system

Abstract

The system according to the embodiment aims to provide coaching by utilizing generative AI. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, a monitoring unit, and a feedback unit. The receiving unit receives input of a theme or goal from a user. The generating unit generates a coaching plan based on the theme or goal received by the receiving unit. The monitoring unit monitors the user's progress based on the coaching plan generated by the generating unit. The feedback unit provides feedback based on the progress monitored by the monitoring unit.

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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional technology, coaching is generally provided by a person, but there is a problem that coaching can be difficult due to interpersonal relationships, time adjustments, etc.

[0005] The system according to the embodiment aims to provide coaching by utilizing generative AI. [Means for solving the problem]

[0006] A system according to an embodiment includes a receiving unit, a generating unit, a monitoring unit, and a feedback unit. The receiving unit receives input of a theme or goal from a user. The generating unit generates a coaching plan based on the theme or goal received by the receiving unit. The monitoring unit monitors the user's progress based on the coaching plan generated by the generating unit. The feedback unit provides feedback based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide coaching by utilizing generative AI. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A coaching system according to an embodiment of the present invention uses a generation AI to provide coaching. This coaching system utilizes a generation AI to solve problems step by step when coaching by a normal person is difficult. First, a user inputs the topic or goal for which they would like to receive coaching. For example, they input a specific topic such as "I want advice for career advancement" or "I want to learn how to manage stress." This information is then input into the generation AI. The generation AI then generates an appropriate coaching plan based on the input topic or goal. The generation AI analyzes past coaching data and related information to provide the user with optimal advice and support. For example, it suggests specific steps for career advancement and stress management methods. Based on the generated coaching plan, the user takes actions to achieve their goals step by step. The generation AI monitors the user's progress and provides feedback and additional advice as needed. This allows the user to receive coaching at their own pace. This mechanism resolves interpersonal and scheduling issues and allows for efficient coaching. For example, even if a user is busy, they can receive coaching at any time because the generation AI is available 24 hours a day. Furthermore, the generation AI provides advice from an objective perspective, thereby avoiding interpersonal problems. Furthermore, the generative AI can improve and optimize the coaching plan based on user feedback, enabling more effective coaching to be provided to users. For example, if a user provides positive feedback on a particular piece of advice, the generative AI can reinforce that advice and provide it to other users. In this way, the use of generative AI can achieve efficient and effective coaching and support users in achieving their goals. This allows the coaching system to generate a coaching plan based on the user's themes and goals, monitor progress, and provide feedback, enabling efficient and effective coaching.

[0029] A coaching system according to an embodiment includes a reception unit, a generation unit, a monitoring unit, and a feedback unit. The reception unit receives input of a theme or goal from a user. By inputting the theme or goal for which the user wants to receive coaching, a personalized coaching plan can be provided. For example, if a user inputs "I want advice on career advancement," the reception unit transmits the information to the generation unit. The generation unit uses a generation AI to generate a coaching plan based on the theme or goal received by the reception unit. The generation AI analyzes past coaching data and related information to provide optimal advice and support to the user. For example, the generation AI suggests specific steps for career advancement and stress management methods. The generation unit transmits the generated coaching plan to a monitoring unit. The monitoring unit monitors the user's progress based on the coaching plan generated by the generation unit. The monitoring unit monitors the user's progress in real time and provides feedback as needed. For example, the monitoring unit checks the user's progress toward the goal set by the user and provides additional advice if progress is lagging. The feedback unit provides feedback based on the progress monitored by the monitoring unit. The feedback unit improves and optimizes the coaching plan based on user feedback. For example, if a user provides positive feedback on a particular piece of advice, the feedback unit can reinforce that advice and provide it to other users. This allows the coaching system to generate a coaching plan based on the user's themes and goals, monitor progress, and provide feedback, thereby achieving efficient and effective coaching.

[0030] The generation unit can analyze past coaching data or related information to provide advice or support to the user. The generation unit provides optimal advice and support to the user by analyzing past coaching data and related information. For example, the generation unit analyzes past session records and user feedback to provide specific advice to the user. The generation unit can also provide effective support to the user based on industry best practices and the latest research results. For example, the generation unit suggests specific steps for career advancement or stress management methods. In this way, the generation unit can provide optimal advice and support to the user by analyzing past coaching data and related information.

[0031] The monitoring unit can monitor the user's progress in real time and provide feedback as needed. The monitoring unit monitors the user's progress in real time. For example, the monitoring unit checks how much progress the user has made toward a goal set by the user and provides additional advice if progress is lagging behind. The monitoring unit can also visually display the user's progress as a graph or chart. For example, the monitoring unit can display the user's progress in chronological order to grasp the trend of progress. This allows the monitoring unit to monitor the user's progress in real time and provide feedback as needed, thereby achieving effective coaching.

[0032] The feedback unit can improve the coaching plan based on the user's feedback. The feedback unit improves and optimizes the coaching plan based on the user's feedback. For example, if the user provides positive feedback on specific advice, the feedback unit can strengthen the advice and provide it to other users. In addition, if the user provides negative feedback, the feedback unit can also modify the coaching plan based on the feedback. For example, if the user provides feedback that "this advice was not helpful," the feedback unit can improve the advice and provide more effective advice. In this way, the feedback unit can provide more effective coaching by improving and optimizing the coaching plan based on the user's feedback.

[0033] The reception unit allows the user to input a topic or goal for which the user wants to receive coaching. By inputting the topic or goal for which the user wants to receive coaching, the reception unit can provide an individually tailored coaching plan. For example, if the user inputs "I want advice on career advancement," the reception unit transmits that information to the generation unit. The reception unit can also save the topic or goal input by the user and refer to it later. For example, the user can reconfirm topics or goals inputted in the past. In this way, the reception unit can provide an individually tailored coaching plan by inputting the topic or goal for which the user wants to receive coaching.

[0034] The generation unit can generate a coaching plan using the generation AI. The generation unit generates the coaching plan using the generation AI. The generation AI analyzes past coaching data and related information to provide optimal advice and support to the user. For example, the generation AI may suggest specific steps for career advancement or stress management methods. The generation AI can also improve and optimize the coaching plan based on user feedback. For example, if a user provides positive feedback on specific advice, the generation AI can reinforce that advice and provide it to other users. This allows the generation unit to generate efficient and effective coaching plans using the generation AI.

[0035] The reception unit can analyze the user's past input history of themes or goals and suggest an input method. The reception unit analyzes the user's past input history of themes and goals and suggest the optimal input method. For example, the reception unit automatically displays themes and goals that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes and goals to be used in a specific time period from the user's past input history. In this way, the reception unit can suggest the optimal input method to the user by analyzing the user's past input history.

[0036] When a theme or goal is input, the reception unit can filter the input content based on the user's current situation or areas of interest. When a theme or goal is input, the reception unit filters the input content based on the user's current situation or areas of interest. For example, if the user is dissatisfied with their current work environment, the reception unit can preferentially display themes related to career advancement. Also, if the user is interested in health, the reception unit can suggest goals related to health management. Furthermore, if the user wants to learn a new skill, the reception unit can display themes related to skill improvement. In this way, the reception unit can suggest more appropriate themes and goals by filtering the input content based on the user's current situation and areas of interest.

[0037] When inputting a theme or goal, the reception unit can prioritize inputting highly relevant themes and goals by taking into account the user's geographical location information. When inputting a theme or goal, the reception unit prioritizes inputting highly relevant themes and goals by taking into account the user's geographical location information. For example, if the user lives in an urban area, the reception unit can prioritize displaying themes related to urban life. Also, if the user lives in a rural area, the reception unit can prioritize displaying goals related to agriculture or nature. Furthermore, if the user is traveling to a specific area, the reception unit can prioritize displaying themes related to that area. In this way, the reception unit can prioritize inputting highly relevant themes and goals by taking into account the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity when inputting a theme or goal, and input related themes or goals. The reception unit analyzes the user's social media activity when inputting a theme or goal, and input related themes or goals. For example, the reception unit can suggest related themes based on content that the user frequently posts on social media. The reception unit can also suggest goals that the user may be interested in based on content of accounts the user follows. Furthermore, the reception unit can suggest related themes based on topics in online communities in which the user participates. In this way, the reception unit can input related themes and goals by analyzing the user's social media activity.

[0039] When generating a coaching plan, the generation unit can adjust the level of detail of the plan based on the importance of the theme or goal. When generating a coaching plan, the generation unit adjusts the level of detail of the plan based on the importance of the theme or goal. For example, the generation unit generates a detailed step-by-step plan for a highly important theme. The generation unit can also generate a plan that provides concise advice for a low-importance theme. Furthermore, the generation unit can generate a plan with a moderate level of detail for a theme of medium importance. In this way, the generation unit can provide a more appropriate coaching plan by adjusting the level of detail of the plan based on the importance of the theme or goal.

[0040] The generation unit can apply different generation algorithms depending on the category of the theme or goal when generating a coaching plan. The generation unit applies different generation algorithms depending on the category of the theme or goal when generating a coaching plan. For example, the generation unit applies a generation algorithm based on occupation-related data to a theme related to career advancement. The generation unit can also apply a generation algorithm based on medical data to a theme related to health management. Furthermore, the generation unit can apply a generation algorithm based on educational data to a theme related to skill improvement. In this way, the generation unit can provide a more effective coaching plan by applying different generation algorithms depending on the category of the theme or goal.

[0041] When generating coaching plans, the generation unit can determine the priority of the plans based on the time of submission of the themes and goals. When generating coaching plans, the generation unit determines the priority of the plans based on the time of submission of themes and goals. For example, the generation unit generates coaching plans with the highest priority for themes with high urgency. The generation unit can also generate coaching plans with priority for goals with upcoming submission deadlines. Furthermore, the generation unit can generate coaching plans with a later deadline for goals with distant submission deadlines. In this way, the generation unit can provide more effective coaching by determining the priority of plans based on the time of submission of themes and goals.

[0042] When generating a coaching plan, the generation unit can adjust the order of the plan based on the relevance of the themes and goals. When generating a coaching plan, the generation unit adjusts the order of the plan based on the relevance of the themes and goals. For example, the generation unit prioritizes incorporating highly relevant themes into the coaching plan. The generation unit can also postpone incorporating less relevant themes into the coaching plan. Furthermore, the generation unit can group related themes to efficiently generate a coaching plan. In this way, the generation unit can provide more efficient coaching by adjusting the order of the plan based on the relevance of the themes and goals.

[0043] The monitoring unit can improve the accuracy of monitoring when monitoring the progress status by referring to the user's past progress data. The monitoring unit can improve the accuracy of monitoring when monitoring the progress status by referring to the user's past progress data. For example, the monitoring unit predicts the current progress status based on the user's past progress data. The monitoring unit can also identify problems and propose improvements from the user's past progress data. Furthermore, the monitoring unit can analyze the user's past progress data and propose an optimal monitoring method. In this way, the monitoring unit can improve the accuracy of monitoring by referring to the past progress data.

[0044] The monitoring unit can perform monitoring taking into account the user's attribute information when monitoring the progress status. The monitoring unit performs monitoring taking into account the user's attribute information when monitoring the progress status. For example, the monitoring unit selects an appropriate monitoring method based on the user's age. The monitoring unit can also monitor related progress status based on the user's occupation. Furthermore, the monitoring unit can suggest an optimal monitoring method based on the user's lifestyle. This allows the monitoring unit to perform more appropriate monitoring by taking into account the user's attribute information.

[0045] The monitoring unit can perform monitoring taking into account the geographical distribution of the user when monitoring the progress. The monitoring unit performs monitoring taking into account the geographical distribution of the user when monitoring the progress. For example, if the user lives in an urban area, the monitoring unit monitors progress related to urban life. Also, if the user lives in a rural area, the monitoring unit can monitor progress related to agriculture or nature. Furthermore, if the user is traveling to a specific area, the monitoring unit can monitor progress related to that area. This allows the monitoring unit to perform more appropriate monitoring by taking into account the geographical distribution of the user.

[0046] The monitoring department can improve the accuracy of monitoring by referring to related literature when monitoring the progress status. The monitoring department can improve the accuracy of monitoring by referring to related literature when monitoring the progress status. For example, the monitoring department sets evaluation criteria for the progress status based on related literature. The monitoring department can also propose measures to improve the progress status based on information obtained from related literature. Furthermore, the monitoring department can optimize the progress monitoring method by referring to related literature. In this way, the monitoring department can improve the accuracy of monitoring by referring to related literature.

[0047] The feedback unit can select a feedback method by referring to the user's past feedback history when providing feedback. The feedback unit selects a feedback method by referring to the user's past feedback history when providing feedback. For example, the feedback unit prioritizes feedback methods to which the user has previously responded positively. The feedback unit can also avoid feedback methods to which the user has previously responded negatively. Furthermore, the feedback unit can analyze the user's past feedback history and suggest an optimal feedback method. In this way, the feedback unit can select an optimal feedback method by referring to the past feedback history.

[0048] The feedback unit can customize the content of the feedback based on the user's current situation when providing feedback. The feedback unit customizes the content of the feedback based on the user's current situation when providing feedback. For example, if the user is dissatisfied with their current work environment, the feedback unit can provide feedback regarding workplace improvements. Also, if the user is interested in their health, the feedback unit can provide feedback regarding health management. Furthermore, if the user wants to learn a new skill, the feedback unit can provide feedback regarding skill improvement. This allows the feedback unit to customize the content of the feedback based on the user's current situation, thereby enabling more appropriate feedback.

[0049] The feedback unit may select a feedback method by taking into account the user's geographical location information when providing feedback. The feedback unit may select a feedback method by taking into account the user's geographical location information when providing feedback. For example, if the user lives in an urban area, the feedback unit may provide feedback related to urban life. If the user lives in a rural area, the feedback unit may provide feedback related to agriculture or nature. Furthermore, if the user is traveling to a specific area, the feedback unit may provide feedback related to the area. In this way, the feedback unit may select an optimal feedback method by taking into account the user's geographical location information.

[0050] The feedback unit may analyze the user's social media activity to suggest feedback content when providing feedback. The feedback unit may analyze the user's social media activity to suggest feedback content when providing feedback. For example, the feedback unit may provide relevant feedback based on content frequently posted by the user on social media. The feedback unit may also provide feedback that is likely to be of interest to the user based on content of accounts the user follows. Furthermore, the feedback unit may provide relevant feedback based on topics in online communities in which the user participates. In this way, the feedback unit may provide relevant feedback by analyzing the user's social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can analyze the user's past coaching history and suggest new themes and goals based on the themes and goals the user has previously worked on. For example, if the user has previously received coaching on career advancement, the reception unit can suggest improving leadership skills as the next step. Also, if the user has received coaching on stress management, the reception unit can suggest a theme related to maintaining mental health. Furthermore, if the user has acquired a specific skill, the reception unit can suggest a new goal that makes use of that skill. In this way, the reception unit can suggest more appropriate themes and goals by utilizing the user's past coaching history.

[0053] The generation unit can customize the coaching plan based on the user's current living situation and environment. For example, if the user is working remotely, the generation unit can suggest time management and communication methods suitable for remote work. Also, if the user is raising children, the generation unit can provide advice on balancing childcare and work. Furthermore, if the user moves to a new city, the generation unit can suggest ways to network in that city and how to utilize local resources. In this way, the generation unit can provide a coaching plan tailored to the user's living situation and environment.

[0054] The monitoring unit can take the user's health data into account when monitoring the user's progress. For example, it can monitor the user's stress level and sleep patterns based on data obtained from the user's fitness tracker or smartwatch. It can also analyze the user's diet and exercise records to provide feedback based on the user's health status. Furthermore, if the user has set health goals, it can monitor the user's progress in real time and provide advice as needed. In this way, the monitoring unit can utilize the user's health data to achieve more comprehensive progress monitoring.

[0055] The feedback unit can suggest collaborations with other users based on the user's feedback. For example, if the user wants to learn a particular skill, the feedback unit can suggest pairing with other users who have the same goal. Also, if the user is working on a project, the feedback unit can suggest collaborations with other users who have related skills. Furthermore, if the user needs support to stay motivated, the feedback unit can suggest mentoring programs. In this way, the feedback unit can utilize the user's feedback to encourage collaborations with other users.

[0056] The reception unit can analyze the user's past input history of themes and goals and suggest an input method. For example, the reception unit can automatically display themes and goals that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes and goals to be used in a specific time period based on the user's past input history. In this way, the reception unit can suggest the optimal input method for the user by analyzing the user's past input history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives input of a theme or goal from the user. By inputting the theme or goal for which the user wants to receive coaching, an individually tailored coaching plan can be provided. For example, if the user inputs "I would like advice on career advancement," the reception unit sends that information to the generation unit. Step 2: The generation unit uses the generation AI to generate a coaching plan based on the theme or goal received by the reception unit. The generation AI analyzes past coaching data and related information to provide optimal advice and support to the user. For example, the generation AI may suggest specific steps for career advancement or stress management methods. The generation unit sends the generated coaching plan to the monitoring unit. Step 3: The monitoring unit monitors the user's progress based on the coaching plan generated by the generation unit. The monitoring unit monitors the user's progress in real time and provides feedback as needed. For example, the monitoring unit checks how much progress the user has made toward the goals they set, and provides additional advice if progress is lagging behind. Step 4: The feedback unit provides feedback based on the progress monitored by the monitoring unit. The feedback unit improves and optimizes the coaching plan based on the user's feedback. For example, if a user provides positive feedback on a particular piece of advice, the feedback unit can reinforce that advice and provide it to other users.

[0059] (Example 2) A coaching system according to an embodiment of the present invention uses a generation AI to provide coaching. This coaching system utilizes a generation AI to solve problems step by step when coaching by a normal person is difficult. First, a user inputs the topic or goal for which they would like to receive coaching. For example, they input a specific topic such as "I want advice for career advancement" or "I want to learn how to manage stress." This information is then input into the generation AI. The generation AI then generates an appropriate coaching plan based on the input topic or goal. The generation AI analyzes past coaching data and related information to provide the user with optimal advice and support. For example, it suggests specific steps for career advancement and stress management methods. Based on the generated coaching plan, the user takes actions to achieve their goals step by step. The generation AI monitors the user's progress and provides feedback and additional advice as needed. This allows the user to receive coaching at their own pace. This mechanism resolves interpersonal and scheduling issues and allows for efficient coaching. For example, even if a user is busy, they can receive coaching at any time because the generation AI is available 24 hours a day. Furthermore, the generation AI provides advice from an objective perspective, thereby avoiding interpersonal problems. Furthermore, the generative AI can improve and optimize the coaching plan based on user feedback, enabling more effective coaching to be provided to users. For example, if a user provides positive feedback on a particular piece of advice, the generative AI can reinforce that advice and provide it to other users. In this way, the use of generative AI can achieve efficient and effective coaching and support users in achieving their goals. This allows the coaching system to generate a coaching plan based on the user's themes and goals, monitor progress, and provide feedback, enabling efficient and effective coaching.

[0060] A coaching system according to an embodiment includes a reception unit, a generation unit, a monitoring unit, and a feedback unit. The reception unit receives input of a theme or goal from a user. By inputting the theme or goal for which the user wants to receive coaching, a personalized coaching plan can be provided. For example, if a user inputs "I want advice on career advancement," the reception unit transmits the information to the generation unit. The generation unit uses a generation AI to generate a coaching plan based on the theme or goal received by the reception unit. The generation AI analyzes past coaching data and related information to provide optimal advice and support to the user. For example, the generation AI suggests specific steps for career advancement and stress management methods. The generation unit transmits the generated coaching plan to a monitoring unit. The monitoring unit monitors the user's progress based on the coaching plan generated by the generation unit. The monitoring unit monitors the user's progress in real time and provides feedback as needed. For example, the monitoring unit checks the user's progress toward the goal set by the user and provides additional advice if progress is lagging. The feedback unit provides feedback based on the progress monitored by the monitoring unit. The feedback unit improves and optimizes the coaching plan based on user feedback. For example, if a user provides positive feedback on a particular piece of advice, the feedback unit can reinforce that advice and provide it to other users. This allows the coaching system to generate a coaching plan based on the user's themes and goals, monitor progress, and provide feedback, thereby achieving efficient and effective coaching.

[0061] The generation unit can analyze past coaching data or related information to provide advice or support to the user. The generation unit provides optimal advice and support to the user by analyzing past coaching data and related information. For example, the generation unit analyzes past session records and user feedback to provide specific advice to the user. The generation unit can also provide effective support to the user based on industry best practices and the latest research results. For example, the generation unit suggests specific steps for career advancement or stress management methods. In this way, the generation unit can provide optimal advice and support to the user by analyzing past coaching data and related information.

[0062] The monitoring unit can monitor the user's progress in real time and provide feedback as needed. The monitoring unit monitors the user's progress in real time. For example, the monitoring unit checks how much progress the user has made toward a goal set by the user and provides additional advice if progress is lagging behind. The monitoring unit can also visually display the user's progress as a graph or chart. For example, the monitoring unit can display the user's progress in chronological order to grasp the trend of progress. This allows the monitoring unit to monitor the user's progress in real time and provide feedback as needed, thereby achieving effective coaching.

[0063] The feedback unit can improve the coaching plan based on the user's feedback. The feedback unit improves and optimizes the coaching plan based on the user's feedback. For example, if the user provides positive feedback on specific advice, the feedback unit can strengthen the advice and provide it to other users. In addition, if the user provides negative feedback, the feedback unit can also modify the coaching plan based on the feedback. For example, if the user provides feedback that "this advice was not helpful," the feedback unit can improve the advice and provide more effective advice. In this way, the feedback unit can provide more effective coaching by improving and optimizing the coaching plan based on the user's feedback.

[0064] The reception unit allows the user to input a topic or goal for which the user wants to receive coaching. By inputting the topic or goal for which the user wants to receive coaching, the reception unit can provide an individually tailored coaching plan. For example, if the user inputs "I want advice on career advancement," the reception unit transmits that information to the generation unit. The reception unit can also save the topic or goal input by the user and refer to it later. For example, the user can reconfirm topics or goals inputted in the past. In this way, the reception unit can provide an individually tailored coaching plan by inputting the topic or goal for which the user wants to receive coaching.

[0065] The generation unit can generate a coaching plan using the generation AI. The generation unit generates the coaching plan using the generation AI. The generation AI analyzes past coaching data and related information to provide optimal advice and support to the user. For example, the generation AI may suggest specific steps for career advancement or stress management methods. The generation AI can also improve and optimize the coaching plan based on user feedback. For example, if a user provides positive feedback on specific advice, the generation AI can reinforce that advice and provide it to other users. This allows the generation unit to generate efficient and effective coaching plans using the generation AI.

[0066] The reception unit can estimate the user's emotions and adjust the input method for themes and goals based on the estimated user emotions. The reception unit can estimate the user's emotions and adjust the input method for themes and goals based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input themes and goals. This allows the reception unit to adjust the input method according to the user's emotions, thereby enabling the user to input more appropriate themes and goals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The reception unit can analyze the user's past input history of themes or goals and suggest an input method. The reception unit analyzes the user's past input history of themes and goals and suggest the optimal input method. For example, the reception unit automatically displays themes and goals that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes and goals to be used in a specific time period from the user's past input history. In this way, the reception unit can suggest the optimal input method to the user by analyzing the user's past input history.

[0068] When a theme or goal is input, the reception unit can filter the input content based on the user's current situation or areas of interest. When a theme or goal is input, the reception unit filters the input content based on the user's current situation or areas of interest. For example, if the user is dissatisfied with their current work environment, the reception unit can preferentially display themes related to career advancement. Also, if the user is interested in health, the reception unit can suggest goals related to health management. Furthermore, if the user wants to learn a new skill, the reception unit can display themes related to skill improvement. In this way, the reception unit can suggest more appropriate themes and goals by filtering the input content based on the user's current situation and areas of interest.

[0069] The reception unit can estimate the user's emotions and determine the priority of the themes and goals to be input based on the estimated user emotions. The reception unit can estimate the user's emotions and determine the priority of the themes and goals to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize displaying themes related to stress management. Also, if the user is feeling motivated, the reception unit can prioritize displaying goals related to career advancement. Furthermore, if the user is relaxed, the reception unit can prioritize displaying themes related to hobbies and relaxation. In this way, the reception unit can provide more effective coaching by prioritizing themes and goals based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] When inputting a theme or goal, the reception unit can prioritize inputting highly relevant themes and goals by taking into account the user's geographical location information. When inputting a theme or goal, the reception unit prioritizes inputting highly relevant themes and goals by taking into account the user's geographical location information. For example, if the user lives in an urban area, the reception unit can prioritize displaying themes related to urban life. Also, if the user lives in a rural area, the reception unit can prioritize displaying goals related to agriculture or nature. Furthermore, if the user is traveling to a specific area, the reception unit can prioritize displaying themes related to that area. In this way, the reception unit can prioritize inputting highly relevant themes and goals by taking into account the user's geographical location information.

[0071] The reception unit can analyze the user's social media activity when inputting a theme or goal, and input related themes or goals. The reception unit analyzes the user's social media activity when inputting a theme or goal, and input related themes or goals. For example, the reception unit can suggest related themes based on content that the user frequently posts on social media. The reception unit can also suggest goals that the user may be interested in based on content of accounts the user follows. Furthermore, the reception unit can suggest related themes based on topics in online communities in which the user participates. In this way, the reception unit can input related themes and goals by analyzing the user's social media activity.

[0072] The generation unit can estimate the user's emotions and adjust the presentation of the coaching plan based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the presentation of the coaching plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a coaching plan that progresses at a leisurely pace. If the user is in a hurry, the generation unit can generate a coaching plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a coaching plan that adds visually stimulating effects. This allows the generation unit to provide more effective coaching by adjusting the presentation of the coaching plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] When generating a coaching plan, the generation unit can adjust the level of detail of the plan based on the importance of the theme or goal. When generating a coaching plan, the generation unit adjusts the level of detail of the plan based on the importance of the theme or goal. For example, the generation unit generates a detailed step-by-step plan for a highly important theme. The generation unit can also generate a plan that provides concise advice for a low-importance theme. Furthermore, the generation unit can generate a plan with a moderate level of detail for a theme of medium importance. In this way, the generation unit can provide a more appropriate coaching plan by adjusting the level of detail of the plan based on the importance of the theme or goal.

[0074] The generation unit can apply different generation algorithms depending on the category of the theme or goal when generating a coaching plan. The generation unit applies different generation algorithms depending on the category of the theme or goal when generating a coaching plan. For example, the generation unit applies a generation algorithm based on occupation-related data to a theme related to career advancement. The generation unit can also apply a generation algorithm based on medical data to a theme related to health management. Furthermore, the generation unit can apply a generation algorithm based on educational data to a theme related to skill improvement. In this way, the generation unit can provide a more effective coaching plan by applying different generation algorithms depending on the category of the theme or goal.

[0075] The generation unit can estimate the user's emotions and adjust the length of the coaching plan based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the length of the coaching plan based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short and to-the-point coaching plan. Alternatively, if the user is relaxed, the generation unit can generate a longer coaching plan with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a coaching plan with visually stimulating effects. In this way, the generation unit can provide a more appropriate coaching plan by adjusting the length of the coaching plan based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] When generating coaching plans, the generation unit can determine the priority of the plans based on the time of submission of the themes and goals. When generating coaching plans, the generation unit determines the priority of the plans based on the time of submission of themes and goals. For example, the generation unit generates coaching plans with the highest priority for themes with high urgency. The generation unit can also generate coaching plans with priority for goals with upcoming submission deadlines. Furthermore, the generation unit can generate coaching plans with a later deadline for goals with distant submission deadlines. In this way, the generation unit can provide more effective coaching by determining the priority of plans based on the time of submission of themes and goals.

[0077] When generating a coaching plan, the generation unit can adjust the order of the plan based on the relevance of the themes and goals. When generating a coaching plan, the generation unit adjusts the order of the plan based on the relevance of the themes and goals. For example, the generation unit prioritizes incorporating highly relevant themes into the coaching plan. The generation unit can also postpone incorporating less relevant themes into the coaching plan. Furthermore, the generation unit can group related themes to efficiently generate a coaching plan. In this way, the generation unit can provide more efficient coaching by adjusting the order of the plan based on the relevance of the themes and goals.

[0078] The monitoring unit can estimate the user's emotions and adjust the progress monitoring method based on the estimated user emotions. The monitoring unit can estimate the user's emotions and adjust the progress monitoring method based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can display a concise progress status. Alternatively, if the user is relaxed, the monitoring unit can display a detailed progress status. Furthermore, if the user is in a hurry, the monitoring unit can display a progress status that focuses on the main points. This allows the monitoring unit to adjust the progress monitoring method based on the user's emotions, enabling more appropriate monitoring. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The monitoring unit can improve the accuracy of monitoring when monitoring the progress status by referring to the user's past progress data. The monitoring unit can improve the accuracy of monitoring when monitoring the progress status by referring to the user's past progress data. For example, the monitoring unit predicts the current progress status based on the user's past progress data. The monitoring unit can also identify problems and propose improvements from the user's past progress data. Furthermore, the monitoring unit can analyze the user's past progress data and propose an optimal monitoring method. In this way, the monitoring unit can improve the accuracy of monitoring by referring to the past progress data.

[0080] The monitoring unit can perform monitoring taking into account the user's attribute information when monitoring the progress status. The monitoring unit performs monitoring taking into account the user's attribute information when monitoring the progress status. For example, the monitoring unit selects an appropriate monitoring method based on the user's age. The monitoring unit can also monitor related progress status based on the user's occupation. Furthermore, the monitoring unit can suggest an optimal monitoring method based on the user's lifestyle. This allows the monitoring unit to perform more appropriate monitoring by taking into account the user's attribute information.

[0081] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can also provide a display method that focuses on the main points. This allows the monitoring unit to adjust the display method of the monitoring results based on the user's emotions, thereby enabling a more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The monitoring unit can perform monitoring taking into account the geographical distribution of the user when monitoring the progress. The monitoring unit performs monitoring taking into account the geographical distribution of the user when monitoring the progress. For example, if the user lives in an urban area, the monitoring unit monitors progress related to urban life. Also, if the user lives in a rural area, the monitoring unit can monitor progress related to agriculture or nature. Furthermore, if the user is traveling to a specific area, the monitoring unit can monitor progress related to that area. This allows the monitoring unit to perform more appropriate monitoring by taking into account the geographical distribution of the user.

[0083] The monitoring department can improve the accuracy of monitoring by referring to related literature when monitoring the progress status. The monitoring department can improve the accuracy of monitoring by referring to related literature when monitoring the progress status. For example, the monitoring department sets evaluation criteria for the progress status based on related literature. The monitoring department can also propose measures to improve the progress status based on information obtained from related literature. Furthermore, the monitoring department can optimize the progress monitoring method by referring to related literature. In this way, the monitoring department can improve the accuracy of monitoring by referring to related literature.

[0084] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated user emotions. The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated user emotions. For example, if the user is feeling stressed, the feedback unit prioritizes positive feedback. Also, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can provide concise and to-the-point feedback. This allows the feedback unit to adjust the feedback method based on the user's emotions, thereby enabling more appropriate feedback. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The feedback unit can select a feedback method by referring to the user's past feedback history when providing feedback. The feedback unit selects a feedback method by referring to the user's past feedback history when providing feedback. For example, the feedback unit prioritizes feedback methods to which the user has previously responded positively. The feedback unit can also avoid feedback methods to which the user has previously responded negatively. Furthermore, the feedback unit can analyze the user's past feedback history and suggest an optimal feedback method. In this way, the feedback unit can select an optimal feedback method by referring to the past feedback history.

[0086] The feedback unit can customize the content of the feedback based on the user's current situation when providing feedback. The feedback unit customizes the content of the feedback based on the user's current situation when providing feedback. For example, if the user is dissatisfied with their current work environment, the feedback unit can provide feedback regarding workplace improvements. Also, if the user is interested in their health, the feedback unit can provide feedback regarding health management. Furthermore, if the user wants to learn a new skill, the feedback unit can provide feedback regarding skill improvement. This allows the feedback unit to customize the content of the feedback based on the user's current situation, thereby enabling more appropriate feedback.

[0087] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. For example, if the user is feeling stressed, the feedback unit can prioritize feedback related to stress management. Also, if the user is feeling motivated, the feedback unit can prioritize feedback related to career advancement. Furthermore, if the user is relaxed, the feedback unit can prioritize feedback related to hobbies and relaxation. This allows the feedback unit to prioritize feedback based on the user's emotions, thereby enabling more effective feedback. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The feedback unit may select a feedback method by taking into account the user's geographical location information when providing feedback. The feedback unit may select a feedback method by taking into account the user's geographical location information when providing feedback. For example, if the user lives in an urban area, the feedback unit may provide feedback related to urban life. If the user lives in a rural area, the feedback unit may provide feedback related to agriculture or nature. Furthermore, if the user is traveling to a specific area, the feedback unit may provide feedback related to the area. In this way, the feedback unit may select an optimal feedback method by taking into account the user's geographical location information.

[0089] The feedback unit may analyze the user's social media activity to suggest feedback content when providing feedback. The feedback unit may analyze the user's social media activity to suggest feedback content when providing feedback. For example, the feedback unit may provide relevant feedback based on content frequently posted by the user on social media. The feedback unit may also provide feedback that is likely to be of interest to the user based on content of accounts the user follows. Furthermore, the feedback unit may provide relevant feedback based on topics in online communities in which the user participates. In this way, the feedback unit may provide relevant feedback by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, monitoring unit, and feedback 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 reception device 38 of the smart device 14, and the user inputs the theme or goal for which they would like to receive coaching. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a coaching plan using a generation AI. The monitoring unit is realized, for example, by the control unit 46A of the smart device 14, and monitors the user's progress. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and improves and optimizes the coaching plan based on the user's feedback. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, monitoring unit, and feedback 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 microphone 238 of the smart glasses 214, and the user vocally inputs the topic or goal for which they want to receive coaching. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a coaching plan using a generation AI. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214, and monitors the user's progress. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and improves and optimizes the coaching plan based on the user's feedback. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, monitoring unit, and feedback 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 microphone 238 of the headset-type terminal 314, and the user vocally inputs the topic or goal for which they want to receive coaching. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a coaching plan using a generation AI. The monitoring unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and monitors the user's progress. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and improves and optimizes the coaching plan based on the user's feedback. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, monitoring unit, and feedback unit 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 microphone 238 of the robot 414, and the user vocally inputs the topic or goal for which they want to receive coaching. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a coaching plan using a generation AI. The monitoring unit is realized, for example, by the control unit 46A of the robot 414, and monitors the user's progress. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and improves and optimizes the coaching plan based on the user's feedback.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can analyze the user's past coaching history and suggest new themes and goals based on the themes and goals the user has previously worked on. For example, if the user has previously received coaching on career advancement, the reception unit can suggest improving leadership skills as the next step. Also, if the user has received coaching on stress management, the reception unit can suggest a theme related to maintaining mental health. Furthermore, if the user has acquired a specific skill, the reception unit can suggest a new goal that makes use of that skill. In this way, the reception unit can suggest more appropriate themes and goals by utilizing the user's past coaching history.

[0092] The generation unit can customize the coaching plan based on the user's current living situation and environment. For example, if the user is working remotely, the generation unit can suggest time management and communication methods suitable for remote work. Also, if the user is raising children, the generation unit can provide advice on balancing childcare and work. Furthermore, if the user moves to a new city, the generation unit can suggest ways to network in that city and how to utilize local resources. In this way, the generation unit can provide a coaching plan tailored to the user's living situation and environment.

[0093] The monitoring unit can take the user's health data into account when monitoring the user's progress. For example, it can monitor the user's stress level and sleep patterns based on data obtained from the user's fitness tracker or smartwatch. It can also analyze the user's diet and exercise records to provide feedback based on the user's health status. Furthermore, if the user has set health goals, it can monitor the user's progress in real time and provide advice as needed. In this way, the monitoring unit can utilize the user's health data to achieve more comprehensive progress monitoring.

[0094] The feedback unit can suggest collaborations with other users based on the user's feedback. For example, if the user wants to learn a particular skill, the feedback unit can suggest pairing with other users who have the same goal. Also, if the user is working on a project, the feedback unit can suggest collaborations with other users who have related skills. Furthermore, if the user needs support to stay motivated, the feedback unit can suggest mentoring programs. In this way, the feedback unit can utilize the user's feedback to encourage collaborations with other users.

[0095] The reception unit can estimate the user's emotions and adjust the input method for themes and goals based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly input themes and goals. In this way, the reception unit can adjust the input method according to the user's emotions, enabling the user to input more appropriate themes and goals.

[0096] The generation unit can estimate the user's emotions and adjust the way the coaching plan is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a coaching plan that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a coaching plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can also generate a coaching plan that adds visually stimulating effects. In this way, the generation unit can provide more effective coaching by adjusting the way the coaching plan is presented based on the user's emotions.

[0097] The monitoring unit can estimate the user's emotions and adjust the progress monitoring method based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring unit can display a concise progress status. If the user is relaxed, the monitoring unit can also display a detailed progress status. Furthermore, if the user is in a hurry, the monitoring unit can also display a progress status that focuses on the main points. This allows the monitoring unit to adjust the progress monitoring method based on the user's emotions, thereby enabling more appropriate monitoring.

[0098] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can prioritize positive feedback. If the user is relaxed, the feedback unit can also provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can also provide brief, to-the-point feedback. This allows the feedback unit to adjust the feedback method based on the user's emotions, thereby enabling more appropriate feedback.

[0099] The generation unit can estimate the user's emotions and adjust the length of the coaching plan based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point coaching plan. If the user is relaxed, the generation unit can generate a longer coaching plan that includes detailed explanations. Furthermore, if the user is excited, the generation unit can generate a coaching plan that includes visually stimulating effects. In this way, the generation unit can provide a more appropriate coaching plan by adjusting the length of the coaching plan based on the user's emotions.

[0100] The reception unit can analyze the user's past input history of themes and goals and suggest an input method. For example, the reception unit can automatically display themes and goals that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest themes and goals to be used in a specific time period based on the user's past input history. In this way, the reception unit can suggest the optimal input method for the user by analyzing the user's past input history.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives input of a theme or goal from the user. By inputting the theme or goal for which the user wants to receive coaching, an individually tailored coaching plan can be provided. For example, if the user inputs "I would like advice on career advancement," the reception unit sends that information to the generation unit. Step 2: The generation unit uses the generation AI to generate a coaching plan based on the theme or goal received by the reception unit. The generation AI analyzes past coaching data and related information to provide optimal advice and support to the user. For example, the generation AI may suggest specific steps for career advancement or stress management methods. The generation unit sends the generated coaching plan to the monitoring unit. Step 3: The monitoring unit monitors the user's progress based on the coaching plan generated by the generation unit. The monitoring unit monitors the user's progress in real time and provides feedback as needed. For example, the monitoring unit checks how much progress the user has made toward the goals they set, and provides additional advice if progress is lagging behind. Step 4: The feedback unit provides feedback based on the progress monitored by the monitoring unit. The feedback unit improves and optimizes the coaching plan based on the user's feedback. For example, if a user provides positive feedback on a particular piece of advice, the feedback unit can reinforce that advice and provide it to other users.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0104] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and 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.

[0110] 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.

[0111] 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 audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] 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 carried out in a secure state.

[0114] Fig. 4 shows an example of the 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.

[0115] 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.

[0116] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0120] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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, RAM 30, and 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.

[0126] 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.

[0127] 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 audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] 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 carried out in a secure state.

[0130] 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.

[0131] 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.

[0132] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0134] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0136] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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, RAM 30, and 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.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] 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 audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] 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 carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0147] 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.

[0148] 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.

[0149] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0151] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] 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 voice indicating a user input regarding 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.

[0153] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] 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.

[0164] 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 USB (Universal Serial Bus) 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.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] 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.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0169] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 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 input of a theme or goal from a user; a generation unit that generates a coaching plan based on the theme or goal received by the reception unit; a monitoring unit that monitors the progress of the user based on the coaching plan generated by the generation unit; a feedback unit that provides feedback based on the progress monitored by the monitoring unit; Equipped with A system characterized by:

2. The generation unit Analyze past coaching data or related information to provide advice or support to users The system of claim 1 .

3. The monitoring unit Monitor user progress in real time and provide feedback as needed The system of claim 1 .

4. The feedback unit Improve your coaching plan based on user feedback The system of claim 1 .

5. The reception unit The user inputs the topic or goal for which they would like to receive coaching. The system of claim 1 .

6. The generation unit Generate coaching plans using generative AI The system of claim 1 .

7. The reception unit Inferring user emotions and adjusting the method of inputting a theme or goal based on the estimated user emotions The system of claim 1 .

8. The reception unit Analyze the user's past input history of themes or goals and suggest input methods The system of claim 1 .

Images