user's emotion and adjust the progress monitoring method based on the estimated user's emotion. The monitoring unit uses the generation AI to estimate the user's emotion and adjust the progress monitoring method based on the estimated user's emotion. For example, if the user is nervous, the monitoring unit provides a concise monitoring method. Also, if the user is relaxed, a detailed monitoring method can be provided. Furthermore, if the user is in a hurry, a quick monitoring method can be provided. This makes it possible to provide a monitoring method according to the user's emotion. The emotion estimation is realized 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 monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the monitoring method.

[0093] The monitoring unit can select the optimal monitoring method by referring to the user's past progress data during monitoring. The monitoring unit uses the generation AI to select the optimal monitoring method by referring to the user's past progress data during monitoring. For example, the monitoring unit preferentially suggests monitoring methods that have been successful for the user in the past. It can also suggest to avoid monitoring methods that have failed for the user in the past. Furthermore, it can also suggest a realistic monitoring method based on the user's past progress data. This makes it possible to provide an optimal monitoring method based on the user's past progress data. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's past progress data to the generation AI and cause the generation AI to select the optimal monitoring method.

[0094] The monitoring unit can customize the monitoring means based on the user's current project status at the time of monitoring. The monitoring unit uses the generation AI to customize the monitoring means based on the user's current project status at the time of monitoring. For example, a monitoring method related to the project that the user is currently working on is provided. The frequency of monitoring can also be adjusted according to the progress of the user's project. Furthermore, the level of detail of monitoring can also be adjusted according to the importance of the user's project. This makes it possible to provide a monitoring means according to the user's current project status. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input data on the user's project status to the generation AI and cause the generation AI to customize the monitoring means.

[0095] The monitoring unit can estimate the user's emotions and determine the priority of the progress status based on the estimated user's emotions. The monitoring unit uses the generation AI to estimate the user's emotions and determine the priority of the progress status based on the estimated user's emotions. For example, when the user is highly motivated, the monitoring unit prioritizes monitoring of important progress status. In addition, when the user is stressed, it is also possible to prioritize monitoring of easy progress status. Furthermore, when the user is relaxed, it is also possible to monitor balanced progress status. This makes it possible to provide a priority of the progress status according to the user's emotions. 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, 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 monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the progress status.

[0096] The monitoring unit can select the optimal monitoring method in consideration of the geographical location information of the user at the time of monitoring. The monitoring unit uses the generation AI to select the optimal monitoring method in consideration of the geographical location information of the user at the time of monitoring. For example, when the user is in a specific area, a monitoring method related to the area is provided. In addition, when the user is on a business trip, a monitoring method that can be performed at the business trip destination can be provided. Furthermore, when the user is at home, a monitoring method that can be performed at home can be provided. This makes it possible to provide the optimal monitoring method according to the geographical location of the user. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the optimal monitoring method.

[0097] The monitoring unit can analyze the user's social media activities during monitoring and suggest monitoring means. The monitoring unit uses the generation AI to analyze the user's social media activities during monitoring and suggest monitoring means. For example, a monitoring method related to a theme that the user frequently mentions on social media can be provided. Also, a monitoring method that increases influence based on the number of the user's social media followers can be provided. Furthermore, a monitoring method that analyzes the content of the user's social media posts and attracts interest can be provided. This makes it possible to provide a monitoring means based on the user's social media activities. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input data on the user's social media activities into the generation AI and cause the generation AI to execute a suggestion of a monitoring means.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The reception unit can analyze the user's past goal achievement history and adjust the difficulty of the goal based on the degree of achievement. For example, if the user has shown a high achievement rate in the past, the reception unit can suggest a more challenging goal. Also, if the user has shown a low achievement rate in the past, the reception unit can suggest an easy-to-achieve goal. Furthermore, realistic goal setting can be suggested based on the user's past goal achievement history. This enables optimal goal setting based on the user's past history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past goal achievement history into the generation AI and cause the generation AI to adjust the difficulty of the goal.

[0100] The feedback unit can provide achievable steps for the user's goal in a step-by-step manner. For example, if the user sets a goal of "improving project management skills," the feedback unit provides specific steps such as "clarifying task priorities," "utilizing progress management tools," and "strengthening communication with team members." In addition, the feedback unit can provide criteria for evaluating the degree of achievement for each step. In addition, the feedback unit can suggest resources and tools for the user to achieve each step. This allows the user to have a specific action plan, making it easier to check progress toward achieving the 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 goal into the generation AI and have the generation AI generate steps and set evaluation criteria.

[0101] The coaching unit can customize the coaching method based on the user's learning style. For example, if the user is a visual learner, the coaching unit can provide coaching that makes extensive use of visual aids and graphs. If the user is an auditory learner, the coaching unit can provide coaching in the form of audio guides or podcasts. If the user is a practical learner, the coaching unit can also suggest hands-on workshops and practical tasks. This makes it possible to provide an optimal coaching method according to the user's learning style. Some or all of the above-mentioned processing in the coaching unit may be performed, for example, using AI or may be performed without using AI. For example, the coaching unit can input data on the user's learning style into the generation AI and cause the generation AI to customize the coaching method.

[0102] The monitoring unit can visualize the user's progress in real time and provide it in the form of a dashboard. For example, the progress toward the goals set by the user can be displayed in graphs and charts. The degree of achievement of each goal and the remaining tasks can also be displayed in a list. Furthermore, a customizable widget can be provided so that the user can grasp the progress at a glance. This allows the user to intuitively understand his / her own progress and take necessary actions quickly. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input the user's progress data to the generation AI and cause the generation AI to generate a dashboard.

[0103] The reception unit can estimate the user's emotions and adjust the contents of the goal setting based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can suggest a relaxing goal. If the user is highly motivated, the reception unit can also suggest a challenging goal. Furthermore, if the user is tired, the reception unit can also suggest a short-term, easy-to-achieve goal. This enables optimal goal setting according to the user's emotions. 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, 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the contents of the goal setting.

[0104] The feedback unit can estimate the user's emotion and adjust the timing of the feedback based on the estimated user's emotion. For example, if the user is feeling stressed, the feedback unit provides feedback during a time period when the user can relax. Also, if the user is highly motivated, the feedback unit can provide feedback on the spot. Furthermore, if the user is tired, the feedback unit can provide feedback after the user has rested. This allows feedback to be provided at an optimal timing according to the user's emotion. The emotion estimation is realized 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 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 cause the generation AI to adjust the timing of the feedback.

[0105] The coaching unit can estimate the user's emotion and adjust the coaching content based on the estimated user's emotion. For example, if the user is nervous, the coaching unit provides relaxing content. Also, if the user is relaxed, detailed coaching can be provided. Furthermore, if the user is in a hurry, brief coaching can be provided. This makes it possible to provide optimal coaching content according to the user's emotion. The emotion estimation is realized 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 coaching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the coaching unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the coaching content.

[0106] The monitoring unit can estimate the user's emotions and adjust the progress reporting method based on the estimated user's emotions. For example, if the user is nervous, the monitoring unit can provide a concise reporting method. Also, if the user is relaxed, the monitoring unit can provide a detailed reporting method. Furthermore, if the user is in a hurry, the monitoring unit can provide a quick reporting method. This makes it possible to provide an optimal reporting method according to the user's emotions. 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, 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 monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the reporting method.

[0107] The reception unit can estimate the user's emotion and determine the priority of goal setting based on the estimated user's emotion. For example, when the user is highly motivated, the reception unit can prioritize goals with high difficulty. Also, when the user is stressed, it can also prioritize easy goals. Furthermore, when the user is relaxed, it can also set balanced goals. This allows optimal goal priorities to be set according to the user's emotion. The emotion estimation is realized 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of goals.

[0108] The reception unit can evaluate the user's current skill level and suggest appropriate goals. For example, if the user is a beginner, the reception unit can suggest a basic goal. If the user is an intermediate player, the reception unit can suggest a more advanced goal. If the user is an advanced player, the reception unit can suggest a specialized goal. This enables optimal goal setting according to the user's skill level. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input data on the user's skill level into the generation AI and cause the generation AI to execute the goal suggestion.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The reception section allows the user to input goals related to personal growth and organizational success. Goals input by the user include skill improvement, sales targets, project completion, etc. For example, the reception section accepts the user to input specific goals such as "improve project management skills" or "improve team communication." Step 2: The feedback unit uses the generation AI to provide customized feedback based on the information received by the reception unit. The feedback includes specific advice for the user's goals, such as "how to clarify task priorities" and "how to utilize progress management tools." The generation AI customizes the feedback by taking into account the user's past performance, current situation, type of goal, etc. Step 3: The coaching unit uses the generating AI to provide coaching based on the feedback provided by the feedback unit. Coaching includes creating a specific action plan for the user to achieve the goal. The generating AI monitors the user's progress and adjusts the action plan as necessary. Step 4: The monitoring unit uses the generating AI to monitor the user's progress based on the coaching provided by the coaching unit and adjust the action plan. Monitoring includes evaluating the user's progress and adjusting the action plan as necessary. The generating AI monitors the user's progress in real time and adjusts the action plan taking into account the progress evaluation criteria, monitoring frequency, tools used, etc.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements including the above-mentioned reception unit, feedback unit, coaching unit, and monitoring unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user inputs goals regarding personal growth and organizational success. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides customized feedback using a generation AI. The coaching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides coaching based on the feedback. The monitoring unit is realized, for example, by the control unit 46A of the smart device 14, and monitors the user's progress and adjusts the action plan. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements including the above-mentioned reception unit, feedback unit, coaching unit, and monitoring unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user inputs goals regarding personal growth and organizational success. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides customized feedback using a generation AI. The coaching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides coaching based on the feedback. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214, and monitors the user's progress and adjusts the action plan. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements including the above-mentioned reception unit, feedback unit, coaching unit, and monitoring unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the user inputs goals regarding personal growth and organizational success. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides customized feedback using a generation AI. The coaching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides coaching based on the feedback. The monitoring unit is realized, for example, by the control unit 46A of the headset type terminal 314, and monitors the user's progress and adjusts the action plan. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements including the reception unit, feedback unit, coaching unit, and monitoring unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user inputs goals regarding personal growth and organizational success. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides customized feedback using a generation AI. The coaching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides coaching based on the feedback. The monitoring unit is realized, for example, by the control unit 46A of the robot 414, and monitors the user's progress and adjusts the action plan. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] These emotions are distributed in the 3 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Appendix 1) A reception unit that receives information for setting a goal; a feedback unit that provides customized feedback based on the information received by the reception unit; a coaching unit that provides coaching based on the feedback provided by the feedback unit; A monitoring unit that monitors the progress of the user based on the coaching performed by the coaching unit and adjusts an action plan. A system characterized by: (Appendix 2) The reception unit is Users enter goals related to personal growth and organizational success 2. The system of claim 1, (Appendix 3) The feedback unit is Generate appropriate feedback for user goals 2. The system of claim 1, (Appendix 4) The coaching section includes: Create specific action plans to help users achieve their goals 2. The system of claim 1, (Appendix 5) The monitoring unit is Monitor user progress and adjust action plans as needed 2. The system of claim 1, (Appendix 6) The feedback unit is Providing specific advice such as "how to clarify task priorities" or "how to utilize progress management tools" to achieve the user's goals 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and adjust the timing of goal setting based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past goal setting history and select the optimal goal setting method 2. The system of claim 1, (Appendix 9) The reception unit is Filter goals based on the user's current projects and interests 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize goals based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When setting goals, prioritize relevant goals based on the user's geographic location 2. The system of claim 1, (Appendix 12) The reception unit is When setting goals, analyze users' social media activity and set relevant goals 2. The system of claim 1, (Appendix 13) The feedback unit is Infer user emotions and adjust feedback presentation based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The feedback unit is When providing feedback, adjust the detail of your feedback based on the importance of the goal 2. The system of claim 1, (Appendix 15) The feedback unit is Apply different feedback algorithms depending on the goal category when providing feedback 2. The system of claim 1, (Appendix 16) 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 17) The feedback unit is When providing feedback, prioritize it based on goal timing 2. The system of claim 1, (Appendix 18) The feedback unit is When providing feedback, adjust the order of feedback based on goal relevance 2. The system of claim 1, (Appendix 19) The coaching section includes: Inferring a user's emotions and adjusting a coaching method based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The coaching section includes: When coaching, analyze the user's past behavior history to select the most appropriate coaching method 2. The system of claim 1, (Appendix 21) The coaching section includes: When coaching, customize the coaching experience based on the user's current life situation 2. The system of claim 1, (Appendix 22) The coaching section includes: Inferring a user's emotions and determining a coaching priority based on the estimated user's emotions 2. The system of claim 1, (Appendix 23) The coaching section includes: When coaching, the optimal coaching method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 24) The coaching section includes: During coaching, analyze users' social media activity to suggest coaching measures 2. The system of claim 1, (Appendix 25) The monitoring unit is Inferring a user's emotions and adjusting progress monitoring methods based on the inferred user emotions 2. The system of claim 1, (Appendix 26) The monitoring unit is When monitoring, select the optimal monitoring method by referring to the user's past progress data. 2. The system of claim 1, (Appendix 27) The monitoring unit is When monitoring, customize the monitoring experience based on the user's current project status 2. The system of claim 1, (Appendix 28) The monitoring unit is Infer user emotions and prioritize progress based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The monitoring unit is When monitoring, the optimal monitoring method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The monitoring unit is During monitoring, analyze users' social media activity and suggest monitoring methods 2. The system of claim 1, [Explanation of symbols]

[0183] 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 reception unit that receives information for setting a goal; a feedback unit that provides customized feedback based on the information received by the reception unit; a coaching unit that provides coaching based on the feedback provided by the feedback unit; A monitoring unit that monitors the progress of the user based on the coaching performed by the coaching unit and adjusts an action plan. A system characterized by:

2. The reception unit is Users enter goals related to personal growth and organizational success 2. The system of claim 1 .

3. The feedback unit is Generate appropriate feedback for user goals 2. The system of claim 1 .

4. The coaching section includes: Create specific action plans to help users achieve their goals 2. The system of claim 1 .

5. The monitoring unit is Monitor user progress and adjust action plans as needed 2. The system of claim 1 .

6. The reception unit is Estimate the user's emotions and adjust the timing of goal setting based on the estimated user's emotions.

2. The system of claim 1 .

7. The reception unit is Analyze the user's past goal setting history and select the optimal goal setting method 2. The system of claim 1 .

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